2021 |
| 41. | Ebad Banissi; Anna Ursyn; Mark W. McK. Bannatyne; João Moura Pires; Nuno Datia; Mao Lin Huang Weidong Huang; Quang Vinh Nguyen; Kawa Nazemi; Boris Kovalerchuk; Minoru Nakayama; John Counsell; Andrew Agapiou; Farzad Khosrow-shahi; Hing-Wah Chau; Mengbi Li; Richard Laing; Fatma Bouali; Gilles Venturini; Marco Temperini; Muhammad Sarfraz (Ed.) Proceedings of 2021 25th International Conference Information Visualisation (IV) Proceeding IEEE, New York, USA, 2021, ISBN: 978-1-6654-3827-8. @proceedings{Banissi2021,
title = {Proceedings of 2021 25th International Conference Information Visualisation (IV)},
editor = {Ebad Banissi and Anna Ursyn and Mark W. McK. Bannatyne and João Moura Pires and Nuno Datia and Mao Lin Huang Weidong Huang and Quang Vinh Nguyen and Kawa Nazemi and Boris Kovalerchuk and Minoru Nakayama and John Counsell and Andrew Agapiou and Farzad Khosrow-shahi and Hing-Wah Chau and Mengbi Li and Richard Laing and Fatma Bouali and Gilles Venturini and Marco Temperini and Muhammad Sarfraz},
doi = {10.1109/IV53921.2021.00001},
isbn = {978-1-6654-3827-8},
year = {2021},
date = {2021-10-28},
urldate = {2021-10-28},
booktitle = {Information Visualisation: AI & Analytics, Biomedical Visualization, Builtviz, and Geometric Modelling & Imaging},
pages = {1-775},
publisher = {IEEE},
address = {New York, USA},
abstract = {Most aspects of our lives depend on and are driven by data, information, knowledge, user experience, and cultural influences in the current information era. The infrastructure of any informationdependent society relies on the quality of data, information, and analysis of such entities from past and present and projected future activities in addition and possibly most importantly, how it intended to be applied. Information Visualization, Analytics, Machine Learning, Artificial Intelligence and Application domains are just a few of the current state of the art developments that effectively enhance understanding of these driving forces. Several key interdependent variables are emerging that are becoming the focus of scientific activities, such as Information and Data Science. Aspects that tightly couples raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scale to knowledge acquisition. Its dependencies on the domain of application and its evolution steer the next generation of research activities. From the raw data to knowledge, processing the relationship between these phases has added new impetus to how these are understood and communicated. The tradition of use and communication by visualization is deep-rooted. It helps us investigate new meanings for the humanities, history of art, design, human factors, and user experience, leading to discoveries and hypothesis analysis. Modern-day computer-aided analytics and visualization have added momentum in developing tools that exploit metaphor-driven techniques within many applied domains. The methods are developed beyond visualization to simplify the complexities, reveal ambiguity, and work with incompleteness. The next phase of this evolving field is to understand uncertainty, risk analysis, and tapping into unknowns; this uncertainty is built into the processes in all stages of the process, from raw data to the knowledge acquisition stage.
This collection of papers on this year's information visualization forum, compiled for the 25th conference on the Information Visualization – incorporating Artificial Intelligence – analytics, machine-, deep-learning, and Learning Analytics - IV2021, advocates that a new conceptual framework will emerge from information-rich disciplines like the Humanities, Psychology, Sociology, Business of everyday activities as well as the science-rich disciplines. To facilitate this, IV2021 provides the opportunity to resonate with many international and collaborative research projects and lectures and panel discussion from distinguished speakers that channels the way this new framework conceptually and practically has been realized. This year's theme is enhanced further by AI, Social-Networking impact the social, cultural, and heritage aspects of life and learning analysis of today's multifaceted and data-rich environment.
Joining us in this search are some 75 plus researchers who reflect and share a chapter of their thoughts with fellow researchers. The papers collected, peer-reviewed by the international reviewing committee, reflect the vibrant state of information visualization, analytics, applications, and results of researchers, artists, and professionals from more than 25 countries. It has allowed us to address the scope of visualization from a much broader perspective. Each contributor to this conference has added fresh views and thoughts, challenges our beliefs, and further encourages our adventure of innovation.},
keywords = {Information visualization},
pubstate = {published},
tppubtype = {proceedings}
}
Most aspects of our lives depend on and are driven by data, information, knowledge, user experience, and cultural influences in the current information era. The infrastructure of any informationdependent society relies on the quality of data, information, and analysis of such entities from past and present and projected future activities in addition and possibly most importantly, how it intended to be applied. Information Visualization, Analytics, Machine Learning, Artificial Intelligence and Application domains are just a few of the current state of the art developments that effectively enhance understanding of these driving forces. Several key interdependent variables are emerging that are becoming the focus of scientific activities, such as Information and Data Science. Aspects that tightly couples raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scale to knowledge acquisition. Its dependencies on the domain of application and its evolution steer the next generation of research activities. From the raw data to knowledge, processing the relationship between these phases has added new impetus to how these are understood and communicated. The tradition of use and communication by visualization is deep-rooted. It helps us investigate new meanings for the humanities, history of art, design, human factors, and user experience, leading to discoveries and hypothesis analysis. Modern-day computer-aided analytics and visualization have added momentum in developing tools that exploit metaphor-driven techniques within many applied domains. The methods are developed beyond visualization to simplify the complexities, reveal ambiguity, and work with incompleteness. The next phase of this evolving field is to understand uncertainty, risk analysis, and tapping into unknowns; this uncertainty is built into the processes in all stages of the process, from raw data to the knowledge acquisition stage.
This collection of papers on this year's information visualization forum, compiled for the 25th conference on the Information Visualization – incorporating Artificial Intelligence – analytics, machine-, deep-learning, and Learning Analytics - IV2021, advocates that a new conceptual framework will emerge from information-rich disciplines like the Humanities, Psychology, Sociology, Business of everyday activities as well as the science-rich disciplines. To facilitate this, IV2021 provides the opportunity to resonate with many international and collaborative research projects and lectures and panel discussion from distinguished speakers that channels the way this new framework conceptually and practically has been realized. This year's theme is enhanced further by AI, Social-Networking impact the social, cultural, and heritage aspects of life and learning analysis of today's multifaceted and data-rich environment.
Joining us in this search are some 75 plus researchers who reflect and share a chapter of their thoughts with fellow researchers. The papers collected, peer-reviewed by the international reviewing committee, reflect the vibrant state of information visualization, analytics, applications, and results of researchers, artists, and professionals from more than 25 countries. It has allowed us to address the scope of visualization from a much broader perspective. Each contributor to this conference has added fresh views and thoughts, challenges our beliefs, and further encourages our adventure of innovation. |
| 40. | Kawa Nazemi; Dirk Burkhardt; Alexander Kock Visual analytics for technology and innovation management: An interaction approach for strategic decisionmaking Journal Article In: Multimedia Tools and Applications, vol. 1198, 2021, ISSN: 1573-7721. @article{Nazemi2021b,
title = {Visual analytics for technology and innovation management: An interaction approach for strategic decisionmaking },
author = {Kawa Nazemi and Dirk Burkhardt and Alexander Kock},
editor = {Rita Francese and Borko Furht},
doi = {10.1007/s11042-021-10972-3},
issn = {1573-7721},
year = {2021},
date = {2021-05-20},
journal = {Multimedia Tools and Applications},
volume = {1198},
abstract = {The awareness of emerging trends is essential for strategic decision making because technological trends can affect a firm’s competitiveness and market position. The rise of artificial intelligence methods allows gathering new insights and may support these decision-making processes. However, it is essential to keep the human in the loop of these complex analytical tasks, which, often lack an appropriate interaction design. Including special interactive designs for technology and innovation management is therefore essential for successfully analyzing emerging trends and using this information for strategic decision making. A combination of information visualization, trend mining and interaction design can support human users to explore, detect, and identify such trends. This paper enhances and extends a previously published first approach for integrating, enriching, mining, analyzing, identifying, and visualizing emerging trends for technology and innovation management. We introduce a novel interaction design by investigating the main ideas from technology and innovation management and enable a more appropriate interaction approach for technology foresight and innovation detection.},
keywords = {emerging trend identification, Information visualization, Innovation Management, Interaction Design, Multimodal Interaction, Technology Management, Visual analytics, Visual Trend Analytics},
pubstate = {published},
tppubtype = {article}
}
The awareness of emerging trends is essential for strategic decision making because technological trends can affect a firm’s competitiveness and market position. The rise of artificial intelligence methods allows gathering new insights and may support these decision-making processes. However, it is essential to keep the human in the loop of these complex analytical tasks, which, often lack an appropriate interaction design. Including special interactive designs for technology and innovation management is therefore essential for successfully analyzing emerging trends and using this information for strategic decision making. A combination of information visualization, trend mining and interaction design can support human users to explore, detect, and identify such trends. This paper enhances and extends a previously published first approach for integrating, enriching, mining, analyzing, identifying, and visualizing emerging trends for technology and innovation management. We introduce a novel interaction design by investigating the main ideas from technology and innovation management and enable a more appropriate interaction approach for technology foresight and innovation detection. |
| 39. | Midhad Blazevic; Lennart B. Sina; Dirk Burkhardt; Melanie Siegel; Kawa Nazemi Visual Analytics and Similarity Search - Interest-based Similarity Search in Scientific Data Inproceedings In: 2021 25th International Conference Information Visualisation (IV), pp. 211-217, IEEE, 2021. @inproceedings{9582711,
title = {Visual Analytics and Similarity Search - Interest-based Similarity Search in Scientific Data},
author = {Midhad Blazevic and Lennart B. Sina and Dirk Burkhardt and Melanie Siegel and Kawa Nazemi},
doi = {10.1109/IV53921.2021.00041},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {2021 25th International Conference Information Visualisation (IV)},
pages = {211-217},
publisher = {IEEE},
abstract = {Visual Analytics enables solving complex analytical tasks by coupling interactive visualizations and machine learning approaches. Besides the analytical reasoning enabled through Visual Analytics, the exploration of data plays an essential role. The exploration process can be supported through similarity-based approaches that enable finding similar data to those annotated in the context of visual exploration. We propose in this paper a process of annotation in the context of exploration that leads to labeled vectors-of-interest and enables finding similar publications based on interest vectors. The generation and labeling of the interest vectors are performed automatically by the Visual Analytics system and lead to finding similar papers and categorizing the annotated papers. With this approach, we provide a categorized similarity search based on an automatically labeled interest matrix in Visual Analytics.},
keywords = {Artificial Intelligence, Collaboration, Collaborative Systems, Information visualization, Similarity, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
Visual Analytics enables solving complex analytical tasks by coupling interactive visualizations and machine learning approaches. Besides the analytical reasoning enabled through Visual Analytics, the exploration of data plays an essential role. The exploration process can be supported through similarity-based approaches that enable finding similar data to those annotated in the context of visual exploration. We propose in this paper a process of annotation in the context of exploration that leads to labeled vectors-of-interest and enables finding similar publications based on interest vectors. The generation and labeling of the interest vectors are performed automatically by the Visual Analytics system and lead to finding similar papers and categorizing the annotated papers. With this approach, we provide a categorized similarity search based on an automatically labeled interest matrix in Visual Analytics. |
2020 |
| 38. | Ebad Banissi; Farzad Khosrow-shahi; Anna Ursyn; Mark W. McK. Bannatyne; João Moura Pires; Nuno Datia; Kawa Nazemi; Boris Kovalerchuk; John Counsell; Andrew Agapiou; Zora Vrcelj; Hing-Wah Chau; Mengbi Li; Gehan Nagy; Richard Laing; Rita Francese; Muhammad Sarfraz; Fatma Bouali; Gilles Venturin; Marjan Trutschl; Urska Cvek; Heimo Müller; Minoru Nakayama; Marco Temperini; Tania Di Mascio; Filippo SciarroneVeronica Rossano; Ralf Dörner; Loredana Caruccio; Autilia Vitiello; Weidong Huang; Michele Risi; Ugo Erra; Razvan Andonie; Muhammad Aurangzeb Ahmad; Ana Figueiras; and Mabule Samuel Mabakane (Ed.) Proceedings of 2020 24th International Conference Information Visualisation (IV) Proceeding IEEE, New York, USA, 2020, ISBN: 978-1-7281-9134-8. @proceedings{Banissi2020,
title = {Proceedings of 2020 24th International Conference Information Visualisation (IV)},
editor = {Ebad Banissi and Farzad Khosrow-shahi and Anna Ursyn and Mark W. McK. Bannatyne and João Moura Pires and Nuno Datia and Kawa Nazemi and Boris Kovalerchuk and John Counsell and Andrew Agapiou and Zora Vrcelj and Hing-Wah Chau and Mengbi Li and Gehan Nagy and Richard Laing and Rita Francese and Muhammad Sarfraz and Fatma Bouali and Gilles Venturin and Marjan Trutschl and Urska Cvek and Heimo Müller and Minoru Nakayama and Marco Temperini and Tania Di Mascio and Filippo SciarroneVeronica Rossano and Ralf Dörner and Loredana Caruccio and Autilia Vitiello and Weidong Huang and Michele Risi and Ugo Erra and Razvan Andonie and Muhammad Aurangzeb Ahmad and Ana Figueiras and and Mabule Samuel Mabakane},
doi = {10.1109/IV51561.2020},
isbn = {978-1-7281-9134-8},
year = {2020},
date = {2020-09-01},
booktitle = {Information Visualisation: AI & Analytics, Biomedical Visualization, Builtviz, and Geometric Modelling & Imaging},
pages = {1-775},
publisher = {IEEE},
address = {New York, USA},
abstract = {In the current information era, most aspects of life depend on and are driven by data, information, knowledge, user experience, and cultural influences. The infrastructure of any information-dependent society relies on the quality of data, information and analysis of such entities for short to long term as well as past and future activities. Information Visualisation, Analytics, Machine Learning, Artificial Intelligence and Application domains are just a few of the current state of the art developments that effectively enhance understanding of these driving forces. Several key interdependent variables are emerging that are becoming the focus of scientific activities, such as Information and Data Science, an aspect that tightly couples raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scale to knowledge acquisition such that its dependencies on the domain of application and its evolution steer the next generation of research activities. Processing the relationship between these phases, from the raw data to knowledge, has added new impetus to the way these are understood and communicated. The tradition of use and communication by visualisation is deep-rooted. It helps us investigate new meanings for the humanities, history of art, design, human factors, and user experience and leads to discoveries and hypothesis analysis. Modern-day computer-aided analytics and visualisation have added momentum in developing tools that exploit 2D and 3D metaphor-driven techniques within many applied domains. The techniques are developed beyond visualisation to simplify the complexities, to reveal ambiguity, and to work with incompleteness. The next phase of this evolving field is to understand uncertainty, risk analysis, and tapping into unknowns; how this uncertainty is built into the processes that exist in all stages of the process, from raw data to the knowledge acquisition stage.
This collection of papers on this year's information visualisation forum, compiled for the 24th conference on the Information Visualization – incorporating Artificial Intelligence – analytics, machine- & deeplearning - Biomedical Visualization, Learning Analytics & Geometric Modelling and Imaging - IV2020, advocates that a new conceptual framework will emerge from information-rich disciplines like the Humanities, Psychology, Sociology, Business of everyday activities as well as the science-rich disciplines. To facilitate this, IV2020 provides the opportunity to resonate with many international and collaborative research projects as well as lectures from distinguished speakers that channels the way this new framework conceptually, as well as practically has been realised. This year's theme is enhanced further by AI, Social Networks impact on social, cultural and heritage aspect of life and learning analysis of today's multifaceted and data-rich environment.
Joining us in this search are some 100 plus researchers who reflect and share a chapter of their thoughts with fellow researchers. The papers collected, peer-reviewed by the international reviewing committee, reflect the vibrant state of information visualisation, analytics, applications, and results of the work of researchers, artists and professionals from more than 25 countries. It has allowed us to address the scope of visualisation from a much broader perspective. Each contributor to this conference has indeed added fresh perspectives and thoughts, challenges our beliefs and encouraged further our adventure of innovation.},
keywords = {Information visualization},
pubstate = {published},
tppubtype = {proceedings}
}
In the current information era, most aspects of life depend on and are driven by data, information, knowledge, user experience, and cultural influences. The infrastructure of any information-dependent society relies on the quality of data, information and analysis of such entities for short to long term as well as past and future activities. Information Visualisation, Analytics, Machine Learning, Artificial Intelligence and Application domains are just a few of the current state of the art developments that effectively enhance understanding of these driving forces. Several key interdependent variables are emerging that are becoming the focus of scientific activities, such as Information and Data Science, an aspect that tightly couples raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scale to knowledge acquisition such that its dependencies on the domain of application and its evolution steer the next generation of research activities. Processing the relationship between these phases, from the raw data to knowledge, has added new impetus to the way these are understood and communicated. The tradition of use and communication by visualisation is deep-rooted. It helps us investigate new meanings for the humanities, history of art, design, human factors, and user experience and leads to discoveries and hypothesis analysis. Modern-day computer-aided analytics and visualisation have added momentum in developing tools that exploit 2D and 3D metaphor-driven techniques within many applied domains. The techniques are developed beyond visualisation to simplify the complexities, to reveal ambiguity, and to work with incompleteness. The next phase of this evolving field is to understand uncertainty, risk analysis, and tapping into unknowns; how this uncertainty is built into the processes that exist in all stages of the process, from raw data to the knowledge acquisition stage.
This collection of papers on this year's information visualisation forum, compiled for the 24th conference on the Information Visualization – incorporating Artificial Intelligence – analytics, machine- & deeplearning - Biomedical Visualization, Learning Analytics & Geometric Modelling and Imaging - IV2020, advocates that a new conceptual framework will emerge from information-rich disciplines like the Humanities, Psychology, Sociology, Business of everyday activities as well as the science-rich disciplines. To facilitate this, IV2020 provides the opportunity to resonate with many international and collaborative research projects as well as lectures from distinguished speakers that channels the way this new framework conceptually, as well as practically has been realised. This year's theme is enhanced further by AI, Social Networks impact on social, cultural and heritage aspect of life and learning analysis of today's multifaceted and data-rich environment.
Joining us in this search are some 100 plus researchers who reflect and share a chapter of their thoughts with fellow researchers. The papers collected, peer-reviewed by the international reviewing committee, reflect the vibrant state of information visualisation, analytics, applications, and results of the work of researchers, artists and professionals from more than 25 countries. It has allowed us to address the scope of visualisation from a much broader perspective. Each contributor to this conference has indeed added fresh perspectives and thoughts, challenges our beliefs and encouraged further our adventure of innovation. |
2019 |
| 37. | Kawa Nazemi; Dirk Burkhardt A Visual Analytics Approach for Analyzing Technological Trends in Technology and Innovation Management Inproceedings In: George Bebis; Richard Boyle; Bahram Parvin; Darko Koracin; Daniela Ushizima; Sek Chai; Shinjiro Sueda; Xin Lin; Aidong Lu; Daniel Thalmann; Chaoli Wang; Panpan Xu (Ed.): Advances in Visual Computing, pp. 283–294, Springer International Publishing, Cham, 2019, ISBN: 978-3-030-33723-0. @inproceedings{Nazemi_ISVC2019,
title = {A Visual Analytics Approach for Analyzing Technological Trends in Technology and Innovation Management},
author = {Kawa Nazemi and Dirk Burkhardt},
editor = {George Bebis and Richard Boyle and Bahram Parvin and Darko Koracin and Daniela Ushizima and Sek Chai and Shinjiro Sueda and Xin Lin and Aidong Lu and Daniel Thalmann and Chaoli Wang and Panpan Xu},
url = {https://rd.springer.com/chapter/10.1007/978-3-030-33723-0_23, Springer LNCS
https://dx.doi.org/10.5281/zenodo.3473065, doi:10.5281/zenodo.3473065 (Poster)},
doi = {10.1007/978-3-030-33723-0_23},
isbn = {978-3-030-33723-0},
year = {2019},
date = {2019-10-09},
booktitle = {Advances in Visual Computing},
pages = {283--294},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {Visual Analytics provides with a combination of automated techniques and interactive visualizations huge analysis possibilities in technology and innovation management. Thereby not only the use of machine learning data mining methods plays an important role. Due to the high interaction capabilities, it provides a more user-centered approach, where users are able to manipulate the entire analysis process and get the most valuable information. Existing Visual Analytics systems for Trend Analytics and technology and innovation management do not really make use of this unique feature and almost neglect the human in the analysis process. Outcomes from research in information search, information visualization and technology management can lead to more sophisticated Visual Analytics systems that involved the human in the entire analysis process. We propose in this paper a new interaction approach for Visual Analytics in technology and innovation management with a special focus on technological trend analytics.},
keywords = {Artificial Intelligence, Data Analytics, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, maschine learning, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
Visual Analytics provides with a combination of automated techniques and interactive visualizations huge analysis possibilities in technology and innovation management. Thereby not only the use of machine learning data mining methods plays an important role. Due to the high interaction capabilities, it provides a more user-centered approach, where users are able to manipulate the entire analysis process and get the most valuable information. Existing Visual Analytics systems for Trend Analytics and technology and innovation management do not really make use of this unique feature and almost neglect the human in the analysis process. Outcomes from research in information search, information visualization and technology management can lead to more sophisticated Visual Analytics systems that involved the human in the entire analysis process. We propose in this paper a new interaction approach for Visual Analytics in technology and innovation management with a special focus on technological trend analytics. |
| 36. | Dirk Burkhardt; Kawa Nazemi; Arjan Kuijper; Egils Ginters A Mobile Visual Analytics Approach for Instant Trend Analysis in Mobile Contexts Inproceedings In: 5th International Conference of the Virtual and Augmented Reality in Education (VARE2019), pp. 11–19, CAL-TEK SRL, Rende, Italy, 2019, ISBN: 978-88-85741-41-6, (Nominated for Best Paper Award). @inproceedings{Burkhardt2019b,
title = {A Mobile Visual Analytics Approach for Instant Trend Analysis in Mobile Contexts},
author = {Dirk Burkhardt and Kawa Nazemi and Arjan Kuijper and Egils Ginters},
doi = {10.5281/zenodo.3473041},
isbn = {978-88-85741-41-6},
year = {2019},
date = {2019-09-18},
booktitle = {5th International Conference of the Virtual and Augmented Reality in Education (VARE2019)},
pages = {11--19},
publisher = {CAL-TEK SRL},
address = {Rende, Italy},
abstract = {The awareness of market trends becomes relevant for a broad number of market branches, in particular the more they are challenged by the digitalization. Trend analysis solutions help business executives identifying upcoming trends early. But solid market analysis takes their time and are often not available on consulting or strategy discussions. This circumstance often leads to unproductive debates where no clear strategy, technology etc. could be identified. Therefore, we propose a mobile visual trend analysis approach that enables a quick trend analysis to identify at least the most relevant and irrelevant aspects to focus debates on the relevant options. To enable an analysis like this, the exhausting analysis on powerful workstations with large screens has to adopted to mobile devices within a mobile behavior. Our main contribution is the therefore a new approach of a mobile knowledge cockpit, which provides different analytical visualizations within and intuitive interaction design.},
note = {Nominated for Best Paper Award},
keywords = {Business Analytics, Decision Support Systems, Human-Computer Interaction, Information visualization, Mobile Devices, Mobile Visual Analytics, Visual Trend Analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
The awareness of market trends becomes relevant for a broad number of market branches, in particular the more they are challenged by the digitalization. Trend analysis solutions help business executives identifying upcoming trends early. But solid market analysis takes their time and are often not available on consulting or strategy discussions. This circumstance often leads to unproductive debates where no clear strategy, technology etc. could be identified. Therefore, we propose a mobile visual trend analysis approach that enables a quick trend analysis to identify at least the most relevant and irrelevant aspects to focus debates on the relevant options. To enable an analysis like this, the exhausting analysis on powerful workstations with large screens has to adopted to mobile devices within a mobile behavior. Our main contribution is the therefore a new approach of a mobile knowledge cockpit, which provides different analytical visualizations within and intuitive interaction design. |
| 35. | Kawa Nazemi; Dirk Burkhardt Visual Analytics for Analyzing Technological Trends from Text Inproceedings In: 2019 23rd International Conference Information Visualisation (IV), pp. 191-200, IEEE, 2019, ISSN: 2375-0138, (Best Paper Award). @inproceedings{Nazemi2019d,
title = {Visual Analytics for Analyzing Technological Trends from Text},
author = {Kawa Nazemi and Dirk Burkhardt},
doi = {10.1109/IV.2019.00041},
issn = {2375-0138},
year = {2019},
date = {2019-07-01},
urldate = {2019-07-01},
booktitle = {2019 23rd International Conference Information Visualisation (IV)},
pages = {191-200},
publisher = {IEEE},
abstract = {The awareness of emerging technologies is essential for strategic decision making in enterprises. Emerging and decreasing technological trends could lead to strengthening the competitiveness and market positioning. The exploration, detection and identification of such trends can be essentially supported through information visualization, trend mining and in particular through the combination of those. Commonly, trends appear first in science and scientific documents. However, those documents do not provide sufficient information for analyzing and identifying emerging trends. It is necessary to enrich data, extract information from the integrated data, measure the gradient of trends over time and provide effective interactive visualizations. We introduce in this paper an approach for integrating, enriching, mining, analyzing, identifying and visualizing emerging trends from scientific documents. Our approach enhances the state of the art in visual trend analytics by investigating the entire analysis process and providing an approach for enabling human to explore undetected potentially emerging trends.},
note = {Best Paper Award},
keywords = {Artificial Intelligence, Data Mining, Data Models, Data Visualization, emerging trend identification, Hidden Markov models, Information visualization, Market research, Patents, Trend Analytics, Visual analytics, visual business analytics, Visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
The awareness of emerging technologies is essential for strategic decision making in enterprises. Emerging and decreasing technological trends could lead to strengthening the competitiveness and market positioning. The exploration, detection and identification of such trends can be essentially supported through information visualization, trend mining and in particular through the combination of those. Commonly, trends appear first in science and scientific documents. However, those documents do not provide sufficient information for analyzing and identifying emerging trends. It is necessary to enrich data, extract information from the integrated data, measure the gradient of trends over time and provide effective interactive visualizations. We introduce in this paper an approach for integrating, enriching, mining, analyzing, identifying and visualizing emerging trends from scientific documents. Our approach enhances the state of the art in visual trend analytics by investigating the entire analysis process and providing an approach for enabling human to explore undetected potentially emerging trends. |
| 34. | Kawa Nazemi Visual Trend Analytics in Digital Libraries Miscellaneous Contribution at ASIS&T European Chapter Seminar on Information Science Trends: Search Engines and Information Retrieval., 2019. @misc{Naz19ASIST,
title = {Visual Trend Analytics in Digital Libraries},
author = {Kawa Nazemi},
url = {https://zenodo.org/record/3264801#.XSBcMo_gpaR, Zenodo Open Access},
doi = {10.5281/zenodo.3264801},
year = {2019},
date = {2019-04-26},
abstract = {The early awareness of upcoming trends in technology enables a more goal-directed and efficient way for deciding future strategic directions in enterprises and research. Possible sources for this valuable information are ubiquitously and freely available in the Web, e.g. news services, companies’ reports, social media platforms and blog infrastructures. To support users in handling these information sources and to keep track of the newest developments, current information systems make intensively use of information retrieval methods that extract relevant information out of the mass amount of data. The related information systems are commonly focused on providing users with easy access to information of their interest and deal with the access to information items and resources [1], but they neither provide an overview of the content nor enable the exploration of emerging or decreasing trends for inferring possible future innovations. The gathering and analysis of this continuously increasing knowledge pool is a very tedious and time-consuming task and borders on the limits of manual feasibility. The interactive overview on data, the continuous changes in data, and the ability to explore data and gain insights are sufficiently supported by Visual Analytics and information visualization approaches, whereas the appliance of such approach in combination with trend analysis are rarely propagated. In fact, these so-called early signals require not only an analysis through machine learning techniques to identify emerging trends, but also human interaction and intervention to adapt the parameters used to their own needs [2]. There are two main aspects to consider in the analysis process: 1) which data reveal very early trends and 2) how can human be involved in the analysis process [3].},
howpublished = {Contribution at ASIS&T European Chapter Seminar on Information Science Trends: Search Engines and Information Retrieval.},
keywords = {Information visualization, Trend analysis, Trend Analytics, Visual analytics},
pubstate = {published},
tppubtype = {misc}
}
The early awareness of upcoming trends in technology enables a more goal-directed and efficient way for deciding future strategic directions in enterprises and research. Possible sources for this valuable information are ubiquitously and freely available in the Web, e.g. news services, companies’ reports, social media platforms and blog infrastructures. To support users in handling these information sources and to keep track of the newest developments, current information systems make intensively use of information retrieval methods that extract relevant information out of the mass amount of data. The related information systems are commonly focused on providing users with easy access to information of their interest and deal with the access to information items and resources [1], but they neither provide an overview of the content nor enable the exploration of emerging or decreasing trends for inferring possible future innovations. The gathering and analysis of this continuously increasing knowledge pool is a very tedious and time-consuming task and borders on the limits of manual feasibility. The interactive overview on data, the continuous changes in data, and the ability to explore data and gain insights are sufficiently supported by Visual Analytics and information visualization approaches, whereas the appliance of such approach in combination with trend analysis are rarely propagated. In fact, these so-called early signals require not only an analysis through machine learning techniques to identify emerging trends, but also human interaction and intervention to adapt the parameters used to their own needs [2]. There are two main aspects to consider in the analysis process: 1) which data reveal very early trends and 2) how can human be involved in the analysis process [3]. |
| 33. | Kawa Nazemi; Dirk Burkhardt Visual analytical dashboards for comparative analytical tasks – a case study on mobility and transportation Journal Article In: ICTE in Transportation and Logistics 2018 (ICTE 2018), vol. 149, pp. 138-150, 2019, ISSN: 1877-0509. @article{Nazemi2019,
title = {Visual analytical dashboards for comparative analytical tasks – a case study on mobility and transportation},
author = {Kawa Nazemi and Dirk Burkhardt},
url = {http://www.sciencedirect.com/science/article/pii/S1877050919301243, Link to Publisher},
doi = {10.1016/j.procs.2019.01.117},
issn = {1877-0509},
year = {2019},
date = {2019-01-01},
journal = {ICTE in Transportation and Logistics 2018 (ICTE 2018)},
volume = {149},
pages = {138-150},
series = {Procedia Computer Science},
abstract = {Mobility, logistics and transportation are emerging fields of research and application. Humans’ mobility behavior plays an increasing role for societal challenges. Beside the societal challenges these areas are strongly related to technologies and innovations. Gathering information about emerging technologies plays an increasing role for the entire research in these areas. Humans’ information processing can be strongly supported by Visual Analytics that combines automatic modelling and interactive visualizations. The juxtapose orchestration of interactive visualization enables gathering more information in a shorter time. We propose in this paper an approach that goes beyond the established methods of dashboarding and enables visualizing different databases, data-sets and sub-sets of data with juxtaposed visual interfaces. Our approach should be seen as an expandable method. Our main contributions are an in-depth analysis of visual task models and an approach for juxtaposing visual layouts as visual dashboards to enable solving complex tasks. We illustrate our main outcome through a case study that investigates the area of mobility and illustrates how complex analytical tasks can be performed easily by combining different visual interfaces.},
keywords = {Data Analytics, Information visualization, Mobility, Prediction, Transportation, Visual analytics, Visual Interfaces, Visual Tasks},
pubstate = {published},
tppubtype = {article}
}
Mobility, logistics and transportation are emerging fields of research and application. Humans’ mobility behavior plays an increasing role for societal challenges. Beside the societal challenges these areas are strongly related to technologies and innovations. Gathering information about emerging technologies plays an increasing role for the entire research in these areas. Humans’ information processing can be strongly supported by Visual Analytics that combines automatic modelling and interactive visualizations. The juxtapose orchestration of interactive visualization enables gathering more information in a shorter time. We propose in this paper an approach that goes beyond the established methods of dashboarding and enables visualizing different databases, data-sets and sub-sets of data with juxtaposed visual interfaces. Our approach should be seen as an expandable method. Our main contributions are an in-depth analysis of visual task models and an approach for juxtaposing visual layouts as visual dashboards to enable solving complex tasks. We illustrate our main outcome through a case study that investigates the area of mobility and illustrates how complex analytical tasks can be performed easily by combining different visual interfaces. |
2018 |
| 32. | Kawa Nazemi; Dirk Burkhardt Juxtaposing Visual Layouts – An Approach for Solving Analytical and Exploratory Tasks through Arranging Visual Interfaces Inproceedings In: A. G. Bruzzone; Egils Ginters; E. G. Mendívil; J. M. Guitierrez; F. Longo (Ed.): The 4th International Conference of the Virtual and Augmented Reality in Education, I3M, 2018, ISBN: 978-88-85741-21-8. @inproceedings{Nazemi2018b,
title = {Juxtaposing Visual Layouts – An Approach for Solving Analytical and Exploratory Tasks through Arranging Visual Interfaces},
author = {Kawa Nazemi and Dirk Burkhardt},
editor = {A. G. Bruzzone and Egils Ginters and E. G. Mendívil and J. M. Guitierrez and F. Longo},
doi = {10.5281/zenodo.2542952},
isbn = {978-88-85741-21-8},
year = {2018},
date = {2018-09-18},
booktitle = {The 4th International Conference of the Virtual and Augmented Reality in Education},
publisher = {I3M},
abstract = {Interactive visualization and visual analytics systems enables solving a variety of tasks. Starting with simple search tasks for outliers, anomalies etc. in data to analytical comparisons, information visualizations may lead to a faster and more precise solving of tasks. There exist a variety of methods to support users in the process of task solving, e.g. superimposing, juxtaposing or partitioning complex visual structures. Commonly all these methods make use of a single data source that is visualized at the same time. We propose in this paper an approach that goes beyond the established methods and enables visualizing different databases, data-sets and sub-sets of data with juxtaposed visual interfaces. Our approach should be seen as an expandable method. Our main contributions are an in-depth analysis of visual task models and an approach for juxtaposing visual layouts as visual interfaces to enable solving complex tasks.},
keywords = {Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
Interactive visualization and visual analytics systems enables solving a variety of tasks. Starting with simple search tasks for outliers, anomalies etc. in data to analytical comparisons, information visualizations may lead to a faster and more precise solving of tasks. There exist a variety of methods to support users in the process of task solving, e.g. superimposing, juxtaposing or partitioning complex visual structures. Commonly all these methods make use of a single data source that is visualized at the same time. We propose in this paper an approach that goes beyond the established methods and enables visualizing different databases, data-sets and sub-sets of data with juxtaposed visual interfaces. Our approach should be seen as an expandable method. Our main contributions are an in-depth analysis of visual task models and an approach for juxtaposing visual layouts as visual interfaces to enable solving complex tasks. |
| 31. | Dirk Burkhardt; Kawa Nazemi Visualizing Law - A Norm-Graph Visualization Approach based on Semantic Legal Data Inproceedings In: The 4th International Conference of the Virtual and Augmented Reality in Education, I3M, 2018, ISBN: 978-88-85741-21-8. @inproceedings{Burkhardt2018,
title = {Visualizing Law - A Norm-Graph Visualization Approach based on Semantic Legal Data},
author = {Dirk Burkhardt and Kawa Nazemi},
doi = {10.5281/zenodo.2543729},
isbn = {978-88-85741-21-8},
year = {2018},
date = {2018-09-16},
booktitle = {The 4th International Conference of the Virtual and Augmented Reality in Education},
publisher = {I3M},
abstract = {Laws or in general legal documents regulate a wide range of our daily life and also define the borders of business models and commercial services. However, legal text and laws are almost hard to understand. From other domains it is already known that visualizations can help understanding complex aspects easier. In fact, in this paper we introduce a new approach to visualize legal texts in a Norm-graph visualization. In the developed Norm-graph visualization it is possible to show major aspects of laws and make it easier for users to understand it. The Norm-graph is based on semantic legal data, a so called Legal-Concept-Ontology.},
keywords = {Human Factors, Human-computer interaction (HCI), Information visualization, Semantics visualization, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
Laws or in general legal documents regulate a wide range of our daily life and also define the borders of business models and commercial services. However, legal text and laws are almost hard to understand. From other domains it is already known that visualizations can help understanding complex aspects easier. In fact, in this paper we introduce a new approach to visualize legal texts in a Norm-graph visualization. In the developed Norm-graph visualization it is possible to show major aspects of laws and make it easier for users to understand it. The Norm-graph is based on semantic legal data, a so called Legal-Concept-Ontology. |
2017 |
| 30. | Dirk Burkhardt; Sachin Pattan; Kawa Nazemi; Arjan Kuijper Search Intention Analysis for Task- and User-Centered Visualization in Big Data Applications Journal Article In: Procedia Computer Science, vol. 104, pp. 539 - 547, 2017, ISSN: 1877-0509, (ICTE 2016, Riga Technical University, Latvia). @article{Burkhardt2017c,
title = {Search Intention Analysis for Task- and User-Centered Visualization in Big Data Applications},
author = {Dirk Burkhardt and Sachin Pattan and Kawa Nazemi and Arjan Kuijper},
doi = {10.1016/j.procs.2017.01.170},
issn = {1877-0509},
year = {2017},
date = {2017-12-01},
urldate = {2017-12-01},
journal = {Procedia Computer Science},
volume = {104},
pages = {539 - 547},
abstract = {A new approach for classifying users’ search intentions is described in this paper. The approach uses the parameters: word frequency, query length and entity matching for distinguishing the user's query into exploratory, targeted and analysis search. The approach focuses mainly on word frequency analysis, where different sources for word frequency data are considered such as the Wortschatz frequency service by the University of Leipzig and the Microsoft Ngram service (now part of the Microsoft Cognitive Services). The model is evaluated with the help of a survey tool and few machine learning techniques. The survey was conducted with more than one hundred users and on evaluating the model with the collected data, the results are satisfactory. In big data applications the search intention analysis can be used to identify the purpose of a performed search, to provide an optimal initially set of visualizations that respects the intended task of the user to work with the result data.},
note = {ICTE 2016, Riga Technical University, Latvia},
keywords = {Information visualization, Intelligent Systems, User behavior, User Interactions, User Interface, User-centered design, Visual analytics},
pubstate = {published},
tppubtype = {article}
}
A new approach for classifying users’ search intentions is described in this paper. The approach uses the parameters: word frequency, query length and entity matching for distinguishing the user's query into exploratory, targeted and analysis search. The approach focuses mainly on word frequency analysis, where different sources for word frequency data are considered such as the Wortschatz frequency service by the University of Leipzig and the Microsoft Ngram service (now part of the Microsoft Cognitive Services). The model is evaluated with the help of a survey tool and few machine learning techniques. The survey was conducted with more than one hundred users and on evaluating the model with the collected data, the results are satisfactory. In big data applications the search intention analysis can be used to identify the purpose of a performed search, to provide an optimal initially set of visualizations that respects the intended task of the user to work with the result data. |
| 29. | Dirk Burkhardt; Kawa Nazemi Informationsvisualisierung und Visual Analytics zur Unterstützung von E-Government Prozessen Inproceedings In: Korinna Bade; Matthias Pietsch; Susanne Raabe; Lars Schütz (Ed.): Technologische Trends im Spannungsfeld von Beteiligung – Entscheidung – Planung, pp. 29–38, Shaker Verlag, Aachen, Germany, 2017, ISBN: 978-3844054392. @inproceedings{Burkhardt2017b,
title = {Informationsvisualisierung und Visual Analytics zur Unterstützung von E-Government Prozessen},
author = {Dirk Burkhardt and Kawa Nazemi},
editor = {Korinna Bade and Matthias Pietsch and Susanne Raabe and Lars Schütz},
url = {https://www.shaker.de/de/content/catalogue/index.asp?lang=de&ID=8&ISBN=978-3-8440-5439-2&search=yes, Publisher Site
https://dx.doi.org/10.2370/9783844054392, doi:10.2370/9783844054392 (Full Proceedings)},
doi = {10.5281/zenodo.2576074},
isbn = {978-3844054392},
year = {2017},
date = {2017-01-05},
booktitle = {Technologische Trends im Spannungsfeld von Beteiligung – Entscheidung – Planung},
pages = {29--38},
publisher = {Shaker Verlag},
address = {Aachen, Germany},
abstract = {Politische und gesellschaftliche Prozesse werden durch Informationen sehr stark geprägt, wie auch die jüngsten Ereignisse aufzeigen. Diese Informationen können, trotz enormer Fortschritte, nicht immer aus den sehr großen, heterogenen und verteilten Daten entnommen werden. „Big Data“ stellt somit auch in der öffentlichen Verwaltung eine immer größere Herausforderung dar. Sowohl durch eine umfangreiche Erhebung von Statistiken, als auch durch Dokumente wie Berichte und Studien, wachsen in Behörden die zu bewältigenden Informationsaufgaben. Darüber hinaus spielt die Berücksichtigung von Bürgermeinungen, vor allem auf kommunaler Ebene, eine immer größere Rolle. Eine Auswertung ohne moderne Informationstechnik ist dabei kaum mehr möglich. Damit aber aus diesen Daten tatsächlich die relevanten Informationen extrahiert werden, bedarf es Informationsvisualisierung und Visual Analytics Systeme die sehr detaillierte, aber dennoch einfache und schnelle Analysen für den Menschen erlauben. Dies stellt aber sehr hohe Anforderungen an die visuellen Systeme, da sie gleichzeitig auch den Nutzer und dessen Fähigkeiten berücksichtigen müssen.},
keywords = {eGovernance, Information visualization, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
Politische und gesellschaftliche Prozesse werden durch Informationen sehr stark geprägt, wie auch die jüngsten Ereignisse aufzeigen. Diese Informationen können, trotz enormer Fortschritte, nicht immer aus den sehr großen, heterogenen und verteilten Daten entnommen werden. „Big Data“ stellt somit auch in der öffentlichen Verwaltung eine immer größere Herausforderung dar. Sowohl durch eine umfangreiche Erhebung von Statistiken, als auch durch Dokumente wie Berichte und Studien, wachsen in Behörden die zu bewältigenden Informationsaufgaben. Darüber hinaus spielt die Berücksichtigung von Bürgermeinungen, vor allem auf kommunaler Ebene, eine immer größere Rolle. Eine Auswertung ohne moderne Informationstechnik ist dabei kaum mehr möglich. Damit aber aus diesen Daten tatsächlich die relevanten Informationen extrahiert werden, bedarf es Informationsvisualisierung und Visual Analytics Systeme die sehr detaillierte, aber dennoch einfache und schnelle Analysen für den Menschen erlauben. Dies stellt aber sehr hohe Anforderungen an die visuellen Systeme, da sie gleichzeitig auch den Nutzer und dessen Fähigkeiten berücksichtigen müssen. |
| 28. | Kawa Nazemi; Dirk Burkhardt; Arjan Kuijper Analyzing the Information Search Behavior and Intentions in Visual Information Systems Journal Article In: Journal of Computer Science Technology Updates, vol. 4, 2017. @article{Nazemi2017,
title = {Analyzing the Information Search Behavior and Intentions in Visual Information Systems},
author = {Kawa Nazemi and Dirk Burkhardt and Arjan Kuijper},
url = {https://www.cosmosscholars.com/images/JCSTU/JCSTU-V4N2A2-Nazemi.pdf, full text},
doi = {10.15379/2410-2938.2017.04.02.02},
year = {2017},
date = {2017-01-01},
journal = {Journal of Computer Science Technology Updates},
volume = {4},
abstract = {Visual information search systems support different search approaches such as targeted, exploratory or analytical search. Those visual systems deal with the challenge of composing optimal initial result visualization sets that face the search intention and respond to the search behavior of users. The diversity of these kinds of search tasks require different sets of visual layouts and functionalities, e.g. to filter, thrill-down or even analyze concrete data properties. This paper describes a new approach to calculate the probability towards the three mentioned search intentions, derived from users’ behavior. The implementation is realized as a web-service, which is included in a visual environment that is designed to enable various search strategies based on heterogeneous data sources. In fact, based on an entered search query our developed search intention analysis web-service calculates the most probable search task, and our visualization system initially shows the optimal result set of visualizations to solve the task. The main contribution of this paper is a probability-based approach to derive the users’ search intentions based on the search behavior enhanced by the application to a visual system.},
keywords = {Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics},
pubstate = {published},
tppubtype = {article}
}
Visual information search systems support different search approaches such as targeted, exploratory or analytical search. Those visual systems deal with the challenge of composing optimal initial result visualization sets that face the search intention and respond to the search behavior of users. The diversity of these kinds of search tasks require different sets of visual layouts and functionalities, e.g. to filter, thrill-down or even analyze concrete data properties. This paper describes a new approach to calculate the probability towards the three mentioned search intentions, derived from users’ behavior. The implementation is realized as a web-service, which is included in a visual environment that is designed to enable various search strategies based on heterogeneous data sources. In fact, based on an entered search query our developed search intention analysis web-service calculates the most probable search task, and our visualization system initially shows the optimal result set of visualizations to solve the task. The main contribution of this paper is a probability-based approach to derive the users’ search intentions based on the search behavior enhanced by the application to a visual system. |
2016 |
| 27. | Kawa Nazemi Adaptive Semantics Visualization Book Springer International Publishing, Studies in Computational Intelligence 646, 2016, ISBN: 978-3-319-30815-9. @book{C35-P-25155,
title = {Adaptive Semantics Visualization},
author = {Kawa Nazemi},
url = {http://www.springer.com/de/book/9783319308159},
doi = {10.1007/978-3-319-30816-6},
isbn = {978-3-319-30815-9},
year = {2016},
date = {2016-12-01},
publisher = {Springer International Publishing, Studies in Computational Intelligence 646},
series = {Studies in Computational Intelligence 646},
abstract = {This book introduces a novel approach for intelligent visualizations that adapts the different visual variables and data processing to human's behavior and given tasks. Thereby a number of new algorithms and methods are introduced to satisfy the human need of information and knowledge and enable a usable and attractive way of information acquisition. Each method and algorithm is illustrated in a replicable way to enable the reproduction of the entire "SemaVis" system or parts of it. The introduced evaluation is scientifically well-designed and performed with more than enough participants to validate the benefits of the methods. Beside the introduced new approaches and algorithms, readers may find a sophisticated literature review in Information Visualization and Visual Analytics, Semantics and information extraction, and intelligent and adaptive systems. This book is based on an awarded and distinguished doctoral thesis in computer science.},
keywords = {Adaptive Visualization, Human Factors, Information visualization, Intelligent Systems, Visual analytics},
pubstate = {published},
tppubtype = {book}
}
This book introduces a novel approach for intelligent visualizations that adapts the different visual variables and data processing to human's behavior and given tasks. Thereby a number of new algorithms and methods are introduced to satisfy the human need of information and knowledge and enable a usable and attractive way of information acquisition. Each method and algorithm is illustrated in a replicable way to enable the reproduction of the entire "SemaVis" system or parts of it. The introduced evaluation is scientifically well-designed and performed with more than enough participants to validate the benefits of the methods. Beside the introduced new approaches and algorithms, readers may find a sophisticated literature review in Information Visualization and Visual Analytics, Semantics and information extraction, and intelligent and adaptive systems. This book is based on an awarded and distinguished doctoral thesis in computer science. |
| 26. | Kawa Nazemi; Martin Steiger; Dirk Burkhardt; Jörn Kohlhammer Information Visualization and Policy Modeling Book Chapter In: Big Data: Concepts, Methodologies, Tools, and Applications, Information Science Reference, IGI Global, Hershey PA, USA, 2016, ISBN: 978-1-466-69840-6, (reprint). @inbook{Nazemi2016,
title = {Information Visualization and Policy Modeling},
author = {Kawa Nazemi and Martin Steiger and Dirk Burkhardt and Jörn Kohlhammer},
url = {https://www.igi-global.com/chapter/information-visualization-and-policy-modeling/150163, IGI Global},
doi = {10.4018/978-1-4666-9840-6.ch008},
isbn = {978-1-466-69840-6},
year = {2016},
date = {2016-01-01},
booktitle = {Big Data: Concepts, Methodologies, Tools, and Applications},
publisher = {Information Science Reference, IGI Global},
address = {Hershey PA, USA},
institution = {Information Resources Management Association USA},
organization = {Information Resources Management Association USA},
abstract = {Policy design requires the investigation of various data in several design steps for making the right decisions, validating, or monitoring the political environment. The increasing amount of data is challenging for the stakeholders in this domain. One promising way to access the “big data” is by abstracted visual patterns and pictures, as proposed by information visualization. This chapter introduces the main idea of information visualization in policy modeling. First abstracted steps of policy design are introduced that enable the identification of information visualization in the entire policy life-cycle. Thereafter, the foundations of information visualization are introduced based on an established reference model. The authors aim to amplify the incorporation of information visualization in the entire policy design process. Therefore, the aspects of data and human interaction are introduced, too. The foundation leads to description of a conceptual design for social data visualization, and the aspect of semantics plays an important role.},
note = {reprint},
keywords = {Human-centered user interfaces, Information visualization, Semantic data modeling, Semantic visualization, User-centered design, Visual analytics},
pubstate = {published},
tppubtype = {inbook}
}
Policy design requires the investigation of various data in several design steps for making the right decisions, validating, or monitoring the political environment. The increasing amount of data is challenging for the stakeholders in this domain. One promising way to access the “big data” is by abstracted visual patterns and pictures, as proposed by information visualization. This chapter introduces the main idea of information visualization in policy modeling. First abstracted steps of policy design are introduced that enable the identification of information visualization in the entire policy life-cycle. Thereafter, the foundations of information visualization are introduced based on an established reference model. The authors aim to amplify the incorporation of information visualization in the entire policy design process. Therefore, the aspects of data and human interaction are introduced, too. The foundation leads to description of a conceptual design for social data visualization, and the aspect of semantics plays an important role. |
2015 |
| 25. | Kawa Nazemi; Reimond Retz; Dirk Burkhardt; Arjan Kuijper; Jörn Kohlhammer; Dieter W. Fellner Visual Trend Analysis with Digital Libraries Inproceedings In: Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business., pp. 14:1–14:8, ACM, Graz, Austria, 2015, ISBN: 978-1-4503-3721-2. @inproceedings{Nazemi2015b,
title = {Visual Trend Analysis with Digital Libraries},
author = {Kawa Nazemi and Reimond Retz and Dirk Burkhardt and Arjan Kuijper and Jörn Kohlhammer and Dieter W. Fellner},
url = {http://doi.acm.org/10.1145/2809563.2809569},
doi = {10.1145/2809563.2809569},
isbn = {978-1-4503-3721-2},
year = {2015},
date = {2015-10-01},
booktitle = {Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business.},
pages = {14:1--14:8},
publisher = {ACM},
address = {Graz, Austria},
series = {i-KNOW '15},
abstract = {The early awareness of new technologies and upcoming trends is essential for making strategic decisions in enterprises and research. Trends may signal that technologies or related topics might be of great interest in the future or obsolete for future directions. The identification of such trends premises analytical skills that can be supported through trend mining and visual analytics. Thus the earliest trends or signals commonly appear in science, the investigation of digital libraries in this context is inevitable. However, digital libraries do not provide sufficient information for analyzing trends. It is necessary to integrate data, extract information from the integrated data and provide effective interactive visual analysis tools. We introduce in this paper a model that investigates all stages from data integration to interactive visualization for identifying trends and analyzing the market situation through our visual trend analysis environment. Our approach improves the visual analysis of trends by investigating the entire transformation steps from raw and structured data to visual representations.},
keywords = {Data Analytics, datamining, Information extraction, Information visualization, Trend analysis, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
The early awareness of new technologies and upcoming trends is essential for making strategic decisions in enterprises and research. Trends may signal that technologies or related topics might be of great interest in the future or obsolete for future directions. The identification of such trends premises analytical skills that can be supported through trend mining and visual analytics. Thus the earliest trends or signals commonly appear in science, the investigation of digital libraries in this context is inevitable. However, digital libraries do not provide sufficient information for analyzing trends. It is necessary to integrate data, extract information from the integrated data and provide effective interactive visual analysis tools. We introduce in this paper a model that investigates all stages from data integration to interactive visualization for identifying trends and analyzing the market situation through our visual trend analysis environment. Our approach improves the visual analysis of trends by investigating the entire transformation steps from raw and structured data to visual representations. |
| 24. | Kawa Nazemi; Dirk Burkhardt; David Hoppe; Mariam Nazemi; Jörn Kohlhammer Web-based Evaluation of Information Visualization Journal Article In: Procedia Manufacturing, vol. 3, pp. 5527 - 5534, 2015, ISSN: 2351-9789, (6th International Conference on Applied Human Factors and Ergonomics (AHFE 2015) and the Affiliated Conferences, AHFE 2015). @article{Nazemi2015d,
title = {Web-based Evaluation of Information Visualization},
author = {Kawa Nazemi and Dirk Burkhardt and David Hoppe and Mariam Nazemi and Jörn Kohlhammer},
url = {http://www.sciencedirect.com/science/article/pii/S2351978915007192, Elsevier Science Direct https://www.sciencedirect.com/science/article/pii/S2351978915007192/pdf?md5=ee6ef6cc5f2f761a33314ffc3ee12445&pid=1-s2.0-S2351978915007192-main.pdf, full text},
doi = {https://doi.org/10.1016/j.promfg.2015.07.718},
issn = {2351-9789},
year = {2015},
date = {2015-03-01},
journal = {Procedia Manufacturing},
volume = {3},
pages = {5527 - 5534},
abstract = {Information visualization is strongly related to human perception, human behavior, and in particular human interaction. It is a discipline that focuses on human to enable him gathering insights, knowledge, and solving various and heterogeneous tasks. The human-centered characteristic of information visualization requires valid and proper user studies that improve the system or validate their benefits. New methods, techniques, or approaches of information visualization are commonly evaluated. However, the evaluation is either time and cost consuming or they are made minimum resources that leads to results, which may not be valid. In particular the number of participants is commonly restricted and does not enable a valid assumption about the results. Thus performance measures plays a key role in information visualization, existing web-survey tools are not convenient. We introduce in this paper a new method that enables web-based evaluations of information visualization systems. Our main contribution is the enhancement of web-based survey tools with performance measures. Our approach enables the measurement of task-completion time, correctness of solved tasks, and includes a number of pre- and post-questionnaires.},
note = {6th International Conference on Applied Human Factors and Ergonomics (AHFE 2015) and the Affiliated Conferences, AHFE 2015},
keywords = {Evaluation Methods, Evaluation Tools, Human Perception, Information visualization, User Study, User-centered design, Web-based Evaluation},
pubstate = {published},
tppubtype = {article}
}
Information visualization is strongly related to human perception, human behavior, and in particular human interaction. It is a discipline that focuses on human to enable him gathering insights, knowledge, and solving various and heterogeneous tasks. The human-centered characteristic of information visualization requires valid and proper user studies that improve the system or validate their benefits. New methods, techniques, or approaches of information visualization are commonly evaluated. However, the evaluation is either time and cost consuming or they are made minimum resources that leads to results, which may not be valid. In particular the number of participants is commonly restricted and does not enable a valid assumption about the results. Thus performance measures plays a key role in information visualization, existing web-survey tools are not convenient. We introduce in this paper a new method that enables web-based evaluations of information visualization systems. Our main contribution is the enhancement of web-based survey tools with performance measures. Our approach enables the measurement of task-completion time, correctness of solved tasks, and includes a number of pre- and post-questionnaires. |
2014 |
| 23. | Kawa Nazemi Adaptive Semantics Visualization PhD Thesis Technische Universität Darmstadt, 2014, (Reprint by Eugraphics Association (EG)). @phdthesis{Nazemi2014f,
title = {Adaptive Semantics Visualization},
author = {Kawa Nazemi},
url = {https://diglib.eg.org/handle/10.2312/12076, EG Lib
https://diglib.eg.org/bitstream/handle/10.2312/12076/nazemi.pdf, full text},
doi = {10.2312/12076},
year = {2014},
date = {2014-11-27},
school = {Technische Universität Darmstadt},
abstract = {Human access to the increasing amount of information and data plays an essential role for the professional level and also for everyday life. While information visualization has developed new and remarkable ways for visualizing data and enabling the exploration process, adaptive systems focus on users' behavior to tailor information for supporting the information acquisition process. Recent research on adaptive visualization shows promising ways of synthesizing these two complementary approaches and make use of the surpluses of both disciplines. The emerged methods and systems aim to increase the performance, acceptance, and user experience of graphical data representations for a broad range of users. Although the evaluation results of the recently proposed systems are promising, some important aspects of information visualization are not considered in the adaptation process. The visual adaptation is commonly limited to change either visual parameters or replace visualizations entirely. Further, no existing approach adapts the visualization based on data and user characteristics. Other limitations of existing approaches include the fact that the visualizations require training by experts in the field.
In this thesis, we introduce a novel model for adaptive visualization. In contrast to existing approaches, we have focused our investigation on the potentials of information visualization for adaptation. Our reference model for visual adaptation not only considers the entire transformation, from data to visual representation, but also enhances it to meet the requirements for visual adaptation. Our model adapts different visual layers that were identified based on various models and studies on human visual perception and information processing. In its adaptation process, our conceptual model considers the impact of both data and user on visualization adaptation. We investigate different approaches and models and their effects on system adaptation to gather implicit information about users and their behavior. These are than transformed and applied to affect the visual representation and model human interaction behavior with visualizations and data to achieve a more appropriate visual adaptation. Our enhanced user model further makes use of the semantic hierarchy to enable a domain-independent adaptation.
To face the problem of a system that requires to be trained by experts, we introduce the canonical user model that models the average usage behavior with the visualization environment. Our approach learns from the behavior of the average user to adapt the different visual layers and transformation steps. This approach is further enhanced with similarity and deviation analysis for individual users to determine similar behavior on an individual level and identify differing behavior from the canonical model. Users with similar behavior get similar visualization and data recommendations, while behavioral anomalies lead to a lower level of adaptation. Our model includes a set of various visual layouts that can be used to compose a multi-visualization interface, a sort of "visualization cockpit". This model facilitates various visual layouts to provide different perspectives and enhance the ability to solve difficult and exploratory search challenges. Data from different data-sources can be visualized and compared in a visual manner. These different visual perspectives on data can be chosen by users or can be automatically selected by the system.
This thesis further introduces the implementation of our model that includes additional approaches for an efficient adaptation of visualizations as proof of feasibility. We further conduct a comprehensive user study that aims to prove the benefits of our model and underscore limitations for future work. The user study with overall 53 participants focuses with its four conditions on our enhanced reference model to evaluate the adaptation effects of the different visual layers.},
note = {Reprint by Eugraphics Association (EG)},
keywords = {Adaptive Information Visualization, Adaptive User Interfaces, Adaptive Visualization, Computer Based Learning, Data Analytics, E-Learning, Exploratory learning, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, Ontology visualization, personalization, Policy modeling, reference model, Semantic data modeling, Semantic visualization, Semantic web, Semantics visualization, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics},
pubstate = {published},
tppubtype = {phdthesis}
}
Human access to the increasing amount of information and data plays an essential role for the professional level and also for everyday life. While information visualization has developed new and remarkable ways for visualizing data and enabling the exploration process, adaptive systems focus on users' behavior to tailor information for supporting the information acquisition process. Recent research on adaptive visualization shows promising ways of synthesizing these two complementary approaches and make use of the surpluses of both disciplines. The emerged methods and systems aim to increase the performance, acceptance, and user experience of graphical data representations for a broad range of users. Although the evaluation results of the recently proposed systems are promising, some important aspects of information visualization are not considered in the adaptation process. The visual adaptation is commonly limited to change either visual parameters or replace visualizations entirely. Further, no existing approach adapts the visualization based on data and user characteristics. Other limitations of existing approaches include the fact that the visualizations require training by experts in the field.
In this thesis, we introduce a novel model for adaptive visualization. In contrast to existing approaches, we have focused our investigation on the potentials of information visualization for adaptation. Our reference model for visual adaptation not only considers the entire transformation, from data to visual representation, but also enhances it to meet the requirements for visual adaptation. Our model adapts different visual layers that were identified based on various models and studies on human visual perception and information processing. In its adaptation process, our conceptual model considers the impact of both data and user on visualization adaptation. We investigate different approaches and models and their effects on system adaptation to gather implicit information about users and their behavior. These are than transformed and applied to affect the visual representation and model human interaction behavior with visualizations and data to achieve a more appropriate visual adaptation. Our enhanced user model further makes use of the semantic hierarchy to enable a domain-independent adaptation.
To face the problem of a system that requires to be trained by experts, we introduce the canonical user model that models the average usage behavior with the visualization environment. Our approach learns from the behavior of the average user to adapt the different visual layers and transformation steps. This approach is further enhanced with similarity and deviation analysis for individual users to determine similar behavior on an individual level and identify differing behavior from the canonical model. Users with similar behavior get similar visualization and data recommendations, while behavioral anomalies lead to a lower level of adaptation. Our model includes a set of various visual layouts that can be used to compose a multi-visualization interface, a sort of "visualization cockpit". This model facilitates various visual layouts to provide different perspectives and enhance the ability to solve difficult and exploratory search challenges. Data from different data-sources can be visualized and compared in a visual manner. These different visual perspectives on data can be chosen by users or can be automatically selected by the system.
This thesis further introduces the implementation of our model that includes additional approaches for an efficient adaptation of visualizations as proof of feasibility. We further conduct a comprehensive user study that aims to prove the benefits of our model and underscore limitations for future work. The user study with overall 53 participants focuses with its four conditions on our enhanced reference model to evaluate the adaptation effects of the different visual layers. |
| 22. | Kawa Nazemi Adaptive Semantics Visualization PhD Thesis Technische Universität Darmstadt, 2014, (Department of Computer Science. Supervised by Dieter W. Fellner.). @phdthesis{Nazemi2014g,
title = {Adaptive Semantics Visualization},
author = {Kawa Nazemi},
url = {http://tuprints.ulb.tu-darmstadt.de/id/eprint/4319, TU Darmstadt Prints
http://tuprints.ulb.tu-darmstadt.de/4319/1/Nazemi_Diss.pdf, full text},
year = {2014},
date = {2014-11-23},
address = {Darmstadt, Germany},
school = {Technische Universität Darmstadt},
abstract = {Human access to the increasing amount of information and data plays an essential role for the professional level and also for everyday life. While information visualization has developed new and remarkable ways for visualizing data and enabling the exploration process, adaptive systems focus on users’ behavior to tailor information for supporting the information acquisition process. Recent research on adaptive visualization shows promising ways of synthesizing these two complementary approaches and make use of the surpluses of both disciplines. The emerged methods and systems aim to increase the performance, acceptance, and user experience of graphical data representations for a broad range of users. Although the evaluation results of the recently proposed systems are promising, some important aspects of information visualization are not considered in the adaptation process. The visual adaptation is commonly limited to change either visual parameters or replace visualizations entirely. Further, no existing approach adapts the visualization based on data and user characteristics. Other limitations of existing approaches include the fact that the visualizations require training by experts in the field.
In this thesis, we introduce a novel model for adaptive visualization. In contrast to existing approaches, we have focused our investigation on the potentials of information visualization for adaptation. Our reference model for visual adaptation not only considers the entire transformation, from data to visual representation, but also enhances it to meet the requirements for visual adaptation. Our model adapts different visual layers that were identified based on various models and studies on human visual perception and information processing. In its adaptation process, our conceptual model considers the impact of both data and user on visualization adaptation. We investigate different approaches and models and their effects on system adaptation to gather implicit information about users and their behavior. These are than transformed and applied to affect the visual representation and model human interaction behavior with visualizations and data to achieve a more appropriate visual adaptation. Our enhanced user model further makes use of the semantic hierarchy to enable a domain-independent adaptation.
To face the problem of a system that requires to be trained by experts, we introduce the canonical user model that models the average usage behavior with the visualization environment. Our approach learns from the behavior of the average user to adapt the different visual layers and transformation steps. This approach is further enhanced with similarity and deviation analysis for individual users to determine similar behavior on an individual level and identify differing behavior from the canonical model. Users with similar behavior get similar visualization and data recommendations, while behavioral anomalies lead to a lower level of adaptation. Our model includes a set of various visual layouts that can be used to compose a multi-visualization interface, a sort of "‘visualization cockpit"’. This model facilitates various visual layouts to provide different perspectives and enhance the ability to solve difficult and exploratory search challenges. Data from different data-sources can be visualized and compared in a visual manner. These different visual perspectives on data can be chosen by users or can be automatically selected by the system.
This thesis further introduces the implementation of our model that includes additional approaches for an efficient adaptation of visualizations as proof of feasibility. We further conduct a comprehensive user study that aims to prove the benefits of our model and underscore limitations for future work. The user study with overall 53 participants focuses with its four conditions on our enhanced reference model to evaluate the adaptation effects of the different visual layers.},
note = {Department of Computer Science. Supervised by Dieter W. Fellner.},
keywords = {Adaptive Information Visualization, Adaptive User Interfaces, Computer Based Learning, Data Analytics, eGovernance, Exploratory learning, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction Design, Ontology visualization, personalization, Policy modeling, Semantic data modeling, Semantic visualization, Semantic web, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics},
pubstate = {published},
tppubtype = {phdthesis}
}
Human access to the increasing amount of information and data plays an essential role for the professional level and also for everyday life. While information visualization has developed new and remarkable ways for visualizing data and enabling the exploration process, adaptive systems focus on users’ behavior to tailor information for supporting the information acquisition process. Recent research on adaptive visualization shows promising ways of synthesizing these two complementary approaches and make use of the surpluses of both disciplines. The emerged methods and systems aim to increase the performance, acceptance, and user experience of graphical data representations for a broad range of users. Although the evaluation results of the recently proposed systems are promising, some important aspects of information visualization are not considered in the adaptation process. The visual adaptation is commonly limited to change either visual parameters or replace visualizations entirely. Further, no existing approach adapts the visualization based on data and user characteristics. Other limitations of existing approaches include the fact that the visualizations require training by experts in the field.
In this thesis, we introduce a novel model for adaptive visualization. In contrast to existing approaches, we have focused our investigation on the potentials of information visualization for adaptation. Our reference model for visual adaptation not only considers the entire transformation, from data to visual representation, but also enhances it to meet the requirements for visual adaptation. Our model adapts different visual layers that were identified based on various models and studies on human visual perception and information processing. In its adaptation process, our conceptual model considers the impact of both data and user on visualization adaptation. We investigate different approaches and models and their effects on system adaptation to gather implicit information about users and their behavior. These are than transformed and applied to affect the visual representation and model human interaction behavior with visualizations and data to achieve a more appropriate visual adaptation. Our enhanced user model further makes use of the semantic hierarchy to enable a domain-independent adaptation.
To face the problem of a system that requires to be trained by experts, we introduce the canonical user model that models the average usage behavior with the visualization environment. Our approach learns from the behavior of the average user to adapt the different visual layers and transformation steps. This approach is further enhanced with similarity and deviation analysis for individual users to determine similar behavior on an individual level and identify differing behavior from the canonical model. Users with similar behavior get similar visualization and data recommendations, while behavioral anomalies lead to a lower level of adaptation. Our model includes a set of various visual layouts that can be used to compose a multi-visualization interface, a sort of "‘visualization cockpit"’. This model facilitates various visual layouts to provide different perspectives and enhance the ability to solve difficult and exploratory search challenges. Data from different data-sources can be visualized and compared in a visual manner. These different visual perspectives on data can be chosen by users or can be automatically selected by the system.
This thesis further introduces the implementation of our model that includes additional approaches for an efficient adaptation of visualizations as proof of feasibility. We further conduct a comprehensive user study that aims to prove the benefits of our model and underscore limitations for future work. The user study with overall 53 participants focuses with its four conditions on our enhanced reference model to evaluate the adaptation effects of the different visual layers. |
| 21. | Peter Sonntagbauer; Kawa Nazemi; Susanne Sonntagbauer; Giorgio Prister; Dirk Burkhardt (Ed.) Handbook of Research on Advanced ICT Integration for Governance and Policy Modeling Book Business Science Reference (IGI Global), Hershey PA, USA, Hershey PA, USA, 2014, ISBN: 978-1-466-66236-0. @book{Sonntagbauer2014,
title = {Handbook of Research on Advanced ICT Integration for Governance and Policy Modeling},
editor = {Peter Sonntagbauer and Kawa Nazemi and Susanne Sonntagbauer and Giorgio Prister and Dirk Burkhardt},
url = {https://www.igi-global.com/book/handbook-research-advanced-ict-integration/102238, link to publisher},
doi = {10.4018/978-1-4666-6236-0},
isbn = {978-1-466-66236-0},
year = {2014},
date = {2014-06-01},
pages = {508},
publisher = {Business Science Reference (IGI Global), Hershey PA, USA},
address = {Hershey PA, USA},
series = {Handbook of Research},
abstract = {As governments and policy makers take advantage of information and communication technologies, leaders must understand how to navigate the ever-shifting landscape of modern technologies in order to be most effective in enacting change and leading their constituents.
The Handbook of Research on Advanced ICT Integration for Governance and Policy Modeling builds on the available literature, research, and recent advances in e-governance to explore advanced methods and applications of digital tools in government. This collection of the latest research in the field presents an essential reference for academics, researchers, and advanced-level students, as well as government leaders, policy makers, and experts in international relations.
Reviews and Testimonials
Sonntagbauer, Nazemi, Sonntagbauer, Prister, and Burhardt present an essential reference text for advanced students, academics, government leaders, policy makers, experts, and researchers in the field of international relations on the subject of e-governance and the advanced methods and applications of digital tools in government. Utilizing the latest available literature and research into recent advances in the field of e-governance, the text explores citizen engagement, civil service, decision-making strategies, e-participation modeling and a variety of other subjects as they pertain to the overall topic.},
keywords = {Artificial Intelligence, eGovernance, Information visualization, Interaction Design, Machine Leanring, Policy modeling},
pubstate = {published},
tppubtype = {book}
}
As governments and policy makers take advantage of information and communication technologies, leaders must understand how to navigate the ever-shifting landscape of modern technologies in order to be most effective in enacting change and leading their constituents.
The Handbook of Research on Advanced ICT Integration for Governance and Policy Modeling builds on the available literature, research, and recent advances in e-governance to explore advanced methods and applications of digital tools in government. This collection of the latest research in the field presents an essential reference for academics, researchers, and advanced-level students, as well as government leaders, policy makers, and experts in international relations.
Reviews and Testimonials
Sonntagbauer, Nazemi, Sonntagbauer, Prister, and Burhardt present an essential reference text for advanced students, academics, government leaders, policy makers, experts, and researchers in the field of international relations on the subject of e-governance and the advanced methods and applications of digital tools in government. Utilizing the latest available literature and research into recent advances in the field of e-governance, the text explores citizen engagement, civil service, decision-making strategies, e-participation modeling and a variety of other subjects as they pertain to the overall topic. |
| 20. | Dirk Burkhardt; Kawa Nazemi; Jörn Kohlhammer Visual Process Support to Assist Users in Policy Making Book Chapter In: Peter Sonntagbauer; Kawa Nazemi; Susanne Sonntagbauer; Giorgio Prister; Dirk Burkhardt (Ed.): Handbook of Research on Advanced ICT Integration for Governance and Policy Modeling, pp. 149–162, IGI Global, 2014, ISBN: 978-1-466-66236-0. @inbook{burkhardt2014visual,
title = {Visual Process Support to Assist Users in Policy Making},
author = {Dirk Burkhardt and Kawa Nazemi and Jörn Kohlhammer},
editor = {Peter Sonntagbauer and Kawa Nazemi and Susanne Sonntagbauer and Giorgio Prister and Dirk Burkhardt},
url = {https://www.igi-global.com/chapter/visual-process-support-to-assist-users-in-policy-making/116661, IGI Global},
doi = {10.4018/978-1-4666-6236-0.ch009},
isbn = {978-1-466-66236-0},
year = {2014},
date = {2014-06-01},
booktitle = {Handbook of Research on Advanced ICT Integration for Governance and Policy Modeling},
journal = {Handbook of Research on Advanced ICT Integration for Governance and Policy Modeling},
pages = {149--162},
publisher = {IGI Global},
series = {Handbook of Research},
crossref = {Sonntagbauer2014},
abstract = {The policy making process requires the involvement of various stakeholders, who bring in very heterogeneous experiences and skills concerning the policymaking domain, as well as experiences of ICT solutions. Current solutions are primarily designed to provide “one-solution-fits-all” answers, which in most cases fail the needs of all stakeholders. In this chapter, the authors introduce a new approach to assist users based on their tasks. Therefore, the system observes the interaction of the user and recognizes the current phase of the policymaking process and the profile of the user to assist him more sufficiently in solving his task. For this purpose, the system automatically enables or disables supporting features such as visualization, tools, and supporting techniques.},
keywords = {Information visualization, Interaction analysis, Process Support, Semantic visualization, Visual analytics},
pubstate = {published},
tppubtype = {inbook}
}
The policy making process requires the involvement of various stakeholders, who bring in very heterogeneous experiences and skills concerning the policymaking domain, as well as experiences of ICT solutions. Current solutions are primarily designed to provide “one-solution-fits-all” answers, which in most cases fail the needs of all stakeholders. In this chapter, the authors introduce a new approach to assist users based on their tasks. Therefore, the system observes the interaction of the user and recognizes the current phase of the policymaking process and the profile of the user to assist him more sufficiently in solving his task. For this purpose, the system automatically enables or disables supporting features such as visualization, tools, and supporting techniques. |
| 19. | Kawa Nazemi; Martin Steiger; Dirk Burkhardt; Jörn Kohlhammer Information Visualization and Policy Modeling Book Chapter In: Peter Sonntagbauer; Kawa Nazemi; Susanne Sonntagbauer; Giorgio Prister; Dirk Burkhardt (Ed.): Handbook of Research on Advanced ICT Integration for Governance and Policy Modeling, pp. 175–215, Business Science Reference (IGI Global), Hershey PA, USA, Hershey PA, USA, 2014, ISBN: 978-1-466-66236-0. @inbook{nazemi2014information,
title = {Information Visualization and Policy Modeling},
author = {Kawa Nazemi and Martin Steiger and Dirk Burkhardt and Jörn Kohlhammer},
editor = {Peter Sonntagbauer and Kawa Nazemi and Susanne Sonntagbauer and Giorgio Prister and Dirk Burkhardt},
url = {https://www.igi-global.com/chapter/information-visualization-and-policy-modeling/116664, IGI Global},
doi = {10.4018/978-1-4666-6236-0.ch011},
isbn = {978-1-466-66236-0},
year = {2014},
date = {2014-06-01},
booktitle = {Handbook of Research on Advanced ICT Integration for Governance and Policy Modeling},
journal = {Handbook of Research on Advanced ICT Integration for Governance and Policy Modeling},
pages = {175--215},
publisher = {Business Science Reference (IGI Global), Hershey PA, USA},
address = {Hershey PA, USA},
series = {Handbook of Research},
crossref = {Sonntagbauer2014},
abstract = {Policy design requires the investigation of various data in several design steps for making the right decisions, validating, or monitoring the political environment. The increasing amount of data is challenging for the stakeholders in this domain. One promising way to access the “big data” is by abstracted visual patterns and pictures, as proposed by information visualization. This chapter introduces the main idea of information visualization in policy modeling. First abstracted steps of policy design are introduced that enable the identification of information visualization in the entire policy life-cycle. Thereafter, the foundations of information visualization are introduced based on an established reference model. The authors aim to amplify the incorporation of information visualization in the entire policy design process. Therefore, the aspects of data and human interaction are introduced, too. The foundation leads to description of a conceptual design for social data visualization, and the aspect of semantics plays an important role.},
keywords = {eGovernance, Information visualization, Policy modeling, Visual analytics},
pubstate = {published},
tppubtype = {inbook}
}
Policy design requires the investigation of various data in several design steps for making the right decisions, validating, or monitoring the political environment. The increasing amount of data is challenging for the stakeholders in this domain. One promising way to access the “big data” is by abstracted visual patterns and pictures, as proposed by information visualization. This chapter introduces the main idea of information visualization in policy modeling. First abstracted steps of policy design are introduced that enable the identification of information visualization in the entire policy life-cycle. Thereafter, the foundations of information visualization are introduced based on an established reference model. The authors aim to amplify the incorporation of information visualization in the entire policy design process. Therefore, the aspects of data and human interaction are introduced, too. The foundation leads to description of a conceptual design for social data visualization, and the aspect of semantics plays an important role. |
| 18. | Kawa Nazemi; Dirk Burkhardt; Reimond Retz; Arjan Kuijper; Jörn Kohlhammer Adaptive Visualization of Linked-Data Inproceedings In: George Bebis; Richard Boyle; Bahram Parvin; Darko Koracin; Ryan McMahan; Jason Jerald; Hui Zhang; Steven M Drucker; Chandra Kambhamettu; Maha El Choubassi; Zhigang Deng; Mark Carlson (Ed.): Proceedings of International Symposium on Visual Computing (ISVC 2014). Advances in Visual Computing., pp. 872–883, Springer International Publishing, Cham, 2014, ISBN: 978-3-319-14364-4. @inproceedings{Nazemi2014b,
title = {Adaptive Visualization of Linked-Data},
author = {Kawa Nazemi and Dirk Burkhardt and Reimond Retz and Arjan Kuijper and Jörn Kohlhammer},
editor = {George Bebis and Richard Boyle and Bahram Parvin and Darko Koracin and Ryan McMahan and Jason Jerald and Hui Zhang and Steven M Drucker and Chandra Kambhamettu and Maha El Choubassi and Zhigang Deng and Mark Carlson},
url = {https://link.springer.com/chapter/10.1007/978-3-319-14364-4_84, Springer link},
doi = {10.1007/978-3-319-14364-4_84},
isbn = {978-3-319-14364-4},
year = {2014},
date = {2014-03-01},
booktitle = {Proceedings of International Symposium on Visual Computing (ISVC 2014). Advances in Visual Computing.},
pages = {872--883},
publisher = {Springer International Publishing},
address = {Cham},
series = {LNCS 8888},
abstract = {Adaptive visualizations reduces the required cognitive effort to comprehend interactive visual pictures and amplify cognition. Although the research on adaptive visualizations grew in the last years, the existing approaches do not consider the transformation pipeline from data to visual representation for a more efficient and effective adaptation. Further todays systems commonly require an initial training by experts from the field and are limited to adaptation based either on user behavior or on data characteristics. A combination of both is not proposed to our knowledge. This paper introduces an enhanced instantiation of our previously proposed model that combines both: involving different influencing factors for and adapting various levels of visual peculiarities, on content, visual layout, visual presentation, and visual interface. Based on data type and users’ behavior, our system adapts a set of applicable visualization types. Moreover, retinal variables of each visualization type are adapted to meet individual or canonical requirements on both, data types and users’ behavior. Our system does not require an initial expert modeling.},
keywords = {Adaptive Information Visualization, Adaptive User Interfaces, Adaptive Visualization, Data Analytics, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, personalization, reference model, Semantic visualization, Semantic web, User behavior, User modeling, User-centered design, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
Adaptive visualizations reduces the required cognitive effort to comprehend interactive visual pictures and amplify cognition. Although the research on adaptive visualizations grew in the last years, the existing approaches do not consider the transformation pipeline from data to visual representation for a more efficient and effective adaptation. Further todays systems commonly require an initial training by experts from the field and are limited to adaptation based either on user behavior or on data characteristics. A combination of both is not proposed to our knowledge. This paper introduces an enhanced instantiation of our previously proposed model that combines both: involving different influencing factors for and adapting various levels of visual peculiarities, on content, visual layout, visual presentation, and visual interface. Based on data type and users’ behavior, our system adapts a set of applicable visualization types. Moreover, retinal variables of each visualization type are adapted to meet individual or canonical requirements on both, data types and users’ behavior. Our system does not require an initial expert modeling. |
| 17. | Dirk Burkhardt; Kawa Nazemi; Mohsen Parisay; Jörn Kohlhammer Visual Correlation Analysis to Explain Open Government Data based on Linked-Open Data for Decision Making Journal Article In: International Journal of Digital Society, vol. 5, pp. 915–923, 2014, ISSN: 2040-2570. @article{Burkhardt2014b,
title = {Visual Correlation Analysis to Explain Open Government Data based on Linked-Open Data for Decision Making},
author = {Dirk Burkhardt and Kawa Nazemi and Mohsen Parisay and Jörn Kohlhammer},
url = {http://infonomics-society.org/wp-content/uploads/ijds/published-papers/volume-5-2014/Visual-Correlation-Analysis-to-Explain-Open-Government-Data-based-on-Linked-Open-Data-for-.pdf, full text},
issn = {2040-2570},
year = {2014},
date = {2014-01-01},
journal = {International Journal of Digital Society},
volume = {5},
pages = {915--923},
publisher = {Infonomics Society},
institution = {Infonomics Society},
organization = {Infonomics Society},
abstract = {Public authorities normally consider statistical data about indicators in their decision makings. Such valid kind of data allows an objective observation about indicator developments over time. In case of a
significant deviation from the normal indicator level, it is difficult to understand the reasons for upcoming problems. In this article we present an approach that allows an enhanced information gathering through an improved information overview about the depending aspects to such an indicator by considering governmental data-sources that provide also other types of data than just statistics. Even more, our approach integrates a system that allows generating explanations for Open Government Data, especially to specific indicators, based on Linked-Open Data and shows it in graphical form to enable a fast overview gathering. This allows decision-makers to get hints for unexpected reasons of concrete problems that may influence an indicator.},
keywords = {Data Analytics, eGovernance, Human-computer interaction (HCI), Information visualization, Policy modeling, Visual analytics},
pubstate = {published},
tppubtype = {article}
}
Public authorities normally consider statistical data about indicators in their decision makings. Such valid kind of data allows an objective observation about indicator developments over time. In case of a
significant deviation from the normal indicator level, it is difficult to understand the reasons for upcoming problems. In this article we present an approach that allows an enhanced information gathering through an improved information overview about the depending aspects to such an indicator by considering governmental data-sources that provide also other types of data than just statistics. Even more, our approach integrates a system that allows generating explanations for Open Government Data, especially to specific indicators, based on Linked-Open Data and shows it in graphical form to enable a fast overview gathering. This allows decision-makers to get hints for unexpected reasons of concrete problems that may influence an indicator. |
