Publications
Burkhardt, Dirk; Nazemi, Kawa Visualizing Law - A Norm-Graph Visualization Approach based on Semantic Legal Data Conference The 4th International Conference of the Virtual and Augmented Reality in Education, I3M I3M, 2018, ISBN: 978-88-85741-21-8. Abstract | Links | BibTeX | Tags: Information visualization, Semantic visualization, Visual analytics Burkhardt, Dirk; Pattan, Sachin; Nazemi, Kawa; Kuijper, Arjan 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). Abstract | Links | BibTeX | Tags: Information visualization, Intelligent Systems, User behavior, User Interactions, User Interface, User-centered design, Visual analytics Burkhardt, Dirk; Nazemi, Kawa Informationsvisualisierung und Visual Analytics zur Unterstützung von E-Government Prozessen Proceedings Article In: Bade, Korinna; Pietsch, Matthias; Raabe, Susanne; Schütz, Lars (Ed.): Technologische Trends im Spannungsfeld von Beteiligung – Entscheidung – Planung, pp. 29-38, Shaker Verlag, 2017, ISBN: 978-3844054392. Abstract | Links | BibTeX | Tags: eGovernance, Information visualization, Visual analytics Nazemi, Kawa; Burkhardt, Dirk; Kuijper, Arjan Analyzing the Information Search Behavior and Intentions in Visual Information Systems Journal Article In: Journal of Computer Science Technology Updates, vol. 4, 2017. Abstract | Links | BibTeX | Tags: 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 Nazemi, Kawa Adaptive Semantics Visualization Book Springer International Publishing, Studies in Computational Intelligence 646, 2016, ISBN: 978-3-319-30815-9. Abstract | Links | BibTeX | Tags: Adaptive visualization, Human Factors, Information visualization, Intelligent Systems, Visual analytics Nazemi, Kawa; Steiger, Martin; Burkhardt, Dirk; Kohlhammer, Jörn 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). Abstract | Links | BibTeX | Tags: Human-centered user interfaces, Information visualization, Semantic data modeling, Semantic visualization, User-centered design, Visual analytics Nazemi, Kawa; Retz, Reimond; Burkhardt, Dirk; Kuijper, Arjan; Kohlhammer, Jörn; Fellner, Dieter W Visual Trend Analysis with Digital Libraries Proceedings Article 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. Abstract | Links | BibTeX | Tags: Data Analytics, datamining, Information extraction, Information visualization, Trend analysis, Visual analytics Burkhardt, Dirk; Nazemi, Kawa; Ginters, Egils; Aizstrauts, Artis; Kohlhammer, Jörn Explorative Visualization of Impact Analysis for Policy Modeling by Bonding Open Government and Simulation Data Proceedings Article In: Yamamoto, Sakae (Ed.): International Conference on Human Interface and the Management of Information (HIMI 2015). Information and Knowledge Design., pp. 34–45, Springer International Publishing, Cham, 2015, ISBN: 978-3-319-20612-7. Abstract | Links | BibTeX | Tags: Exploration, Semantics visualization, Simulation, User behavior, User Interactions, User Interface, User-centered design, Visual analytics Nazemi, Kawa; Burkhardt, Dirk; Ginters, Egils; Kohlhammer, Jorn Semantics Visualization – Definition, Approaches and Challenges Journal Article In: Procedia Computer Science, vol. 75, pp. 75 - 83, 2015, ISSN: 1877-0509, (2015 International Conference Virtual and Augmented Reality in Education). Abstract | Links | BibTeX | Tags: Semantic visualization, Simulation, Visual analytics, Visualization and virtualization Nazemi, Kawa Adaptive Semantics Visualization PhD Thesis Technische Universität Darmstadt, 2014, (Reprint by Eugraphics Association (EG)). Abstract | Links | BibTeX | Tags: 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 Nazemi, Kawa Adaptive Semantics Visualization PhD Thesis Technische Universität Darmstadt, 2014, (Department of Computer Science. Supervised by Dieter W. Fellner.). Abstract | Links | BibTeX | Tags: 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 Nazemi, Kawa; Retz, Wilhelm; Kohlhammer, Jörn; Kuijper, Arjan User Similarity and Deviation Analysis for Adaptive Visualizations Proceedings Article In: Yamamoto, Sakae (Ed.): 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., pp. 64–75, Springer International Publishing , Cham, 2014, ISBN: 978-3-319-07731-7. Abstract | Links | BibTeX | Tags: 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 May, Thorsten; Nazemi, Kawa; Kohlhammer, Jörn From Raw Data to Rich Visualization: Combining Visual Search with Data Analysis Book Chapter In: Wahlster, Wolfgang; Grallert, Hans-Joachim; Wess, Stefan; Friedrich, Hermann; Widenka, Thomas (Ed.): Towards the Internet of Services: The THESEUS Research Program, pp. 203–209, Springer International Publishing, Cham, 2014, ISBN: 978-3-319-06755-1. Abstract | Links | BibTeX | Tags: Visual analytics Burkhardt, Dirk; Nazemi, Kawa; Kohlhammer, Jörn Visual Process Support to Assist Users in Policy Making Book Chapter In: Sonntagbauer, Peter; Nazemi, Kawa; Sonntagbauer, Susanne; Prister, Giorgio; Burkhardt, Dirk (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. Abstract | Links | BibTeX | Tags: Information visualization, Interaction analysis, Process Support, Semantic visualization, Visual analytics Nazemi, Kawa; Steiger, Martin; Burkhardt, Dirk; Kohlhammer, Jörn Information Visualization and Policy Modeling Book Chapter In: Sonntagbauer, Peter; Nazemi, Kawa; Sonntagbauer, Susanne; Prister, Giorgio; Burkhardt, Dirk (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. Abstract | Links | BibTeX | Tags: eGovernance, Information visualization, Policy modeling, Visual analytics Nazemi, Kawa; Burkhardt, Dirk; Retz, Reimond; Kuijper, Arjan; Kohlhammer, Jörn Adaptive Visualization of Linked-Data Proceedings Article In: Bebis, George; Boyle, Richard; Parvin, Bahram; Koracin, Darko; McMahan, Ryan; Jerald, Jason; Zhang, Hui; Drucker, Steven M; Kambhamettu, Chandra; Choubassi, Maha El; Deng, Zhigang; Carlson, Mark (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. Abstract | Links | BibTeX | Tags: 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 Burkhardt, Dirk; Nazemi, Kawa; Parisay, Mohsen; Kohlhammer, Jörn 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. Abstract | Links | BibTeX | Tags: Data Analytics, eGovernance, Human-computer interaction (HCI), Information visualization, Policy modeling, Visual analytics Nazemi, Kawa; Burkhardt, Dirk; Retz, Wilhelm; Kohlhammer, Jörn Adaptive Visualization of Social Media Data for Policy Modeling Proceedings Article In: Bebis, George; Boyle, Richard; Parvin, Bahram; Koracin, Darko; McMahan, Ryan; Jerald, Jason; Zhang, Hui; Drucker, Steven M; Kambhamettu, Chandra; Choubassi, Maha El; Deng, Zhigang; Carlson, Mark (Ed.): Proceeding of the International Symposium on Visual Computing (ISVC 2014). Advances in Visual Computing., pp. 333–344, Springer International Publishing, Cham, 2014, ISBN: 978-3-319-14249-4. Abstract | Links | BibTeX | Tags: 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 Nazemi, Kawa; Kuijper, Arjan; Hutter, Marco; Kohlhammer, Jörn; Fellner, Dieter W Measuring Context Relevance for Adaptive Semantics Visualizations Proceedings Article In: Proceedings of the 14th International Conference on Knowledge Technologies and Data-driven Business, pp. 14:1–14:8, ACM, Graz, Austria, 2014, ISBN: 978-1-4503-2769-5, (Honourable Mention). Abstract | Links | BibTeX | Tags: 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 Burkhardt, Dirk; Nazemi, Kawa; Encarnacao, Jose Daniel; Retz, Wilhelm; Kohlhammer, Jörn Visualization Adaptation Based on Environmental Influencing Factors Proceedings Article In: Kurosu, Masaaki (Ed.): International Conference on Human-Computer (HCI 2014). Human-Computer Interaction. Theories, Methods, and Tools., pp. 411–422, Springer International Publishing, Cham, 2014, ISBN: 978-3-319-07233-3. Abstract | Links | BibTeX | Tags: Adaptive information visualization, Adaptive user interfaces, User modeling, User-centered design, Visual analytics2018
@conference{Burkhardt2018,
title = {Visualizing Law - A Norm-Graph Visualization Approach based on Semantic Legal Data},
author = {Dirk Burkhardt and Kawa Nazemi},
editor = {A. G. Bruzzone and E. GINTERS and E. G. Mendívil and J. M. Guitierrez and F. Longo},
url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85056721291&origin=inward&txGid=497efbb2698c5dc7e8406ede09327453, Scopus},
isbn = {978-88-85741-21-8},
year = {2018},
date = {2018-09-17},
booktitle = {The 4th International Conference of the Virtual and Augmented Reality in Education},
publisher = {I3M},
organization = {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 = {Information visualization, Semantic visualization, Visual analytics},
pubstate = {published},
tppubtype = {conference}
}
2017
@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},
url = {https://www.sciencedirect.com/science/article/pii/S1877050917301710, Elsevier Science Direct
https://www.sciencedirect.com/science/article/pii/S1877050917301710/pdf?md5=505e85e86e138c532368faf70d2ab1e2&pid=1-s2.0-S1877050917301710-main.pdf, full text},
doi = {https://doi.org/10.1016/j.procs.2017.01.170},
issn = {1877-0509},
year = {2017},
date = {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}
}
@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}
}
@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}
}
2016
@book{C35-P-25155,
title = {Adaptive Semantics Visualization},
author = {Kawa Nazemi},
url = {https://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}
}
@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}
}
2015
@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}
}
@article{NAZEMI201575,
title = {Semantics Visualization – Definition, Approaches and Challenges},
author = {Kawa Nazemi and Dirk Burkhardt and Egils Ginters and Jorn Kohlhammer},
url = {https://www.sciencedirect.com/science/article/pii/S1877050915036777, Elsevier Science Direct
https://www.sciencedirect.com/science/article/pii/S1877050915036777/pdf?md5=b7e921e7548cdf69e35324864b0b2ea1&pid=1-s2.0-S1877050915036777-main.pdf, full text
},
doi = {https://doi.org/10.1016/j.procs.2015.12.216},
issn = {1877-0509},
year = {2015},
date = {2015-01-01},
journal = {Procedia Computer Science},
volume = {75},
pages = {75 - 83},
abstract = {The visualization of the simulation results must be done in conformity with beneficiaries perception and professional domain understanding. It means that right data must be identified before. Semantic technologies provide new ways for accessing data and acquiring knowledge. The underlying structures allow finding information easier, gathering meanings and associations of the data entities and associating the data to users’ knowledge. Even though the focus of the research in this area is more to provide “machine readable” data, human-centered systems benefit from the technologies too. Especially graphical representations of the semantically structured data play a key-role in today's research. The meaningful relations of data entities and the meaningful and labeled clustering of data in form of semantic concepts enable new ways to visualize data. With these new ways, various challenges are related with deploying semantics visualizations beyond analytical search and simulation. The goal is to give a common understanding of the term semantics as it is used in semantic web. This paper dealt with the general idea of semantics visualization. First a short introduction to semantic formalisms is given followed by a general definition. Subsequently approaches and techniques of existing semantics visualizations are presented, where-as a new classification is introduced to describe the techniques. The article concludes with future challenges in semantics visualization focusing on users, data and tasks.},
note = {2015 International Conference Virtual and Augmented Reality in Education},
keywords = {Semantic visualization, Simulation, Visual analytics, Visualization and virtualization},
pubstate = {published},
tppubtype = {article}
}
2014
@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}
}
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.@phdthesis{Nazemi2014g,
title = {Adaptive Semantics Visualization},
author = {Kawa Nazemi},
url = {https://tuprints.ulb.tu-darmstadt.de/id/eprint/4319, TU Darmstadt Prints
https://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}
}
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.@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}
}
@inbook{May2014,
title = {From Raw Data to Rich Visualization: Combining Visual Search with Data Analysis},
author = {Thorsten May and Kawa Nazemi and Jörn Kohlhammer},
editor = {Wolfgang Wahlster and Hans-Joachim Grallert and Stefan Wess and Hermann Friedrich and Thomas Widenka},
url = {https://www.springer.com/de/book/9783319067544?wt_mc=ThirdParty.SpringerLink.3.EPR653.About_eBook, Springer link},
doi = {10.1007/978-3-319-06755-1_16},
isbn = {978-3-319-06755-1},
year = {2014},
date = {2014-07-01},
booktitle = {Towards the Internet of Services: The THESEUS Research Program},
pages = {203--209},
publisher = {Springer International Publishing},
address = {Cham},
series = {Cognitive Technologies},
abstract = {Visual analytics is an interdisciplinary field of research at the boundary between data mining, statistics and visualization. Patterns and relations in the data complement a semantic representation of knowledge on a lower level of abstraction. One important goal of visual analytics is to find relations hidden in vast amounts of data, which can be turned into useful knowledge. Analysis needs to be ''visual'', because human's visual cognitive abilities are important for the identification and refinement of the analytical process. Further the results of the analysis have to be presented in a way to match the user's perspective on the proposed task. However, typical users are not experts in statistics or data mining. The challenge of visual analytics is to keep domain experts in charge of the analytical process while reducing the workload due to the complexity of the techniques. While search and analysis usually are mentioned in different contexts, they are highly interdependent processes. In fact, every exploratory analysis is a search for new knowledge. In turn, this knowledge can be used to refine future searches by introducing new concepts or relations to draw from. This article will show how automated and visual methods can be combined to connect knowledge artifacts on multiple levels of abstraction.},
keywords = {Visual analytics},
pubstate = {published},
tppubtype = {inbook}
}
@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}
}
@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}
}
@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}
}
@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 = {https://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}
}
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. @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}
}