Publications
Nazemi, Kawa; Burkhardt, Dirk; Hoppe, David; Nazemi, Mariam; Kohlhammer, Jörn Web-based Evaluation of Information Visualization Journal Article In: Procedia Manufacturing, vol. 3, pp. 5527 - 5534, 2015, ISSN: 2351-9789, (6th International Conference on Applied Human Factors and Ergonomics (AHFE 2015) and the Affiliated Conferences, AHFE 2015). Abstract | Links | BibTeX | Tags: Evaluation Methods, Evaluation Tools, Human Perception, Information visualization, User Study, User-centered design, Web-based Evaluation 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 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 Burkhardt, Dirk; Nazemi, Kawa; Retz, Wilhelm; Kohlhammer, Jörn Visual explanation of government-data for policy making through open-data inclusion Proceedings Article In: The 9th International Conference for Internet Technology and Secured Transactions (ICITST-2014), pp. 83-89, IEEE, 2014, ISBN: 978-1-908320-39-1. Abstract | Links | BibTeX | Tags: eGovernance, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Interaction Design, Policy modeling, Semantic visualization, User-centered design 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; Kohlhammer, Jörn Visual Variables in Adaptive Visualizations. Proceedings Article In: Berkovsky, Shlomo; Herder, Eelco; Lops, Pasquale; Santos, Olga C. (Ed.): 21st Conference on User Modeling, Adaptation, and Personalization. UMAP 2013 Extended Proceedings. Proceeding of 1st International Workshop on User-Adaptive Visualizations., CEUR Workshop Proceedings, Rome, Italy,, 2013, ISSN: 1613-0073. Abstract | Links | BibTeX | Tags: Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Human Factors, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, Semantic visualization Nazemi, Kawa; Retz, Reimond; Bernard, Jürgen; Kohlhammer, Jörn; Fellner, Dieter Adaptive Semantic Visualization for Bibliographic Entries Proceedings Article In: Bebis, George; Boyle, Richard; Parvin, Bahram; Koracin, Darko; Li, Baoxin; Porikli, Fatih; Zordan, Victor; Klosowski, James; Coquillart, Sabine; Luo, Xun; Chen, Min; Gotz, David (Ed.): Proceedings of International Symposium on Visual Computing (ISVC 2013). Advances in Visual Computing., pp. 13–24, Springer Berlin Heidelberg, Berlin, Heidelberg, 2013, ISBN: 978-3-642-41939-3. 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, Visual analytics Kohlhammer, Jörn; Nazemi, Kawa; Ruppert, Tobias; Burkhardt, Dirk Toward Visualization in Policy Modeling Journal Article In: IEEE Computer Graphics and Applications (CG&A), vol. 32, no. 5, pp. 84-89, 2012, ISSN: 0272-1716. Abstract | Links | BibTeX | Tags: Data Analytics, eGovernance, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Policy modeling, Semantic data modeling, Semantic visualization, Visual analytics Burkhardt, Dirk; Stab, Christian; Steiger, Martin; Breyer, Matthias; Nazemi, Kawa Interactive Exploration System: A User-Centered Interaction Approach in Semantics Visualizations Proceedings Article In: 2012 International Conference on Cyberworlds, pp. 261-267, IEEE, 2012, ISBN: 978-1-4673-2736-7. Abstract | Links | BibTeX | Tags: Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Interaction Design, Semantics visualization, User-centered design, Visual analytics Burkhardt, Dirk; Ruppert, Tobias; Nazemi, Kawa Towards process-oriented Information Visualization for supporting users Proceedings Article In: 15th International Conference on Interactive Collaborative Learning (ICL), pp. 1-8, Institute of Electrical and Electronics Engineering IEEE IEEE Press, 2012, ISBN: 978-1-4673-2427-4. Abstract | Links | BibTeX | Tags: Human Factors, Human-centered user interfaces, Information visualization, User Interface, User-centered design Nazemi, Kawa; Burkhardt, Dirk; Praetorius, Alexander; Breyer, Matthias; Kuijper, Arjan Adapting User Interfaces by Analyzing Data Characteristics for Determining Adequate Visualizations Proceedings Article In: Kurosu, Masaaki (Ed.): Human Centered Design, pp. 566–575, Springer Berlin Heidelberg, Berlin, Heidelberg, 2011, ISBN: 978-3-642-21753-1. Abstract | Links | BibTeX | Tags: Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Data Analytics, Human Factors, Human-computer interaction (HCI), Information visualization, Intelligent Systems, personalization, reference model, Semantic visualization, Semantic web, User behavior Burkhardt, Dirk; Nazemi, Kawa; Breyer, Matthias; Stab, Christian; Kuijper, Arjan SemaZoom: Semantics Exploration by Using a Layer-Based Focus and Context Metaphor Proceedings Article In: Kurosu, Masaaki (Ed.): Human Centered Design, pp. 491–499, Springer Berlin Heidelberg, Berlin, Heidelberg, 2011, ISBN: 978-3-642-21753-1. Abstract | Links | BibTeX | Tags: Graph visualization, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, User behavior, User Interactions, User Interface, User interfaces, User-centered design, Visual analytics Stab, Christian; Nazemi, Kawa; Breyer, Matthias; Burkhardt, Dirk; Kuijper, Arjan Interacting with Semantics and Time Proceedings Article In: Jacko, Julie A (Ed.): Human-Computer Interaction. Users and Applications. Proceedings of HCI International 2011, pp. 520–529, Springer Berlin Heidelberg, Berlin, Heidelberg, 2011, ISBN: 978-3-642-21619-0. Abstract | Links | BibTeX | Tags: Data Analytics, Human Factors, Human-centered user interfaces, Information visualization, Ontology visualization, Semantic visualization, Semantic web, Temporal Visualization, User behavior Nazemi, Kawa; Breyer, Matthias; Forster, Jeanette; Burkhardt, Dirk; Kuijper, Arjan Interacting with Semantics: A User-Centered Visualization Adaptation Based on Semantics Data Proceedings Article In: Smith, Michael J.; Salvendy, Gavriel (Ed.): Human Interface and the Management of Information. Interacting with Information. Symposium on Human Interface 2011., pp. 239–248, Springer Berlin Heidelberg, Berlin, Heidelberg, 2011, ISBN: 978-3-642-21793-7. Abstract | Links | BibTeX | Tags: Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Interaction Design, Semantic visualization, Semantic web, User behavior, User Interactions Nazemi, Kawa; Breyer, Matthias; Burkhardt, Dirk; Fellner, Dieter W Visualization Cockpit: Orchestration of Multiple Visualizations for Knowledge-Exploration Journal Article In: International Journal of Advanced Corporate Learning, vol. 3, no. 4, pp. 26-34, 2010, ISSN: 1867-5565. Abstract | Links | BibTeX | Tags: Computer based learning, E-Learning, Exploratory learning, Human-computer interaction (HCI), Information visualization, Visual analytics Stab, Christian; Nazemi, Kawa; Fellner, Dieter W SemaTime - Timeline Visualization of Time-Dependent Relations and Semantics Conference Advances in Visual Computing. 6th International Symposium, Proceeding of ISVC 2010, LNCS 6455 Springer-Verlag, Berlin Heidelberg, 2010, ISBN: 978-3-642-17276-2. Abstract | Links | BibTeX | Tags: Information visualization, Interactive information visualization, Semantic visualization, Time, Timeline visualization, Visual analytics2015
@article{Nazemi2015d,
title = {Web-based Evaluation of Information Visualization},
author = {Kawa Nazemi and Dirk Burkhardt and David Hoppe and Mariam Nazemi and Jörn Kohlhammer},
url = {https://www.sciencedirect.com/science/article/pii/S2351978915007192, Elsevier Science Direct
https://www.sciencedirect.com/science/article/pii/S2351978915007192/pdf?md5=ee6ef6cc5f2f761a33314ffc3ee12445&pid=1-s2.0-S2351978915007192-main.pdf, full text},
doi = {https://doi.org/10.1016/j.promfg.2015.07.718},
issn = {2351-9789},
year = {2015},
date = {2015-03-01},
journal = {Procedia Manufacturing},
volume = {3},
pages = {5527 - 5534},
abstract = {Information visualization is strongly related to human perception, human behavior, and in particular human interaction. It is a discipline that focuses on human to enable him gathering insights, knowledge, and solving various and heterogeneous tasks. The human-centered characteristic of information visualization requires valid and proper user studies that improve the system or validate their benefits. New methods, techniques, or approaches of information visualization are commonly evaluated. However, the evaluation is either time and cost consuming or they are made minimum resources that leads to results, which may not be valid. In particular the number of participants is commonly restricted and does not enable a valid assumption about the results. Thus performance measures plays a key role in information visualization, existing web-survey tools are not convenient. We introduce in this paper a new method that enables web-based evaluations of information visualization systems. Our main contribution is the enhancement of web-based survey tools with performance measures. Our approach enables the measurement of task-completion time, correctness of solved tasks, and includes a number of pre- and post-questionnaires.},
note = {6th International Conference on Applied Human Factors and Ergonomics (AHFE 2015) and the Affiliated Conferences, AHFE 2015},
keywords = {Evaluation Methods, Evaluation Tools, Human Perception, Information visualization, User Study, User-centered design, Web-based Evaluation},
pubstate = {published},
tppubtype = {article}
}
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.@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{7038782,
title = {Visual explanation of government-data for policy making through open-data inclusion},
author = {Dirk Burkhardt and Kawa Nazemi and Wilhelm Retz and Jörn Kohlhammer},
url = {https://ieeexplore.ieee.org/document/7038782/, IEEE Xplore},
doi = {10.1109/ICITST.2014.7038782},
isbn = {978-1-908320-39-1},
year = {2014},
date = {2014-01-01},
booktitle = {The 9th International Conference for Internet Technology and Secured Transactions (ICITST-2014)},
pages = {83-89},
publisher = {IEEE},
abstract = {Commonly, data used in public authorities are statistical data about certain indicator. Such valid kind of data allows an objective observation about indicator developments over time. In case of a significant deviation from the normal indicator level, it is difficult to understand the reasons for upcoming problems. In our paper we present an approach that allows an enhanced information gathering through an improved information overview about the depending aspects to such an indicator by considering governmental data-sources that provide also other types of data than just statistics. Even more, our approach integrates a system that allows generating explanations for Open Government Data, especially to specific indicators, based on Linked-Open Data. This enables decision-makers to get hints for unexpected reasons of concrete problems that may influence an indicator.},
keywords = {eGovernance, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Interaction Design, Policy modeling, Semantic visualization, User-centered design},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{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}
}
2013
@inproceedings{nazemi2013visual,
title = {Visual Variables in Adaptive Visualizations.},
author = {Kawa Nazemi and Jörn Kohlhammer},
editor = {Shlomo Berkovsky and Eelco Herder and Pasquale Lops and Olga C. Santos },
url = {https://ceur-ws.org/Vol-997/wuav2013_paper_06.pdf, full text},
issn = {1613-0073},
year = {2013},
date = {2013-06-01},
booktitle = {21st Conference on User Modeling, Adaptation, and Personalization. UMAP 2013 Extended Proceedings. Proceeding of 1st International Workshop on User-Adaptive Visualizations.},
publisher = {CEUR Workshop Proceedings},
address = {Rome, Italy,},
series = {Vol. 997},
abstract = {Visualizations provide various variables for the adaptation to the usage context and the users. Today’s adaptive visualizations make use of various visual variables to order or filter information or visualizations. However, the capabilities of visual variables in context of human information processing and tasks are not comprehensively exploited. This paper discusses the value of the different visual variables providing beneficial and more accurately adapted information visualizations.},
keywords = {Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Human Factors, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, Semantic visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Nazemi2013,
title = {Adaptive Semantic Visualization for Bibliographic Entries},
author = {Kawa Nazemi and Reimond Retz and Jürgen Bernard and Jörn Kohlhammer and Dieter Fellner},
editor = {George Bebis and Richard Boyle and Bahram Parvin and Darko Koracin and Baoxin Li and Fatih Porikli and Victor Zordan and James Klosowski and Sabine Coquillart and Xun Luo and Min Chen and David Gotz},
url = {https://link.springer.com/chapter/10.1007/978-3-642-41939-3_2, Springer link},
doi = {10.1007/978-3-642-41939-3_2},
isbn = {978-3-642-41939-3},
year = {2013},
date = {2013-01-01},
booktitle = {Proceedings of International Symposium on Visual Computing (ISVC 2013). Advances in Visual Computing.},
pages = {13--24},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
series = {LNCS 8034},
abstract = {Adaptive visualizations aim to reduce the complexity of visual representations and convey information using interactive visualizations. Although the research on adaptive visualizations grew in the last years, the existing approaches do not make use of the variety of adaptable visual variables. Further the existing approaches often premises experts, who has to model the initial visualization design. In addition, current approaches either incorporate user behavior or data types. A combination of both is not proposed to our knowledge. This paper introduces the instantiation of our previously proposed model that combines both: involving different influencing factors for and adapting various levels of visual peculiarities, on visual layout and visual presentation in a multiple visualization environment. Based on data type and users’ behavior, our system adapts a set of applicable visualization types. Moreover, retinal variables of each visualization type are adapted to meet individual or canonic requirements on both, data types and users’ behavior. Our system does not require an initial expert modeling.},
keywords = {Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Data Analytics, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, personalization, Semantic visualization, Semantic web, User behavior, User Interactions, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
2012
@article{6311373,
title = {Toward Visualization in Policy Modeling},
author = {Jörn Kohlhammer and Kawa Nazemi and Tobias Ruppert and Dirk Burkhardt},
url = {https://ieeexplore.ieee.org/document/6311373/, IEEE Xplore},
doi = {10.1109/MCG.2012.107},
issn = {0272-1716},
year = {2012},
date = {2012-09-01},
journal = {IEEE Computer Graphics and Applications (CG&A)},
volume = {32},
number = {5},
pages = {84-89},
publisher = {IEEE Press},
abstract = {This article looks at the current and future roles of information visualization, semantics visualization, and visual analytics in policy modeling. Many experts believe that you can't overestimate visualization's role in this respect.},
keywords = {Data Analytics, eGovernance, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Policy modeling, Semantic data modeling, Semantic visualization, Visual analytics},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{6337431,
title = {Interactive Exploration System: A User-Centered Interaction Approach in Semantics Visualizations},
author = {Dirk Burkhardt and Christian Stab and Martin Steiger and Matthias Breyer and Kawa Nazemi},
doi = {10.1109/CW.2012.45},
isbn = {978-1-4673-2736-7},
year = {2012},
date = {2012-09-01},
booktitle = {2012 International Conference on Cyberworlds},
pages = {261-267},
publisher = {IEEE},
abstract = {Nowadays a wide range of input devices are available to users of technical systems. Especially modern alternative interaction devices, which are known from game consoles etc., provide a more natural way of interaction. In parallel to that the research on visualization of large amount of data advances very quickly. This research was also influenced by the semantic web and the idea of storing data in a structured and linked form. The semantically annotated data gains more and more importance in information acquisition processes. Especially the Linked Open Data (LOD) format already experienced a huge growth. However, the user-interfaces of web-applications mostly do not reflect the added value of semantics data. This paper describes the conceptual design and implementation of an Interactive Exploration System that offers a user-centered graphical environment of web-based knowledge repositories, to support and optimize explorative learning, and the integration of a taxonomy-based approach to enable the use of more natural interaction metaphors, as they are possible with modern devices like Wii Mote or Microsoft Kinect. Therefore we introduce a different classification for interaction devices, and current approaches for supporting the added values in semantics visualizations. Furthermore, we describe the concept of our IES, including a strategy to organize and structure today's existing input devices, and a semantics exploration system driven by user-experience. We conclude the paper with a description of the implementation of the IES and an application scenario.},
keywords = {Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Interaction Design, Semantics visualization, User-centered design, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{6402080,
title = {Towards process-oriented Information Visualization for supporting users},
author = {Dirk Burkhardt and Tobias Ruppert and Kawa Nazemi},
url = {https://ieeexplore.ieee.org/document/6402080/?anchor=citations, IEEE Xplore},
doi = {10.1109/ICL.2012.6402080},
isbn = {978-1-4673-2427-4},
year = {2012},
date = {2012-07-01},
booktitle = {15th International Conference on Interactive Collaborative Learning (ICL)},
pages = {1-8},
publisher = {IEEE Press},
organization = { Institute of Electrical and Electronics Engineering IEEE},
abstract = {Nowadays daily office work consists of dealing with big numbers of data and data sources, and furthermore of working with complex computer programs. In consequence many users have problems to use such programs effective and efficient. In particular beginners have significant problems to use the programs correctly due to complex functionality and interaction options. To avoid this overload of the user, the Information Visualization community has recently developed some approaches that aim to support the users. Unfortunately, these approaches are limited to one special aspect, and sometimes they are just appropriate for one special task. Thus, in this paper we introduce a process-oriented user-supporting approach. It allows selecting adequate supporting techniques in correlation to a performed process and activity to guide the user and help him to solve his task. Furthermore, we show the benefits of designing programs and applications, which implement process definitions for the existing tasks to provide the user with better process orientation. This guides the user through difficult and complex processes.},
keywords = {Human Factors, Human-centered user interfaces, Information visualization, User Interface, User-centered design},
pubstate = {published},
tppubtype = {inproceedings}
}
2011
@inproceedings{Nazemi2011c,
title = {Adapting User Interfaces by Analyzing Data Characteristics for Determining Adequate Visualizations},
author = {Kawa Nazemi and Dirk Burkhardt and Alexander Praetorius and Matthias Breyer and Arjan Kuijper},
editor = {Masaaki Kurosu},
url = {https://doi.org/10.1007/978-3-642-21753-1_63, DOI
https://link.springer.com/chapter/10.1007/978-3-642-21753-1_63, Springer page},
doi = {10.1007/978-3-642-21753-1_63},
isbn = {978-3-642-21753-1},
year = {2011},
date = {2011-01-01},
booktitle = {Human Centered Design},
pages = {566--575},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
abstract = {Today the information visualization takes in an important position, because it is required in nearly every context where large databases have to be visualized. For this challenge new approaches are needed to allow the user an adequate access to these data. Static visualizations are only able to show the data without any support to the users, which is the reason for the accomplished researches to adaptive user-interfaces, in particular for adaptive visualizations. By these approaches the visualizations were adapted to the users' behavior, so that graphical primitives were change to support a user e.g. by highlighting user-specific entities, which seems relevant for a user. This approach is commonly used, but it is limited on changes for just a single visualization. Modern heterogeneous data providing different kinds of aspects, which modern visualizations try to regard, but therefore a user often needs more than a single visualization for making an information retrieval. In this paper we describe a concept for adapting the user-interface by selecting visualizations in dependence to automatically generated data characteristics. So visualizations will be chosen, which are fitting well to the generated characteristics. Finally the user gets an aquatically arranged set of visualizations as initial point of his interaction through the data.},
keywords = {Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Data Analytics, Human Factors, Human-computer interaction (HCI), Information visualization, Intelligent Systems, personalization, reference model, Semantic visualization, Semantic web, User behavior},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{10.1007/978-3-642-21753-1_55,
title = {SemaZoom: Semantics Exploration by Using a Layer-Based Focus and Context Metaphor},
author = {Dirk Burkhardt and Kawa Nazemi and Matthias Breyer and Christian Stab and Arjan Kuijper},
editor = {Masaaki Kurosu},
url = {https://doi.org/10.1007/978-3-642-21753-1_55, DOI
https://link.springer.com/chapter/10.1007/978-3-642-21753-1_55, Springer page},
doi = {10.1007/978-3-642-21753-1_55},
isbn = {978-3-642-21753-1},
year = {2011},
date = {2011-01-01},
booktitle = {Human Centered Design},
pages = {491--499},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
series = {LNCS 6776},
abstract = {The Semantic Web is a powerful technology for organizing the data in our information based society. The collection and organization of information is an important step for showing important information to interested people. But the usage of such semantic-based data sources depends on effective and efficient information visualizations. Currently different kinds of visualizations in general and visualization metaphors do exist. Many of them are also applied for semantic data source, but often they are designed for semantic web experts and neglecting the normal user and his perception of an easy useable visualization. This kind of user needs less information, but rather a reduced qualitative view on the data. These two aspects of large amount of existing data and one for normal users easy to understand visualization is often not reconcilable. In this paper we create a concept for a visualization to show a bigger set of information to such normal users without overstraining them, because of layer-based data visualization, next to an integration of a Focus and Context metaphor.},
keywords = {Graph visualization, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, User behavior, User Interactions, User Interface, User interfaces, User-centered design, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Stab2011,
title = {Interacting with Semantics and Time},
author = {Christian Stab and Kawa Nazemi and Matthias Breyer and Dirk Burkhardt and Arjan Kuijper},
editor = {Julie A Jacko},
url = {https://link.springer.com/chapter/10.1007/978-3-642-21619-0_64, Springer link
},
doi = {10.1007/978-3-642-21619-0_64},
isbn = {978-3-642-21619-0},
year = {2011},
date = {2011-01-01},
booktitle = {Human-Computer Interaction. Users and Applications. Proceedings of HCI International 2011},
volume = {4},
pages = {520--529},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
series = {LNCS 6764},
abstract = {Time appears in many different semantic information systems like historical databases, multimedia systems or social communities as a common attribute. Beside the temporal information, the resources in these domains are categorized in a domain-specific schema and interconnected by semantic relations. Nevertheless, the high potential of these systems is not yet exhausted completely. Even today most of these knowledge systems present time-dependent semantic knowledge in textual form, what makes it difficult for the average user to understand temporal structures and dependencies. For bridging this gap between human and computer and for simplifying the exploration of time-dependent semantic knowledge, we developed a new interactive timeline visualization called SemaTime. The new designed temporal navigation concept offers an intuitive way for exploring and filtering time-depended resources. Additionally SemaTime offers navigation and visual filtering methods on the conceptual layer of the domain and is able to depict semantic relations. In this paper we describe the conceptual design of SemaTime and illustrate its application potentials in semantic search environments.},
keywords = {Data Analytics, Human Factors, Human-centered user interfaces, Information visualization, Ontology visualization, Semantic visualization, Semantic web, Temporal Visualization, User behavior},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Nazemi2011c,
title = {Interacting with Semantics: A User-Centered Visualization Adaptation Based on Semantics Data},
author = {Kawa Nazemi and Matthias Breyer and Jeanette Forster and Dirk Burkhardt and Arjan Kuijper},
editor = {Michael J. Smith and Gavriel Salvendy},
url = {https://link.springer.com/chapter/10.1007/978-3-642-21793-7_28, Springer link
},
doi = {10.1007/978-3-642-21793-7_28},
isbn = {978-3-642-21793-7},
year = {2011},
date = {2011-01-01},
booktitle = {Human Interface and the Management of Information. Interacting with Information. Symposium on Human Interface 2011.},
pages = {239--248},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
series = {LNCS 6771},
abstract = {Semantically annotated data gain more and more importance in future information acquiring processes. Especially the Linked Open Data (LOD) format has already experienced a great growth. However, the user-interfaces of web-applications mostly do not reflect the added value of semantics data. The following paper describes a new approach of user-centered data-adaptive semantics visualization, which makes use of the advantages of semantics data combined with an adaptive composition of information visualization techniques. It starts with a related work section, where existing LOD systems and information visualization techniques are described. After that, the new approach will bridge the gap between semantically annotated data (LOD) and information visualization and introduces a visualization system that adapts the composition of visualizations based on the underlying data structure. A case study of an example case will conclude this paper.},
keywords = {Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Interaction Design, Semantic visualization, Semantic web, User behavior, User Interactions},
pubstate = {published},
tppubtype = {inproceedings}
}
2010
@article{C35-P-21710,
title = {Visualization Cockpit: Orchestration of Multiple Visualizations for Knowledge-Exploration},
author = {Kawa Nazemi and Matthias Breyer and Dirk Burkhardt and Dieter W Fellner},
url = {https://online-journals.org/index.php/i-jac/article/view/1473, iJAC Journal
https://online-journals.org/index.php/i-jac/article/download/1473/1560.pdf, Full Paper},
issn = {1867-5565},
year = {2010},
date = {2010-01-01},
journal = {International Journal of Advanced Corporate Learning},
volume = {3},
number = {4},
pages = {26-34},
abstract = {Semantic-Web technologies and ontology-based information processing systems are established techniques, in more than only research areas and institutions. Different worldwide projects and enterprise companies identified already the added value of semantic technologies, so they work on different sub-topics for gathering and conveying knowledge. As the process of gathering and structuring semantic information plays a key role in the most developed applications, the process of transferring and adopting knowledge to and by humans is neglected, although the complex structure of knowledge-design opens many research-questions. The customization of the presentation itself and the interaction techniques with these presentation artifacts is a key question for gainful and effective work with semantic information. The following paper describes a new approach for visualizing semantic information as a composition of different adaptable ontology-visualization techniques. We start with a categorized description of existing ontology visualization techniques and show potential gaps.},
keywords = {Computer based learning, E-Learning, Exploratory learning, Human-computer interaction (HCI), Information visualization, Visual analytics},
pubstate = {published},
tppubtype = {article}
}
@conference{C35-P-21759,
title = {SemaTime - Timeline Visualization of Time-Dependent Relations and Semantics},
author = {Christian Stab and Kawa Nazemi and Dieter W Fellner},
editor = {[G. Bebis et al.]},
url = {https://doi.org/10.1007/978-3-642-17277-9_53, DOI
https://link.springer.com/chapter/10.1007/978-3-642-17277-9_53, Springer Link},
doi = {10.1007/978-3-642-17277-9_53},
isbn = {978-3-642-17276-2},
year = {2010},
date = {2010-01-01},
booktitle = {Advances in Visual Computing. 6th International Symposium, Proceeding of ISVC 2010},
pages = {514-523},
publisher = {Springer-Verlag},
address = {Berlin Heidelberg},
series = {LNCS 6455},
abstract = {Timeline based visualizations arrange time-dependent entities along a time-axis and are used in many different domains like digital libraries, criminal investigation and medical information systems to support users in understanding chronological structures. By the use of semantic technologies, the information is categorized in a domain-specific, hierarchical schema and specified by semantic relations. Commonly semantic relations in timeline visualizations are depicted by interconnecting entities with a directed edge. However it is possible that semantic relations change in the course of time. In this paper we introduce a new timeline visualization for time-dependent semantics called SemaTime that offers a hierarchical categorization of time-dependent entities including navigation and filtering features. We also present a novel concept for visualizing time-dependent relations that allows the illustration of time-varying semantic relations and affords an easy understandable
visualization of complex, time-dependent interrelations.},
keywords = {Information visualization, Interactive information visualization, Semantic visualization, Time, Timeline visualization, Visual analytics},
pubstate = {published},
tppubtype = {conference}
}
visualization of complex, time-dependent interrelations.