Publikationen
Nazemi, Kawa; Burkhardt, Dirk A Visual Analytics Approach for Analyzing Technological Trends in Technology and Innovation Management Proceedings Article In: Bebis, George; Boyle, Richard; Parvin, Bahram; Koracin, Darko; Ushizima, Daniela; Chai, Sek; Sueda, Shinjiro; Lin, Xin; Lu, Aidong; Thalmann, Daniel; Wang, Chaoli; Xu, Panpan (Hrsg.): Advances in Visual Computing, S. 283–294, Springer International Publishing, Cham, 2019, ISBN: 978-3-030-33723-0. Abstract | Links | BibTeX | Schlagwörter: Artificial Intelligence, Data Analytics, Human Factors, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Machine Leanring, Visual analytics Nazemi, Kawa Intelligent Visual Analytics -- a Human-Adaptive Approach for Complex and Analytical Tasks Buchkapitel In: Karwowski, W.; Ahram, T. (Hrsg.): Intelligent Human Systems Integration: Proceedings of the International Conference on Intelligent Human Systems Integration (IHSI 2018): Integrating People and Intelligent Systems. Advances in Intelligent Systems and Computing (AISC 722), S. 180–190, Springer International Publishing, Cham, 2018, ISBN: 978-3-319-73888-8. Abstract | Links | BibTeX | Schlagwörter: Adaptive visualization, Human Factors, Intelligent Systems Burkhardt, Dirk; Pattan, Sachin; Nazemi, Kawa; Kuijper, Arjan Search Intention Analysis for Task- and User-Centered Visualization in Big Data Applications Artikel In: Procedia Computer Science, Bd. 104, S. 539 - 547, 2017, ISSN: 1877-0509, (ICTE 2016, Riga Technical University, Latvia). Abstract | Links | BibTeX | Schlagwörter: Information visualization, Intelligent Systems, User behavior, User Interactions, User Interface, User-centered design, Visual analytics Nazemi, Kawa Adaptive Semantics Visualization Buch Springer International Publishing, Studies in Computational Intelligence 646, 2016, ISBN: 978-3-319-30815-9. Abstract | Links | BibTeX | Schlagwörter: Adaptive visualization, Human Factors, Information visualization, Intelligent Systems, Visual analytics Nazemi, Kawa Adaptive Semantics Visualization Promotionsarbeit Technische Universität Darmstadt, 2014, (Reprint by Eugraphics Association (EG)). Abstract | Links | BibTeX | Schlagwörter: 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 Promotionsarbeit Technische Universität Darmstadt, 2014, (Department of Computer Science. Supervised by Dieter W. Fellner.). Abstract | Links | BibTeX | Schlagwörter: 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; 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 (Hrsg.): Proceedings of International Symposium on Visual Computing (ISVC 2014). Advances in Visual Computing., S. 872–883, Springer International Publishing, Cham, 2014, ISBN: 978-3-319-14364-4. Abstract | Links | BibTeX | Schlagwörter: 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 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 (Hrsg.): Proceeding of the International Symposium on Visual Computing (ISVC 2014). Advances in Visual Computing., S. 333–344, Springer International Publishing, Cham, 2014, ISBN: 978-3-319-14249-4. Abstract | Links | BibTeX | Schlagwörter: 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. (Hrsg.): 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 | Schlagwörter: 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 (Hrsg.): Proceedings of International Symposium on Visual Computing (ISVC 2013). Advances in Visual Computing., S. 13–24, Springer Berlin Heidelberg, Berlin, Heidelberg, 2013, ISBN: 978-3-642-41939-3. Abstract | Links | BibTeX | Schlagwörter: 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 Artikel In: IEEE Computer Graphics and Applications (CG&A), Bd. 32, Nr. 5, S. 84-89, 2012, ISSN: 0272-1716. Abstract | Links | BibTeX | Schlagwörter: 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 Nazemi, Kawa; Burkhardt, Dirk; Stab, Christian; Breyer, Matthias; Wichert, Reiner; Fellner, Dieter W Natural Gesture Interaction with Accelerometer-Based Devices in Ambient Assisted Environments Buchkapitel In: Wichert, Reiner; Eberhardt, Birgid (Hrsg.): S. 75–90, Springer Berlin Heidelberg, Berlin, Heidelberg, 2011, ISBN: 978-3-642-18167-2. Abstract | Links | BibTeX | Schlagwörter: Human-computer interaction (HCI), Intelligent Systems, Interaction Design, Interactive multimedia 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 (Hrsg.): Human Centered Design, S. 566–575, Springer Berlin Heidelberg, Berlin, Heidelberg, 2011, ISBN: 978-3-642-21753-1. Abstract | Links | BibTeX | Schlagwörter: 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 Nazemi, Kawa; Burkhardt, Dirk; Breyer, Matthias; Kuijper, Arjan Modeling Users for Adaptive Semantics Visualizations Proceedings Article In: Stephanidis, Constantine (Hrsg.): International Conference on Universal Access in Human-Computer Interaction. Universal Access in Human-Computer Interaction. Users Diversity. , S. 88–97, Springer Berlin Heidelberg, Berlin, Heidelberg, 2011, ISBN: 978-3-642-21663-3. Abstract | Links | BibTeX | Schlagwörter: Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Intelligent Systems, Interaction analysis, Interaction Design, User modeling Nazemi, Kawa; Breyer, Matthias; Stab, Christian; Burkhardt, Dirk; Fellner, Dieter W Intelligent Exploration System - an Approach for User-Centered Exploratory Learning Konferenz 2nd International Conference on Education and New Learning Technologies, 2010, ISBN: 978-84-613-9386-2. Abstract | Links | BibTeX | Schlagwörter: Exploration, Exploratory learning, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Intelligent tutoring systems, Knowledge discovery Burkhardt, Dirk; Nazemi, Kawa; Bhatti, Nadeem; Hornung, Christoph Technology Support for Analyzing User Interactions to Create User-Centered Interactions Buchkapitel In: Stephanidis, Constantine (Hrsg.): Universal Access in Human-Computer Interaction. Addressing Diversity: 5th International Conference, UAHCI 2009, San Diego, CA, USA, July 19-24, 2009. Proceedings, S. 3–12, Springer Berlin Heidelberg, Berlin, Heidelberg, 2009, ISBN: 978-3-642-02707-9. Abstract | Links | BibTeX | Schlagwörter: Adaptive visualization, Human Factors, Intelligent Systems, User Interactions, User Interface Hornung, Christoph; Granić, Andrina; Ćukušić, Maja; Nazemi, Kawa eKnowledge Repositories in eLearning 2.0: UNITE - a European-Wide Network of Schools Buchkapitel In: Li, F.; Zhao, J.; Shih, T. K; Lau, R.; Li, Q.; McLeod, D. (Hrsg.): Advances in Web Based Learning - ICWL 2008: 7th International Conference, Jinhua, China, August 20-22, 2008. Proceedings, S. 99–110, Springer Berlin Heidelberg, Berlin, Heidelberg, 2008, ISBN: 978-3-540-85033-5. Abstract | Links | BibTeX | Schlagwörter: Computer based learning, Intelligent Systems, Intelligent tutoring systems, reference model, Vocational training2019
@inproceedings{Nazemi_ISVC2019,
title = {A Visual Analytics Approach for Analyzing Technological Trends in Technology and Innovation Management},
author = {Kawa Nazemi and Dirk Burkhardt},
editor = {George Bebis and Richard Boyle and Bahram Parvin and Darko Koracin and Daniela Ushizima and Sek Chai and Shinjiro Sueda and Xin Lin and Aidong Lu and Daniel Thalmann and Chaoli Wang and Panpan Xu},
url = {https://rd.springer.com/chapter/10.1007/978-3-030-33723-0_23, Springer LNCS},
doi = {10.1007/978-3-030-33723-0_23},
isbn = {978-3-030-33723-0},
year = {2019},
date = {2019-10-09},
booktitle = {Advances in Visual Computing},
pages = {283--294},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {Visual Analytics provides with a combination of automated techniques and interactive visualizations huge analysis possibilities in technology and innovation management. Thereby not only the use of machine learning data mining methods plays an important role. Due to the high interaction capabilities, it provides a more user-centered approach, where users are able to manipulate the entire analysis process and get the most valuable information. Existing Visual Analytics systems for Trend Analytics and technology and innovation management do not really make use of this unique feature and almost neglect the human in the analysis process. Outcomes from research in information search, information visualization and technology management can lead to more sophisticated Visual Analytics systems that involved the human in the entire analysis process. We propose in this paper a new interaction approach for Visual Analytics in technology and innovation management with a special focus on technological trend analytics.},
keywords = {Artificial Intelligence, Data Analytics, Human Factors, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Machine Leanring, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
2018
@inbook{Nazemi2018,
title = {Intelligent Visual Analytics -- a Human-Adaptive Approach for Complex and Analytical Tasks},
author = {Kawa Nazemi},
editor = {W. Karwowski and T. Ahram},
url = {https://doi.org/10.1007/978-3-319-73888-8_29},
doi = {10.1007/978-3-319-73888-8_29},
isbn = {978-3-319-73888-8},
year = {2018},
date = {2018-01-01},
booktitle = {Intelligent Human Systems Integration: Proceedings of the International Conference on Intelligent Human Systems Integration (IHSI 2018): Integrating People and Intelligent Systems. Advances in Intelligent Systems and Computing (AISC 722)},
pages = {180--190},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {Visual Analytics enables solving complex and analytical tasks by combining automated data analytics methods and interactive visualizations. The complexity of tasks, the huge amount of data and the complex visual representation may overstrain the users of such systems. Intelligent and adaptive visualizations system show already promising results to bridge the gap between human and the complex visualization. We introduce in this paper a revised version of layer-based visual adaptation model that considers the human perception and cognition abilities. The model is then used to enhance the most popular Visual Analytics model to enable the development of Intelligent Visual Analytics systems.},
keywords = {Adaptive visualization, Human Factors, Intelligent Systems},
pubstate = {published},
tppubtype = {inbook}
}
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}
}
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}
}
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{Nazemi2014b,
title = {Adaptive Visualization of Linked-Data},
author = {Kawa Nazemi and Dirk Burkhardt and Reimond Retz and Arjan Kuijper and Jörn Kohlhammer},
editor = {George Bebis and Richard Boyle and Bahram Parvin and Darko Koracin and Ryan McMahan and Jason Jerald and Hui Zhang and Steven M Drucker and Chandra Kambhamettu and Maha El Choubassi and Zhigang Deng and Mark Carlson},
url = {https://link.springer.com/chapter/10.1007/978-3-319-14364-4_84, Springer link},
doi = {10.1007/978-3-319-14364-4_84},
isbn = {978-3-319-14364-4},
year = {2014},
date = {2014-03-01},
booktitle = {Proceedings of International Symposium on Visual Computing (ISVC 2014). Advances in Visual Computing.},
pages = {872--883},
publisher = {Springer International Publishing},
address = {Cham},
series = {LNCS 8888},
abstract = {Adaptive visualizations reduces the required cognitive effort to comprehend interactive visual pictures and amplify cognition. Although the research on adaptive visualizations grew in the last years, the existing approaches do not consider the transformation pipeline from data to visual representation for a more efficient and effective adaptation. Further todays systems commonly require an initial training by experts from the field and are limited to adaptation based either on user behavior or on data characteristics. A combination of both is not proposed to our knowledge. This paper introduces an enhanced instantiation of our previously proposed model that combines both: involving different influencing factors for and adapting various levels of visual peculiarities, on content, visual layout, visual presentation, and visual interface. Based on data type and users’ behavior, our system adapts a set of applicable visualization types. Moreover, retinal variables of each visualization type are adapted to meet individual or canonical requirements on both, data types and users’ behavior. Our system does not require an initial expert modeling.},
keywords = {Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Data Analytics, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, personalization, reference model, Semantic visualization, Semantic web, User behavior, User modeling, User-centered design, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Nazemi2014,
title = {Adaptive Visualization of Social Media Data for Policy Modeling},
author = {Kawa Nazemi and Dirk Burkhardt and Wilhelm Retz and Jörn Kohlhammer},
editor = {George Bebis and Richard Boyle and Bahram Parvin and Darko Koracin and Ryan McMahan and Jason Jerald and Hui Zhang and Steven M Drucker and Chandra Kambhamettu and Maha El Choubassi and Zhigang Deng and Mark Carlson},
url = {https://link.springer.com/chapter/10.1007/978-3-319-14249-4_32, Springer link},
doi = {10.1007/978-3-319-14249-4_32},
isbn = {978-3-319-14249-4},
year = {2014},
date = {2014-01-01},
booktitle = {Proceeding of the International Symposium on Visual Computing (ISVC 2014). Advances in Visual Computing.},
pages = {333--344},
publisher = {Springer International Publishing},
address = {Cham},
series = {LNCS 8887},
abstract = {The visual analysis of social media data emerged a huge number of interactive visual representations that use different characteristics of the data to enable the process of information acquisition. The social data are used in the domain of policy modeling to gather information about citizens' demands, opinions, and requirements and help to decide about political policies. Although existing systems already provide a huge number of visual analysis tools, the search and exploration paradigm is not really clear. Furthermore, the systems commonly do not provide any kind of human centered adaptation for the different stakeholders involved in the policy making process. In this paper, we introduce a novel approach that investigates the exploration and search paradigm from two different perspectives and enables a visual adaptation to support the exploration and analysis process.},
keywords = {Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Data Analytics, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, personalization, Semantic visualization, Semantic web, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
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}
}
2011
@inbook{Nazemi2011,
title = {Natural Gesture Interaction with Accelerometer-Based Devices in Ambient Assisted Environments},
author = {Kawa Nazemi and Dirk Burkhardt and Christian Stab and Matthias Breyer and Reiner Wichert and Dieter W Fellner},
editor = {Reiner Wichert and Birgid Eberhardt},
url = {https://doi.org/10.1007/978-3-642-18167-2_6
https://link.springer.com/chapter/10.1007/978-3-642-18167-2_6, Springer page},
doi = {10.1007/978-3-642-18167-2_6},
isbn = {978-3-642-18167-2},
year = {2011},
date = {2011-01-01},
pages = {75--90},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
abstract = {Using modern interaction methods and devices enables a more natural and intuitive interaction. Currently, only mobile phones and game consoles which are supporting such gesture-based interactions have good payment-rates. This comes along, that such devices will be bought not only by the traditional technical experienced consumers. The interaction with such devices becomes so easy, that also older people playing or working with them. Especially older people have more handicaps, so for them it is difficult to read small text, like they are used as description to buttons on remote controls for televisions. They also become fast overstrained, so that bigger technical systems are no help for them. If it is possible to interact with gestures, all these problems can be avoided. But to allow an intuitive and easy gesture interaction, gestures have to be supported, which are easy to understand. Because of that fact, in this paper we tried to identify intuitive gestures for common interaction scenarios on computer-based systems for uses in ambient assisted environment. In this evaluation, the users should commit their opinion of intuitive gestures for different presented scenarios/tasks. Basing on these results, intuitively useable systems can be developed, so that users are able to communicate with technical systems on more intuitive level using accelerometer-based devices.},
keywords = {Human-computer interaction (HCI), Intelligent Systems, Interaction Design, Interactive multimedia},
pubstate = {published},
tppubtype = {inbook}
}
@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{Nazemi2011d,
title = {Modeling Users for Adaptive Semantics Visualizations},
author = {Kawa Nazemi and Dirk Burkhardt and Matthias Breyer and Arjan Kuijper},
editor = {Constantine Stephanidis},
url = {https://link.springer.com/chapter/10.1007/978-3-642-21663-3_10, Springer link},
doi = {10.1007/978-3-642-21663-3_10},
isbn = {978-3-642-21663-3},
year = {2011},
date = {2011-01-01},
booktitle = {International Conference on Universal Access in Human-Computer Interaction. Universal Access in Human-Computer Interaction. Users Diversity. },
volume = {2},
pages = {88--97},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
series = {LNCS 6766},
abstract = {The automatic adaptation of information visualization systems to the requirements of users plays a key-role in today's research. Different approaches from both disciplines try to face this phenomenon. The modeling of user is an essential part of a user-centered adaptation of visualization. In this paper we introduce a new approach for modeling users especially for semantic visualization systems. The approach consists of a three dimensional model, where semantic data, user and visualization are set in relation in different abstraction layer.},
keywords = {Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Intelligent Systems, Interaction analysis, Interaction Design, User modeling},
pubstate = {published},
tppubtype = {inproceedings}
}
2010
@conference{C35-P-21397,
title = {Intelligent Exploration System - an Approach for User-Centered Exploratory Learning},
author = {Kawa Nazemi and Matthias Breyer and Christian Stab and Dirk Burkhardt and Dieter W Fellner},
editor = {International Association of Technology, Education and Development },
url = {https://library.iated.org/view/NAZEMI2010INT, direct link to publisher},
isbn = {978-84-613-9386-2},
year = {2010},
date = {2010-01-01},
booktitle = {2nd International Conference on Education and New Learning Technologies},
pages = {6476-6484},
abstract = {The following paper describes the conceptual design of an Intelligent Exploration System (IES) that offers a user-adapted graphical environment of web-based knowledge repositories, to support and optimize the explorative learning. The paper starts with a short definition of learning by exploring and introduces the Intelligent Tutoring System and Semantic Technologies for developing such an Intelligent Exploration System. The IES itself will be described with a short overview of existing learner or user analysis methods, visualization techniques for exploring knowledge with semantics technology and the explanation of the characteristics of adaptation to offer a more efficient learning environment.},
keywords = {Exploration, Exploratory learning, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Intelligent tutoring systems, Knowledge discovery},
pubstate = {published},
tppubtype = {conference}
}
2009
@inbook{Burkhardt2009,
title = {Technology Support for Analyzing User Interactions to Create User-Centered Interactions},
author = {Dirk Burkhardt and Kawa Nazemi and Nadeem Bhatti and Christoph Hornung},
editor = {Constantine Stephanidis},
url = {https://doi.org/10.1007/978-3-642-02707-9_1},
doi = {10.1007/978-3-642-02707-9_1},
isbn = {978-3-642-02707-9},
year = {2009},
date = {2009-01-01},
booktitle = {Universal Access in Human-Computer Interaction. Addressing Diversity: 5th International Conference, UAHCI 2009, San Diego, CA, USA, July 19-24, 2009. Proceedings},
pages = {3--12},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
series = {LNCS},
abstract = {Alternative interaction devices become more important in the communication between users and computers. Parallel graphical User Interfaces underlay a continuous development and research. But today does no adequate connection exist between these both aspects. So if a developer wants to provide an alternative access over more intuitive interaction devices, he has to implement this interaction-possibility on his own by regarding the users perception. A better way to avoid this time-consuming development-process is presented in this paper. This method can easy implement by a developer and users get the possibility to interact on intuitive way.},
keywords = {Adaptive visualization, Human Factors, Intelligent Systems, User Interactions, User Interface},
pubstate = {published},
tppubtype = {inbook}
}
2008
@inbook{Hornung2008,
title = {eKnowledge Repositories in eLearning 2.0: UNITE - a European-Wide Network of Schools},
author = {Christoph Hornung and Andrina Granić and Maja Ćukušić and Kawa Nazemi},
editor = {F. Li and J. Zhao and T. K Shih and R. Lau and Q. Li and D. McLeod},
url = {https://doi.org/10.1007/978-3-540-85033-5_11
https://link.springer.com/chapter/10.1007/978-3-540-85033-5_11},
doi = {10.1007/978-3-540-85033-5_11},
isbn = {978-3-540-85033-5},
year = {2008},
date = {2008-01-01},
booktitle = {Advances in Web Based Learning - ICWL 2008: 7th International Conference, Jinhua, China, August 20-22, 2008. Proceedings},
pages = {99--110},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
abstract = {The upcoming Web 2.0 technologies change the aspects of eLearning fundamentally. The traditional paradigm of classroom teaching and homework learning will develop further towards sharing experiences and knowledge in word-wide social communities. Moreover, knowledge capturing in ambient environments gains more and more importance. These aspects characterize the so-called eLearning 2.0. This paper describes a prototype of an eLearning 2.0 system covering the different aspects such as platform, pedagogy and scenarios. The concepts presented here have been applied in the EU-project UNITE. The implementation of this system in the setting of a European network of fourteen schools is presented as an iterative four stage process, covering scenario planning and implementation, validation in addition to platform and process improvement. Achieved intermediate results from the first iteration of the implementation process are discussed and future work is presented.},
keywords = {Computer based learning, Intelligent Systems, Intelligent tutoring systems, reference model, Vocational training},
pubstate = {published},
tppubtype = {inbook}
}