@inproceedings{Burkhardt2017,

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},

doi = {10.2370/9783844054392},

isbn = {978-3844054392},

year  = {2017},

date = {2017-01-05},

booktitle = {Technologische Trends im Spannungsfeld von Beteiligung – Entscheidung – Planung},

pages = {29-38},

publisher = {Shaker Verlag},

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 = {https://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}

}

@inproceedings{10.1007/978-3-319-20612-7_4,

title = {Explorative Visualization of Impact Analysis for Policy Modeling by Bonding Open Government and Simulation Data},

author = {Dirk Burkhardt and Kawa Nazemi and Egils Ginters and Artis Aizstrauts and Jörn Kohlhammer},

editor = {Sakae Yamamoto},

url = {https://link.springer.com/chapter/10.1007/978-3-319-20612-7_4. Springer Link},

doi = {doi.org/10.1007/978-3-319-20612-7_4},

isbn = {978-3-319-20612-7},

year  = {2015},

date = {2015-03-01},

booktitle = {International Conference on Human Interface and the Management of Information (HIMI 2015). Information and Knowledge Design.},

pages = {34--45},

publisher = {Springer International Publishing},

address = {Cham},

series = {LNCS 9172},

abstract = {Problem identification and solution finding are major challenges in policy modeling. Statistical indicator-data build the foundation for most of the required analysis work. In particular finding effective and efficient policies that solve an existing political problem is critical, since the forecast validation of the effectiveness is quite difficult. Simulation technologies can help to identify optimal policies for solutions, but nowadays many of such simulators are stand-alone technologies. In this paper we introduce a new visualization approach to enable the coupling of statistical indicator data from Open Government Data sources with simulators and especially simulation result data with the goal to provide an enhanced impact analysis for political analysts and decision makers. This allows, amongst others a more intuitive and effective way of solution finding.},

keywords = {Exploration, Semantics visualization, Simulation, User behavior, User Interactions, User Interface, User-centered design, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nazemi2014e,

title = {User Similarity and Deviation Analysis for Adaptive Visualizations},

author = {Kawa Nazemi and Wilhelm Retz and Jörn Kohlhammer and Arjan Kuijper},

editor = {Sakae Yamamoto},

url = {https://link.springer.com/chapter/10.1007/978-3-319-07731-4_7, Springer link},

doi = {10.1007/978-3-319-07731-4_7},

isbn = {978-3-319-07731-7},

year  = {2014},

date = {2014-08-01},

booktitle = {International Conference on Human Interface and the Management of Information (HMI 2014). Human Interface and the Management of Information. Information and Knowledge Design and Evaluation.},

pages = {64--75},

publisher = {Springer International Publishing },

address = {Cham},

series = {LNCS 8521},

abstract = {Adaptive visualizations support users in information acquisition and exploration and therewith in human access of data. Their adaptation effect is often based on approaches that require the training by an expert. Further the effects often aims to support just the individual aptitudes. This paper introduces an approach for modeling a canonical user that makes the predefined training-files dispensable and enables an adaptation of visualizations for the majority of users. With the introduced user deviation algorithm, the behavior of individuals can be compared to the average user behavior represented in the canonical user model to identify behavioral anomalies. The further introduced similarity measurements allow to cluster similar deviated behavioral patterns as groups and provide them effective visual adaptations.},

keywords = {Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Data Analytics, reference model, Semantic visualization, Semantics visualization, User behavior, User Interactions, User Interface, User modeling, User-centered design, 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{Nazemi2014,

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{Nazemi:2014:MCR:2637748.2638416,

title = {Measuring Context Relevance for Adaptive Semantics Visualizations},

author = {Kawa Nazemi and Arjan Kuijper and Marco Hutter and Jörn Kohlhammer and Dieter W Fellner},

url = {https://doi.acm.org/10.1145/2637748.2638416, ACM DL},

doi = {10.1145/2637748.2638416},

isbn = {978-1-4503-2769-5},

year  = {2014},

date = {2014-01-01},

booktitle = {Proceedings of the 14th International Conference on Knowledge Technologies and Data-driven Business},

pages = {14:1--14:8},

publisher = {ACM},

address = {Graz, Austria},

series = {i-KNOW '14},

abstract = {Semantics visualizations enable the acquisition of information to amplify the acquisition of knowledge. The dramatic increase of semantics in form of Linked Data and Linked-Open Data yield search databases that allow to visualize the entire context of search results. The visualization of this semantic context enables one to gather more information at once, but the complex structures may as well confuse and frustrate users. To overcome the problems, adaptive visualizations already provide some useful methods to adapt the visualization on users' demands and skills. Although these methods are very promising, these systems do not investigate the relevance of semantic neighboring entities that commonly build most information value. We introduce two new measurements for the relevance of neighboring entities: The Inverse Instance Frequency allows weighting the relevance of semantic concepts based on the number of their instances. The Direct Relation Frequency inverse Relations Frequency measures the relevance of neighboring instances by the type of semantic relations. Both measurements provide a weighting of neighboring entities of a selected semantic instance, and enable an adaptation of retinal variables for the visualized graph. The algorithms can easily be integrated into adaptive visualizations and enhance them with the relevance measurement of neighboring semantic entities. We give a detailed description of the algorithms to enable a replication for the adaptive and semantics visualization community. With our method, one can now easily derive the relevance of neighboring semantic entities of selected instances, and thus gain more information at once, without confusing and frustrating users.},

note = {Honourable Mention},

keywords = {Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Data Analytics, data weighting, Information retrieval, semantic processing, Semantic web, Semantics visualization, User Interface, User modeling, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Burkhardt2014f,

title = {Visualization Adaptation Based on Environmental Influencing Factors},

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

editor = {Masaaki Kurosu},

url = {https://link.springer.com/chapter/10.1007/978-3-319-07233-3_38, Springer link},

isbn = {978-3-319-07233-3},

year  = {2014},

date = {2014-01-01},

booktitle = {International Conference on Human-Computer (HCI 2014). Human-Computer Interaction. Theories, Methods, and Tools.},

pages = {411--422},

publisher = {Springer International Publishing},

address = {Cham},

series = {LNCS 8510},

abstract = {Working effectively with computer-based devices is challenging, especially under mobile conditions, due to the various environmental influences. In this paper a visualization adaptation approach is described, to support the user under discriminatory environmental conditions. For this purpose, a context model for environmental influencing factors is being defined. Based on this context model, an approach to adapt visualizations in regards of certain environmental influences is being evolved, such as the light intensity, air quality, or heavy vibrations.},

keywords = {Adaptive information visualization, Adaptive user interfaces, User modeling, User-centered design, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}