Publications
Burkhardt, Dirk; Nazemi, Kawa; Ginters, Egils Process Support and Visual Adaptation to Assist Visual Trend Analytics in Managing Transportation Innovations Proceedings Article In: Ginters, Egils; Estrada, Mario Arturo Ruiz; Eroles, Miquel Angel Piera (Ed.): ICTE in Transportation and Logistics 2019, pp. 319–327, Springer International Publishing. Lecture Notes in Intelligent Transportation and Infrastructure, Cham, 2020, ISBN: 978-3-030-39688-6. Abstract | Links | BibTeX | Tags: Human Factors, Human-computer interaction (HCI), Mobility, personalization, Process Support, Process-Mining, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics Burkhardt, Dirk; Pattan, Sachin; Nazemi, Kawa; Kuijper, Arjan Search Intention Analysis for Task- and User-Centered Visualization in Big Data Applications Journal Article In: Procedia Computer Science, vol. 104, pp. 539 - 547, 2017, ISSN: 1877-0509, (ICTE 2016, Riga Technical University, Latvia). Abstract | Links | BibTeX | Tags: Information visualization, Intelligent Systems, User behavior, User Interactions, User Interface, User-centered design, Visual analytics Nazemi, Kawa; Burkhardt, Dirk; Kuijper, Arjan Analyzing the Information Search Behavior and Intentions in Visual Information Systems Journal Article In: Journal of Computer Science Technology Updates, vol. 4, 2017. Abstract | Links | BibTeX | Tags: Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics Burkhardt, Dirk; Nazemi, Kawa; Ginters, Egils; Aizstrauts, Artis; Kohlhammer, Jörn Explorative Visualization of Impact Analysis for Policy Modeling by Bonding Open Government and Simulation Data Proceedings Article In: Yamamoto, Sakae (Ed.): International Conference on Human Interface and the Management of Information (HIMI 2015). Information and Knowledge Design., pp. 34–45, Springer International Publishing, Cham, 2015, ISBN: 978-3-319-20612-7. Abstract | Links | BibTeX | Tags: Exploration, Semantics visualization, Simulation, User behavior, User Interactions, User Interface, User-centered design, Visual analytics Nazemi, Kawa Adaptive Semantics Visualization PhD Thesis Technische Universität Darmstadt, 2014, (Reprint by Eugraphics Association (EG)). Abstract | Links | BibTeX | Tags: Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Computer based learning, Data Analytics, E-Learning, Exploratory learning, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, Ontology visualization, personalization, Policy modeling, reference model, Semantic data modeling, Semantic visualization, Semantic web, Semantics visualization, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics Nazemi, Kawa Adaptive Semantics Visualization PhD Thesis Technische Universität Darmstadt, 2014, (Department of Computer Science. Supervised by Dieter W. Fellner.). Abstract | Links | BibTeX | Tags: Adaptive information visualization, Adaptive user interfaces, Computer based learning, Data Analytics, eGovernance, Exploratory learning, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction Design, Ontology visualization, personalization, Policy modeling, Semantic data modeling, Semantic visualization, Semantic web, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics Nazemi, Kawa; Retz, Wilhelm; Kohlhammer, Jörn; Kuijper, Arjan User Similarity and Deviation Analysis for Adaptive Visualizations Proceedings Article In: Yamamoto, Sakae (Ed.): International Conference on Human Interface and the Management of Information (HMI 2014). Human Interface and the Management of Information. Information and Knowledge Design and Evaluation., pp. 64–75, Springer International Publishing , Cham, 2014, ISBN: 978-3-319-07731-7. Abstract | Links | BibTeX | Tags: Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Data Analytics, reference model, Semantic visualization, Semantics visualization, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics Nazemi, Kawa; Burkhardt, Dirk; Retz, Wilhelm; Kohlhammer, Jörn Adaptive Visualization of Social Media Data for Policy Modeling Proceedings Article In: Bebis, George; Boyle, Richard; Parvin, Bahram; Koracin, Darko; McMahan, Ryan; Jerald, Jason; Zhang, Hui; Drucker, Steven M; Kambhamettu, Chandra; Choubassi, Maha El; Deng, Zhigang; Carlson, Mark (Ed.): Proceeding of the International Symposium on Visual Computing (ISVC 2014). Advances in Visual Computing., pp. 333–344, Springer International Publishing, Cham, 2014, ISBN: 978-3-319-14249-4. Abstract | Links | BibTeX | Tags: Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Data Analytics, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, personalization, Semantic visualization, Semantic web, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics Nazemi, Kawa; Kuijper, Arjan; Hutter, Marco; Kohlhammer, Jörn; Fellner, Dieter W Measuring Context Relevance for Adaptive Semantics Visualizations Proceedings Article In: Proceedings of the 14th International Conference on Knowledge Technologies and Data-driven Business, pp. 14:1–14:8, ACM, Graz, Austria, 2014, ISBN: 978-1-4503-2769-5, (Honourable Mention). Abstract | Links | BibTeX | Tags: Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Data Analytics, data weighting, Information retrieval, semantic processing, Semantic web, Semantics visualization, User Interface, User modeling, Visual analytics Burkhardt, Dirk; 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 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 Burkhardt, Dirk; Nazemi, Kawa; Bhatti, Nadeem; Hornung, Christoph Technology Support for Analyzing User Interactions to Create User-Centered Interactions Book Chapter In: Stephanidis, Constantine (Ed.): Universal Access in Human-Computer Interaction. Addressing Diversity: 5th International Conference, UAHCI 2009, San Diego, CA, USA, July 19-24, 2009. Proceedings, pp. 3–12, Springer Berlin Heidelberg, Berlin, Heidelberg, 2009, ISBN: 978-3-642-02707-9. Abstract | Links | BibTeX | Tags: Adaptive visualization, Human Factors, Intelligent Systems, User Interactions, User Interface Nazemi, Kawa; Ullmann, Thomas Daniel; Hornung, Christoph Engineering User Centered Interaction Systems for Semantic Visualizations Book Chapter In: Stephanidis, Constantine (Ed.): Universal Access in Human-Computer Interaction. Addressing Diversity: 5th International Conference, UAHCI 2009, San Diego, CA, USA, July 19-24, 2009. Proceedings, Part I, pp. 126–134, Springer Berlin Heidelberg, Berlin, Heidelberg, 2009, ISBN: 978-3-642-02707-9. Abstract | Links | BibTeX | Tags: Semantics visualization, User Interactions, User Interface, User-centered design2020
@inproceedings{10.1007/978-3-030-39688-6_40,
title = {Process Support and Visual Adaptation to Assist Visual Trend Analytics in Managing Transportation Innovations},
author = {Dirk Burkhardt and Kawa Nazemi and Egils Ginters},
editor = {Egils Ginters and Mario Arturo Ruiz Estrada and Miquel Angel Piera Eroles},
url = {https://rd.springer.com/chapter/10.1007%2F978-3-030-39688-6_40, Springer },
doi = {10.1007/978-3-030-39688-6_40},
isbn = {978-3-030-39688-6},
year = {2020},
date = {2020-01-30},
booktitle = {ICTE in Transportation and Logistics 2019},
pages = {319--327},
publisher = {Springer International Publishing. Lecture Notes in Intelligent Transportation and Infrastructure},
address = {Cham},
abstract = {In the domain of mobility and logistics, a variety of new technologies and business ideas are arising. Beside technologies that aim on ecologically and economic transportation, such as electric engines, there are also fundamental different approaches like central packaging stations or deliveries via drones. Yet, there is a growing need for analytical systems that enable identifying new technologies, innovations, business models etc. and give also the opportunity to rate those in perspective of business relevance. Commonly adaptive systems investigate only the users' behavior, while a process-related supports could assist to solve an analytical task more efficient and effective. In this article an approach that enables non-experts to perform visual trend analysis through an advanced process support based on process mining is described. This allow us to calculate a process model based on events, which is the baseline for process support feature calculation. These features and the process model enable to assist non-expert users in complex analytical tasks.},
keywords = {Human Factors, Human-computer interaction (HCI), Mobility, personalization, Process Support, Process-Mining, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
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}
}
@article{Nazemi2017,
title = {Analyzing the Information Search Behavior and Intentions in Visual Information Systems},
author = {Kawa Nazemi and Dirk Burkhardt and Arjan Kuijper},
url = {https://www.cosmosscholars.com/images/JCSTU/JCSTU-V4N2A2-Nazemi.pdf, full text},
doi = {10.15379/2410-2938.2017.04.02.02},
year = {2017},
date = {2017-01-01},
journal = {Journal of Computer Science Technology Updates},
volume = {4},
abstract = {Visual information search systems support different search approaches such as targeted, exploratory or analytical search. Those visual systems deal with the challenge of composing optimal initial result visualization sets that face the search intention and respond to the search behavior of users. The diversity of these kinds of search tasks require different sets of visual layouts and functionalities, e.g. to filter, thrill-down or even analyze concrete data properties. This paper describes a new approach to calculate the probability towards the three mentioned search intentions, derived from users’ behavior. The implementation is realized as a web-service, which is included in a visual environment that is designed to enable various search strategies based on heterogeneous data sources. In fact, based on an entered search query our developed search intention analysis web-service calculates the most probable search task, and our visualization system initially shows the optimal result set of visualizations to solve the task. The main contribution of this paper is a probability-based approach to derive the users’ search intentions based on the search behavior enhanced by the application to a visual system.},
keywords = {Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics},
pubstate = {published},
tppubtype = {article}
}
2015
@inproceedings{10.1007/978-3-319-20612-7_4,
title = {Explorative Visualization of Impact Analysis for Policy Modeling by Bonding Open Government and Simulation Data},
author = {Dirk Burkhardt and Kawa Nazemi and Egils Ginters and Artis Aizstrauts and Jörn Kohlhammer},
editor = {Sakae Yamamoto},
url = {https://link.springer.com/chapter/10.1007/978-3-319-20612-7_4. Springer Link},
doi = {doi.org/10.1007/978-3-319-20612-7_4},
isbn = {978-3-319-20612-7},
year = {2015},
date = {2015-03-01},
booktitle = {International Conference on Human Interface and the Management of Information (HIMI 2015). Information and Knowledge Design.},
pages = {34--45},
publisher = {Springer International Publishing},
address = {Cham},
series = {LNCS 9172},
abstract = {Problem identification and solution finding are major challenges in policy modeling. Statistical indicator-data build the foundation for most of the required analysis work. In particular finding effective and efficient policies that solve an existing political problem is critical, since the forecast validation of the effectiveness is quite difficult. Simulation technologies can help to identify optimal policies for solutions, but nowadays many of such simulators are stand-alone technologies. In this paper we introduce a new visualization approach to enable the coupling of statistical indicator data from Open Government Data sources with simulators and especially simulation result data with the goal to provide an enhanced impact analysis for political analysts and decision makers. This allows, amongst others a more intuitive and effective way of solution finding.},
keywords = {Exploration, Semantics visualization, Simulation, User behavior, User Interactions, User Interface, User-centered design, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
2014
@phdthesis{Nazemi2014f,
title = {Adaptive Semantics Visualization},
author = {Kawa Nazemi},
url = {https://diglib.eg.org/handle/10.2312/12076, EG Lib
https://diglib.eg.org/bitstream/handle/10.2312/12076/nazemi.pdf, full text},
doi = {10.2312/12076},
year = {2014},
date = {2014-11-27},
school = {Technische Universität Darmstadt},
abstract = {Human access to the increasing amount of information and data plays an essential role for the professional level and also for everyday life. While information visualization has developed new and remarkable ways for visualizing data and enabling the exploration process, adaptive systems focus on users' behavior to tailor information for supporting the information acquisition process. Recent research on adaptive visualization shows promising ways of synthesizing these two complementary approaches and make use of the surpluses of both disciplines. The emerged methods and systems aim to increase the performance, acceptance, and user experience of graphical data representations for a broad range of users. Although the evaluation results of the recently proposed systems are promising, some important aspects of information visualization are not considered in the adaptation process. The visual adaptation is commonly limited to change either visual parameters or replace visualizations entirely. Further, no existing approach adapts the visualization based on data and user characteristics. Other limitations of existing approaches include the fact that the visualizations require training by experts in the field.
In this thesis, we introduce a novel model for adaptive visualization. In contrast to existing approaches, we have focused our investigation on the potentials of information visualization for adaptation. Our reference model for visual adaptation not only considers the entire transformation, from data to visual representation, but also enhances it to meet the requirements for visual adaptation. Our model adapts different visual layers that were identified based on various models and studies on human visual perception and information processing. In its adaptation process, our conceptual model considers the impact of both data and user on visualization adaptation. We investigate different approaches and models and their effects on system adaptation to gather implicit information about users and their behavior. These are than transformed and applied to affect the visual representation and model human interaction behavior with visualizations and data to achieve a more appropriate visual adaptation. Our enhanced user model further makes use of the semantic hierarchy to enable a domain-independent adaptation.
To face the problem of a system that requires to be trained by experts, we introduce the canonical user model that models the average usage behavior with the visualization environment. Our approach learns from the behavior of the average user to adapt the different visual layers and transformation steps. This approach is further enhanced with similarity and deviation analysis for individual users to determine similar behavior on an individual level and identify differing behavior from the canonical model. Users with similar behavior get similar visualization and data recommendations, while behavioral anomalies lead to a lower level of adaptation. Our model includes a set of various visual layouts that can be used to compose a multi-visualization interface, a sort of "visualization cockpit". This model facilitates various visual layouts to provide different perspectives and enhance the ability to solve difficult and exploratory search challenges. Data from different data-sources can be visualized and compared in a visual manner. These different visual perspectives on data can be chosen by users or can be automatically selected by the system.
This thesis further introduces the implementation of our model that includes additional approaches for an efficient adaptation of visualizations as proof of feasibility. We further conduct a comprehensive user study that aims to prove the benefits of our model and underscore limitations for future work. The user study with overall 53 participants focuses with its four conditions on our enhanced reference model to evaluate the adaptation effects of the different visual layers.},
note = {Reprint by Eugraphics Association (EG)},
keywords = {Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Computer based learning, Data Analytics, E-Learning, Exploratory learning, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, Ontology visualization, personalization, Policy modeling, reference model, Semantic data modeling, Semantic visualization, Semantic web, Semantics visualization, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics},
pubstate = {published},
tppubtype = {phdthesis}
}
In this thesis, we introduce a novel model for adaptive visualization. In contrast to existing approaches, we have focused our investigation on the potentials of information visualization for adaptation. Our reference model for visual adaptation not only considers the entire transformation, from data to visual representation, but also enhances it to meet the requirements for visual adaptation. Our model adapts different visual layers that were identified based on various models and studies on human visual perception and information processing. In its adaptation process, our conceptual model considers the impact of both data and user on visualization adaptation. We investigate different approaches and models and their effects on system adaptation to gather implicit information about users and their behavior. These are than transformed and applied to affect the visual representation and model human interaction behavior with visualizations and data to achieve a more appropriate visual adaptation. Our enhanced user model further makes use of the semantic hierarchy to enable a domain-independent adaptation.
To face the problem of a system that requires to be trained by experts, we introduce the canonical user model that models the average usage behavior with the visualization environment. Our approach learns from the behavior of the average user to adapt the different visual layers and transformation steps. This approach is further enhanced with similarity and deviation analysis for individual users to determine similar behavior on an individual level and identify differing behavior from the canonical model. Users with similar behavior get similar visualization and data recommendations, while behavioral anomalies lead to a lower level of adaptation. Our model includes a set of various visual layouts that can be used to compose a multi-visualization interface, a sort of "visualization cockpit". This model facilitates various visual layouts to provide different perspectives and enhance the ability to solve difficult and exploratory search challenges. Data from different data-sources can be visualized and compared in a visual manner. These different visual perspectives on data can be chosen by users or can be automatically selected by the system.
This thesis further introduces the implementation of our model that includes additional approaches for an efficient adaptation of visualizations as proof of feasibility. We further conduct a comprehensive user study that aims to prove the benefits of our model and underscore limitations for future work. The user study with overall 53 participants focuses with its four conditions on our enhanced reference model to evaluate the adaptation effects of the different visual layers.@phdthesis{Nazemi2014g,
title = {Adaptive Semantics Visualization},
author = {Kawa Nazemi},
url = {https://tuprints.ulb.tu-darmstadt.de/id/eprint/4319, TU Darmstadt Prints
https://tuprints.ulb.tu-darmstadt.de/4319/1/Nazemi_Diss.pdf, full text},
year = {2014},
date = {2014-11-23},
address = {Darmstadt, Germany},
school = {Technische Universität Darmstadt},
abstract = {Human access to the increasing amount of information and data plays an essential role for the professional level and also for everyday life. While information visualization has developed new and remarkable ways for visualizing data and enabling the exploration process, adaptive systems focus on users’ behavior to tailor information for supporting the information acquisition process. Recent research on adaptive visualization shows promising ways of synthesizing these two complementary approaches and make use of the surpluses of both disciplines. The emerged methods and systems aim to increase the performance, acceptance, and user experience of graphical data representations for a broad range of users. Although the evaluation results of the recently proposed systems are promising, some important aspects of information visualization are not considered in the adaptation process. The visual adaptation is commonly limited to change either visual parameters or replace visualizations entirely. Further, no existing approach adapts the visualization based on data and user characteristics. Other limitations of existing approaches include the fact that the visualizations require training by experts in the field.
In this thesis, we introduce a novel model for adaptive visualization. In contrast to existing approaches, we have focused our investigation on the potentials of information visualization for adaptation. Our reference model for visual adaptation not only considers the entire transformation, from data to visual representation, but also enhances it to meet the requirements for visual adaptation. Our model adapts different visual layers that were identified based on various models and studies on human visual perception and information processing. In its adaptation process, our conceptual model considers the impact of both data and user on visualization adaptation. We investigate different approaches and models and their effects on system adaptation to gather implicit information about users and their behavior. These are than transformed and applied to affect the visual representation and model human interaction behavior with visualizations and data to achieve a more appropriate visual adaptation. Our enhanced user model further makes use of the semantic hierarchy to enable a domain-independent adaptation.
To face the problem of a system that requires to be trained by experts, we introduce the canonical user model that models the average usage behavior with the visualization environment. Our approach learns from the behavior of the average user to adapt the different visual layers and transformation steps. This approach is further enhanced with similarity and deviation analysis for individual users to determine similar behavior on an individual level and identify differing behavior from the canonical model. Users with similar behavior get similar visualization and data recommendations, while behavioral anomalies lead to a lower level of adaptation. Our model includes a set of various visual layouts that can be used to compose a multi-visualization interface, a sort of "‘visualization cockpit"’. This model facilitates various visual layouts to provide different perspectives and enhance the ability to solve difficult and exploratory search challenges. Data from different data-sources can be visualized and compared in a visual manner. These different visual perspectives on data can be chosen by users or can be automatically selected by the system.
This thesis further introduces the implementation of our model that includes additional approaches for an efficient adaptation of visualizations as proof of feasibility. We further conduct a comprehensive user study that aims to prove the benefits of our model and underscore limitations for future work. The user study with overall 53 participants focuses with its four conditions on our enhanced reference model to evaluate the adaptation effects of the different visual layers.},
note = {Department of Computer Science. Supervised by Dieter W. Fellner.},
keywords = {Adaptive information visualization, Adaptive user interfaces, Computer based learning, Data Analytics, eGovernance, Exploratory learning, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction Design, Ontology visualization, personalization, Policy modeling, Semantic data modeling, Semantic visualization, Semantic web, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics},
pubstate = {published},
tppubtype = {phdthesis}
}
In this thesis, we introduce a novel model for adaptive visualization. In contrast to existing approaches, we have focused our investigation on the potentials of information visualization for adaptation. Our reference model for visual adaptation not only considers the entire transformation, from data to visual representation, but also enhances it to meet the requirements for visual adaptation. Our model adapts different visual layers that were identified based on various models and studies on human visual perception and information processing. In its adaptation process, our conceptual model considers the impact of both data and user on visualization adaptation. We investigate different approaches and models and their effects on system adaptation to gather implicit information about users and their behavior. These are than transformed and applied to affect the visual representation and model human interaction behavior with visualizations and data to achieve a more appropriate visual adaptation. Our enhanced user model further makes use of the semantic hierarchy to enable a domain-independent adaptation.
To face the problem of a system that requires to be trained by experts, we introduce the canonical user model that models the average usage behavior with the visualization environment. Our approach learns from the behavior of the average user to adapt the different visual layers and transformation steps. This approach is further enhanced with similarity and deviation analysis for individual users to determine similar behavior on an individual level and identify differing behavior from the canonical model. Users with similar behavior get similar visualization and data recommendations, while behavioral anomalies lead to a lower level of adaptation. Our model includes a set of various visual layouts that can be used to compose a multi-visualization interface, a sort of "‘visualization cockpit"’. This model facilitates various visual layouts to provide different perspectives and enhance the ability to solve difficult and exploratory search challenges. Data from different data-sources can be visualized and compared in a visual manner. These different visual perspectives on data can be chosen by users or can be automatically selected by the system.
This thesis further introduces the implementation of our model that includes additional approaches for an efficient adaptation of visualizations as proof of feasibility. We further conduct a comprehensive user study that aims to prove the benefits of our model and underscore limitations for future work. The user study with overall 53 participants focuses with its four conditions on our enhanced reference model to evaluate the adaptation effects of the different visual layers.@inproceedings{Nazemi2014e,
title = {User Similarity and Deviation Analysis for Adaptive Visualizations},
author = {Kawa Nazemi and Wilhelm Retz and Jörn Kohlhammer and Arjan Kuijper},
editor = {Sakae Yamamoto},
url = {https://link.springer.com/chapter/10.1007/978-3-319-07731-4_7, Springer link},
doi = {10.1007/978-3-319-07731-4_7},
isbn = {978-3-319-07731-7},
year = {2014},
date = {2014-08-01},
booktitle = {International Conference on Human Interface and the Management of Information (HMI 2014). Human Interface and the Management of Information. Information and Knowledge Design and Evaluation.},
pages = {64--75},
publisher = {Springer International Publishing },
address = {Cham},
series = {LNCS 8521},
abstract = {Adaptive visualizations support users in information acquisition and exploration and therewith in human access of data. Their adaptation effect is often based on approaches that require the training by an expert. Further the effects often aims to support just the individual aptitudes. This paper introduces an approach for modeling a canonical user that makes the predefined training-files dispensable and enables an adaptation of visualizations for the majority of users. With the introduced user deviation algorithm, the behavior of individuals can be compared to the average user behavior represented in the canonical user model to identify behavioral anomalies. The further introduced similarity measurements allow to cluster similar deviated behavioral patterns as groups and provide them effective visual adaptations.},
keywords = {Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Data Analytics, reference model, Semantic visualization, Semantics visualization, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Nazemi2014,
title = {Adaptive Visualization of Social Media Data for Policy Modeling},
author = {Kawa Nazemi and Dirk Burkhardt and Wilhelm Retz and Jörn Kohlhammer},
editor = {George Bebis and Richard Boyle and Bahram Parvin and Darko Koracin and Ryan McMahan and Jason Jerald and Hui Zhang and Steven M Drucker and Chandra Kambhamettu and Maha El Choubassi and Zhigang Deng and Mark Carlson},
url = {https://link.springer.com/chapter/10.1007/978-3-319-14249-4_32, Springer link},
doi = {10.1007/978-3-319-14249-4_32},
isbn = {978-3-319-14249-4},
year = {2014},
date = {2014-01-01},
booktitle = {Proceeding of the International Symposium on Visual Computing (ISVC 2014). Advances in Visual Computing.},
pages = {333--344},
publisher = {Springer International Publishing},
address = {Cham},
series = {LNCS 8887},
abstract = {The visual analysis of social media data emerged a huge number of interactive visual representations that use different characteristics of the data to enable the process of information acquisition. The social data are used in the domain of policy modeling to gather information about citizens' demands, opinions, and requirements and help to decide about political policies. Although existing systems already provide a huge number of visual analysis tools, the search and exploration paradigm is not really clear. Furthermore, the systems commonly do not provide any kind of human centered adaptation for the different stakeholders involved in the policy making process. In this paper, we introduce a novel approach that investigates the exploration and search paradigm from two different perspectives and enables a visual adaptation to support the exploration and analysis process.},
keywords = {Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Data Analytics, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, personalization, Semantic visualization, Semantic web, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Nazemi:2014:MCR:2637748.2638416,
title = {Measuring Context Relevance for Adaptive Semantics Visualizations},
author = {Kawa Nazemi and Arjan Kuijper and Marco Hutter and Jörn Kohlhammer and Dieter W Fellner},
url = {https://doi.acm.org/10.1145/2637748.2638416, ACM DL},
doi = {10.1145/2637748.2638416},
isbn = {978-1-4503-2769-5},
year = {2014},
date = {2014-01-01},
booktitle = {Proceedings of the 14th International Conference on Knowledge Technologies and Data-driven Business},
pages = {14:1--14:8},
publisher = {ACM},
address = {Graz, Austria},
series = {i-KNOW '14},
abstract = {Semantics visualizations enable the acquisition of information to amplify the acquisition of knowledge. The dramatic increase of semantics in form of Linked Data and Linked-Open Data yield search databases that allow to visualize the entire context of search results. The visualization of this semantic context enables one to gather more information at once, but the complex structures may as well confuse and frustrate users. To overcome the problems, adaptive visualizations already provide some useful methods to adapt the visualization on users' demands and skills. Although these methods are very promising, these systems do not investigate the relevance of semantic neighboring entities that commonly build most information value. We introduce two new measurements for the relevance of neighboring entities: The Inverse Instance Frequency allows weighting the relevance of semantic concepts based on the number of their instances. The Direct Relation Frequency inverse Relations Frequency measures the relevance of neighboring instances by the type of semantic relations. Both measurements provide a weighting of neighboring entities of a selected semantic instance, and enable an adaptation of retinal variables for the visualized graph. The algorithms can easily be integrated into adaptive visualizations and enhance them with the relevance measurement of neighboring semantic entities. We give a detailed description of the algorithms to enable a replication for the adaptive and semantics visualization community. With our method, one can now easily derive the relevance of neighboring semantic entities of selected instances, and thus gain more information at once, without confusing and frustrating users.},
note = {Honourable Mention},
keywords = {Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Data Analytics, data weighting, Information retrieval, semantic processing, Semantic web, Semantics visualization, User Interface, User modeling, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
2012
@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{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}
}
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}
}
@inbook{Nazemi2009b,
title = {Engineering User Centered Interaction Systems for Semantic Visualizations},
author = {Kawa Nazemi and Thomas Daniel Ullmann and Christoph Hornung},
editor = {Constantine Stephanidis},
url = {https://doi.org/10.1007/978-3-642-02707-9_14},
doi = {10.1007/978-3-642-02707-9_14},
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, Part I},
pages = {126--134},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
abstract = {For intuitive interaction with semantic visualizations, gesture-based interaction seems a promising way. However, the development of such ensembles is costly. To cut down the engineering effort, we propose a development model for interaction systems with semantic visualizations. In addition, we provide a set of evaluation tools to support the interaction developer engineer evaluating the engineering process.},
keywords = {Semantics visualization, User Interactions, User Interface, User-centered design},
pubstate = {published},
tppubtype = {inbook}
}