@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}

}

@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}

}

@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}

}

@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}

}

@inproceedings{7038782,

title = {Visual explanation of government-data for policy making through open-data inclusion},

author = {Dirk Burkhardt and Kawa Nazemi and Wilhelm Retz and Jörn Kohlhammer},

url = {http://ieeexplore.ieee.org/document/7038782/, IEEE Xplore},

doi = {10.1109/ICITST.2014.7038782},

isbn = {978-1-908320-39-1},

year  = {2014},

date = {2014-01-01},

booktitle = {The 9th International Conference for Internet Technology and Secured Transactions (ICITST-2014)},

pages = {83-89},

publisher = {IEEE},

abstract = {Commonly, data used in public authorities are statistical data about certain indicator. 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 our paper 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. This enables decision-makers to get hints for unexpected reasons of concrete problems that may influence an indicator.},

keywords = {eGovernance, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Interaction Design, Policy modeling, Semantic visualization, User-centered design},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nazemi2014g,

title = {Adaptive Visualization of Social Media Data for Policy Modeling},

author = {Kawa Nazemi and Dirk Burkhardt and Wilhelm Retz 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/10.1007/978-3-319-14249-4_32, Springer link},

doi = {10.1007/978-3-319-14249-4_32},

isbn = {978-3-319-14249-4},

year  = {2014},

date = {2014-01-01},

booktitle = {Proceeding of the International Symposium on Visual Computing (ISVC 2014). Advances in Visual Computing.},

pages = {333--344},

publisher = {Springer International Publishing},

address = {Cham},

series = {LNCS 8887},

abstract = {The visual analysis of social media data emerged a huge number of interactive visual representations that use different characteristics of the data to enable the process of information acquisition. The social data are used in the domain of policy modeling to gather information about citizens' demands, opinions, and requirements and help to decide about political policies. Although existing systems already provide a huge number of visual analysis tools, the search and exploration paradigm is not really clear. Furthermore, the systems commonly do not provide any kind of human centered adaptation for the different stakeholders involved in the policy making process. In this paper, we introduce a novel approach that investigates the exploration and search paradigm from two different perspectives and enables a visual adaptation to support the exploration and analysis process.},

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, Semantic visualization, Semantic web, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nazemi2013visual,

title = {Visual Variables in Adaptive Visualizations.},

author = {Kawa Nazemi and Jörn Kohlhammer},

editor = {Shlomo Berkovsky and Eelco Herder and Pasquale Lops and Olga C Santos},

url = {http://ceur-ws.org/Vol-997/wuav2013_paper_06.pdf, full text},

issn = {1613-0073},

year  = {2013},

date = {2013-06-01},

booktitle = {21st Conference on User Modeling, Adaptation, and Personalization. UMAP 2013 Extended Proceedings. Proceeding of 1st International Workshop on User-Adaptive Visualizations.},

publisher = {CEUR Workshop Proceedings},

address = {Rome, Italy,},

series = {Vol. 997},

abstract = {Visualizations provide various variables for the adaptation to the usage context and the users. Today’s adaptive visualizations make use of various visual variables to order or filter information or visualizations. However, the capabilities of visual variables in context of human information processing and tasks are not comprehensively exploited. This paper discusses the value of the different visual variables providing beneficial and more accurately adapted information visualizations.},

keywords = {Adaptive Information Visualization, Adaptive User Interfaces, Adaptive Visualization, Human Factors, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, Semantic visualization},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nazemi2013b,

title = {Adaptive Semantic Visualization for Bibliographic Entries},

author = {Kawa Nazemi and Reimond Retz and Jürgen Bernard and Jörn Kohlhammer and Dieter W. Fellner},

editor = {George Bebis and Richard Boyle and Bahram Parvin and Darko Koracin and Baoxin Li and Fatih Porikli and Victor Zordan and James Klosowski and Sabine Coquillart and Xun Luo and Min Chen and David Gotz},

url = {https://link.springer.com/chapter/10.1007/978-3-642-41939-3_2, Springer link},

doi = {10.1007/978-3-642-41939-3_2},

isbn = {978-3-642-41939-3},

year  = {2013},

date = {2013-01-01},

booktitle = {Proceedings of International Symposium on Visual Computing (ISVC 2013). Advances in Visual Computing.},

pages = {13--24},

publisher = {Springer Berlin Heidelberg},

address = {Berlin, Heidelberg},

series = {LNCS 8034},

abstract = {Adaptive visualizations aim to reduce the complexity of visual representations and convey information using interactive visualizations. Although the research on adaptive visualizations grew in the last years, the existing approaches do not make use of the variety of adaptable visual variables. Further the existing approaches often premises experts, who has to model the initial visualization design. In addition, current approaches either incorporate user behavior or data types. A combination of both is not proposed to our knowledge. This paper introduces the instantiation of our previously proposed model that combines both: involving different influencing factors for and adapting various levels of visual peculiarities, on visual layout and visual presentation in a multiple visualization environment. 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 canonic 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, Semantic visualization, Semantic web, User behavior, User Interactions, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Burkhardt2012,

title = {Dynamic process support based on users' behavior},

author = {Dirk Burkhardt and Kawa Nazemi},

url = {http://ieeexplore.ieee.org/document/6402079/, IEEE Xplore},

doi = {10.1109/ICL.2012.6402079},

isbn = {978-1-4673-2425-0},

year  = {2012},

date = {2012-09-01},

booktitle = {15th International Conference on Interactive Collaborative Learning (ICL)},

pages = {1-6},

abstract = {Nowadays there is a gap between the possibilities and the massively existing data on the one side and the user as main worker on the other side. In different scenarios e.g. search, exploration, analysis and policy-modeling a user has to deal with massive information, but for this work he usually gets a static designed system. So meanwhile data-driven work-processes are increasing in its complexity the support of the users who are working with these data is limited on basic features. Hence this paper describes a concept for a process-supporting approach, which includes relevant aspects of users' behaviors in support him to successfully finish also complex tasks. This will be achieved by a process-based guidance with an automatic tools selection for every process and activity on the one hand. And on the other hand the consideration of expert-level of a user to a single task and process. This expert-level will be classified during each task and process interaction and allow the automatically selection of optimal tools for a concrete task. In final the user gets for every task an automatically initialized user-interface with useful and required tools.},

keywords = {Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Interaction Design, Process Support, User-centered design},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nazemi2011c,

title = {Adapting User Interfaces by Analyzing Data Characteristics for Determining Adequate Visualizations},

author = {Kawa Nazemi and Dirk Burkhardt and Alexander Praetorius and Matthias Breyer and Arjan Kuijper},

editor = {Masaaki Kurosu},

url = {https://doi.org/10.1007/978-3-642-21753-1_63, DOI 

https://link.springer.com/chapter/10.1007/978-3-642-21753-1_63, Springer page},

doi = {10.1007/978-3-642-21753-1_63},

isbn = {978-3-642-21753-1},

year  = {2011},

date = {2011-01-01},

booktitle = {Human Centered Design},

pages = {566--575},

publisher = {Springer Berlin Heidelberg},

address = {Berlin, Heidelberg},

abstract = {Today the information visualization takes in an important position, because it is required in nearly every context where large databases have to be visualized. For this challenge new approaches are needed to allow the user an adequate access to these data. Static visualizations are only able to show the data without any support to the users, which is the reason for the accomplished researches to adaptive user-interfaces, in particular for adaptive visualizations. By these approaches the visualizations were adapted to the users' behavior, so that graphical primitives were change to support a user e.g. by highlighting user-specific entities, which seems relevant for a user. This approach is commonly used, but it is limited on changes for just a single visualization. Modern heterogeneous data providing different kinds of aspects, which modern visualizations try to regard, but therefore a user often needs more than a single visualization for making an information retrieval. In this paper we describe a concept for adapting the user-interface by selecting visualizations in dependence to automatically generated data characteristics. So visualizations will be chosen, which are fitting well to the generated characteristics. Finally the user gets an aquatically arranged set of visualizations as initial point of his interaction through the data.},

keywords = {Adaptive Information Visualization, Adaptive User Interfaces, Adaptive Visualization, Data Analytics, Human Factors, Human-computer interaction (HCI), Information visualization, Intelligent Systems, personalization, reference model, Semantic visualization, Semantic web, User behavior},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-642-21753-1_55,

title = {SemaZoom: Semantics Exploration by Using a Layer-Based Focus and Context Metaphor},

author = {Dirk Burkhardt and Kawa Nazemi and Matthias Breyer and Christian Stab and Arjan Kuijper},

editor = {Masaaki Kurosu},

url = {https://doi.org/10.1007/978-3-642-21753-1_55, DOI 

https://link.springer.com/chapter/10.1007/978-3-642-21753-1_55, Springer page},

doi = {10.1007/978-3-642-21753-1_55},

isbn = {978-3-642-21753-1},

year  = {2011},

date = {2011-01-01},

booktitle = {Human Centered Design},

pages = {491--499},

publisher = {Springer Berlin Heidelberg},

address = {Berlin, Heidelberg},

series = {LNCS 6776},

abstract = {The Semantic Web is a powerful technology for organizing the data in our information based society. The collection and organization of information is an important step for showing important information to interested people. But the usage of such semantic-based data sources depends on effective and efficient information visualizations. Currently different kinds of visualizations in general and visualization metaphors do exist. Many of them are also applied for semantic data source, but often they are designed for semantic web experts and neglecting the normal user and his perception of an easy useable visualization. This kind of user needs less information, but rather a reduced qualitative view on the data. These two aspects of large amount of existing data and one for normal users easy to understand visualization is often not reconcilable. In this paper we create a concept for a visualization to show a bigger set of information to such normal users without overstraining them, because of layer-based data visualization, next to an integration of a Focus and Context metaphor.},

keywords = {Graph visualization, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, User behavior, User Interactions, User Interface, User interfaces, User-centered design, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nazemi2011cb,

title = {Interacting with Semantics: A User-Centered Visualization Adaptation Based on Semantics Data},

author = {Kawa Nazemi and Matthias Breyer and Jeanette Forster and Dirk Burkhardt and Arjan Kuijper},

editor = {Michael J Smith and Gavriel Salvendy},

url = {https://link.springer.com/chapter/10.1007/978-3-642-21793-7_28, Springer link},

doi = {10.1007/978-3-642-21793-7_28},

isbn = {978-3-642-21793-7},

year  = {2011},

date = {2011-01-01},

booktitle = {Human Interface and the Management of Information. Interacting with Information. Symposium on Human Interface 2011.},

pages = {239--248},

publisher = {Springer Berlin Heidelberg},

address = {Berlin, Heidelberg},

series = {LNCS 6771},

abstract = {Semantically annotated data gain more and more importance in future information acquiring processes. Especially the Linked Open Data (LOD) format has already experienced a great growth. However, the user-interfaces of web-applications mostly do not reflect the added value of semantics data. The following paper describes a new approach of user-centered data-adaptive semantics visualization, which makes use of the advantages of semantics data combined with an adaptive composition of information visualization techniques. It starts with a related work section, where existing LOD systems and information visualization techniques are described. After that, the new approach will bridge the gap between semantically annotated data (LOD) and information visualization and introduces a visualization system that adapts the composition of visualizations based on the underlying data structure. A case study of an example case will conclude this paper.},

keywords = {Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Interaction Design, Semantic visualization, Semantic web, User behavior, User Interactions},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Burk2011c,

title = {Classifying Interaction Methods to Support Intuitive Interaction Devices for Creating User-Centered-Systems},

author = {Dirk Burkhardt and Matthias Breyer and Christian Glaser and Kawa Nazemi and Arjan Kuijper},

editor = {Constantine Stephanidis},

url = {https://link.springer.com/chapter/10.1007/978-3-642-21672-5_3, Springer link},

doi = {10.1007/978-3-642-21672-5_3},

isbn = {978-3-642-21672-8},

year  = {2011},

date = {2011-01-01},

pages = {20--29},

publisher = {Springer Berlin Heidelberg},

address = {Berlin, Heidelberg},

series = {LNCS 6765},

abstract = {Nowadays a wide range of input devices are available to users of technical systems. Especially modern alternative interaction devices, which are known from game consoles etc., provide a more natural way of interaction. But the support in computer programs is currently a big challenge, because a high effort is to invest for developing an application that supports such alternative input devices. For this fact we made a concept for an interaction system, which supports the use of alternative interaction devices. The interaction-system consists as central element a server, which provides a simple access interface for application to support such devices. It is also possible to address an abstract device by its properties and the interaction-system overtakes the converting from a concrete device. For realizing this idea, we also defined a taxonomy for classifying interaction devices by its interaction method and in dependence to the required interaction results, like recognized gestures. Later, by using this system, it is generally possible to develop a user-centered system by integrating this interaction-system, because an adequate integration of alternative interaction devices provides a more natural and easy to understand form of interaction.},

keywords = {Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Interaction Design, User behavior, User-centered design},

pubstate = {published},

tppubtype = {inproceedings}

}