@inproceedings{Nazemi2020,

title = {Visual Analytics in Mobility, Transportation and Logistics},

author = {Kawa Nazemi and Dirk Burkhardt and Lukas Kaupp and Till Dannewald and Matthias Kowald and Egils Ginters},

editor = {Egils Ginters and Mario Arturo Ruiz Estrada and Miquel Angel Piera Eroles},

doi = {10.1007/978-3-030-39688-6_12},

isbn = {978-3-030-39688-6},

year  = {2020},

date = {2020-01-01},

booktitle = {ICTE in Transportation and Logistics 2019},

pages = {82--89},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {Mobility, transportation and logistics are more and more influenced by a variety of indicators such as new technological developments, ecological and economic changes, political decisions and in particular humans' mobility behavior. These indicators will lead to massive changes in our daily live with regards to mobility, transportation and logistics. New technologies will lead to a different mobility behavior with new constraints. These changes in mobility behavior and logistics require analytical systems to forecast the required information and probably appearing changes. These systems have to consider different perspectives and employ multiple indicators. Visual Analytics provides both, the analytical approaches by including machine learning approaches and interactive visualizations to enable such analytical tasks. In this paper the main indicators for Visual Analytics in the domain of mobility transportation and logistics are discussed and followed by exemplary case studies to illustrate the advantages of such systems. The examples are aimed to demonstrate the benefits of Visual Analytics in mobility.},

keywords = {Data Analytics, Mobility Behavior, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Burkhardt2020,

title = {Process Support and Visual Adaptation to Assist Visual Trend Analytics in Managing Transportation Innovations},

author = {Dirk Burkhardt and Kawa Nazemi and Egils Ginters},

editor = {Egils Ginters and Mario Arturo Ruiz Estrada and Miquel Angel Piera Eroles},

doi = {10.1007/978-3-030-39688-6_40},

isbn = {978-3-030-39688-6},

year  = {2020},

date = {2020-01-01},

booktitle = {ICTE in Transportation and Logistics 2019},

pages = {319--327},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {In the domain of mobility and logistics, a variety of new technologies and business ideas are arising. Beside technologies that aim on ecologically and economic transportation, such as electric engines, there are also fundamental different approaches like central packaging stations or deliveries via drones. Yet, there is a growing need for analytical systems that enable identifying new technologies, innovations, business models etc. and give also the opportunity to rate those in perspective of business relevance. Commonly adaptive systems investigate only the users' behavior, while a process-related supports could assist to solve an analytical task more efficient and effective. In this article an approach that enables non-experts to perform visual trend analysis through an advanced process support based on process mining is described. This allow us to calculate a process model based on events, which is the baseline for process support feature calculation. These features and the process model enable to assist non-expert users in complex analytical tasks.},

keywords = {Adaptive Visualization, Process Mining, Transportation and Logistics},

pubstate = {published},

tppubtype = {inproceedings}

}

@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{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}

}

@inproceedings{Ginters2019,

title = {The Concept of Augmented Reality Application for Putting Alignment in Golf},

author = {Egils Ginters and Dirk Burkhardt and Kawa Nazemi and Yuri Merkuryev},

url = {http://www.msc-les.org/proceedings/vare/2019/VARE2019.pdf, Proceedings as PDF},

isbn = {978-88-85741-41-6},

year  = {2019},

date = {2019-09-18},

booktitle = {5th International Conference of the Virtual and Augmented Reality in Education (VARE 2019)},

pages = {20--27},

publisher = {CAL-TEK SRL},

address = {Rende, Italy},

abstract = {Virtual and augmented reality (VR / AR) applications have successfully overcome the critical part of the Gartner curve. Investments are made and new products entering the economy. However, a very small percentage of society have also heard about AR glasses, mainly linking these with potential identity threats and personal data breaches. The authors dealt with the design of application of AR to improve golf skills by improving the putting technique. The above solution is complicated by requiring complex object recognition, tracking and advanced AR software designing.},

keywords = {Android, Augmented Reality, Intelligent Training, Object Tracking, Objects Recognition},

pubstate = {published},

tppubtype = {inproceedings}

}

@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},

booktitle = {2019 23rd International Conference Information Visualisation (IV)},

pages = {191-200},

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 = {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}

}

@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{Deuschel2018,

title = {On the Influence of Human Factors in Adaptive User Interface Design},

author = {Tilman Deuschel},

doi = {10.1145/3213586.3213587},

isbn = {978-1-4503-5784-5},

year  = {2018},

date = {2018-07-08},

booktitle = {Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization},

pages = {187--190},

publisher = {ACM},

address = {Singapore, Singapore},

series = {UMAP '18},

abstract = {The increased utilisation of adaptive visualisation techniques in commercial software requires design principles that provide guidance on how to design user interfaces that change their appearance during runtime. To contribute to the ongoing adaptive user interface (AUI) research, the development of a model is proposed that enables the methodological development of design principles for AUIs. The adoption of the conceptual framework of design-science is proposed for that purpose. Following the framework, the literature of AUI research (to identify observed user behaviour) as well as the literature of human factors (to explain the observed behaviour) have been reviewed. Design principles are built based on this knowledge. This thesis can contribute to an quality improvement of AUI systems.},

keywords = {Adaptive User Interfaces, design principles, empirical evaluation},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}

@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}

}