Publications - Human-Computer Interaction & Visual Analytics Reasearch Department (vis) at Darmstadt University of Applied Sciences (h_da)

PublicationsDirk Burkhardt2023-05-25T12:12:30+02:00

Publications

2014
14.	Kawa Nazemi 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 @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} } Close 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. Close EG Lib full text doi:10.2312/12076 Close
13.	Kawa Nazemi 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 @phdthesis{Nazemi2014g, title = {Adaptive Semantics Visualization}, author = {Kawa Nazemi}, url = {http://tuprints.ulb.tu-darmstadt.de/id/eprint/4319, TU Darmstadt Prints http://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} } Close 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. Close TU Darmstadt Prints full text Close
12.	Kawa Nazemi; Wilhelm Retz; Jörn Kohlhammer; Arjan Kuijper User Similarity and Deviation Analysis for Adaptive Visualizations Inproceedings In: Sakae Yamamoto (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 @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} } Close 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. Close Springer link doi:10.1007/978-3-319-07731-4_7 Close
11.	Kawa Nazemi; Dirk Burkhardt; Reimond Retz; Arjan Kuijper; Jörn Kohlhammer Adaptive Visualization of Linked-Data Inproceedings In: George Bebis; Richard Boyle; Bahram Parvin; Darko Koracin; Ryan McMahan; Jason Jerald; Hui Zhang; Steven M Drucker; Chandra Kambhamettu; Maha El Choubassi; Zhigang Deng; Mark Carlson (Ed.): Proceedings of International Symposium on Visual Computing (ISVC 2014). Advances in Visual Computing., pp. 872–883, Springer International Publishing, Cham, 2014, ISBN: 978-3-319-14364-4. Abstract \| Links \| BibTeX \| Tags: Adaptive Information Visualization, Adaptive User Interfaces, Adaptive Visualization, Data Analytics, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, personalization, reference model, Semantic visualization, Semantic web, User behavior, User modeling, User-centered design, Visual analytics @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} } Close 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. Close Springer link doi:10.1007/978-3-319-14364-4_84 Close
10.	Kawa Nazemi; Dirk Burkhardt; Wilhelm Retz; Jörn Kohlhammer Adaptive Visualization of Social Media Data for Policy Modeling Inproceedings In: George Bebis; Richard Boyle; Bahram Parvin; Darko Koracin; Ryan McMahan; Jason Jerald; Hui Zhang; Steven M Drucker; Chandra Kambhamettu; Maha El Choubassi; Zhigang Deng; Mark Carlson (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 @inproceedings{Nazemi2014g, 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} } Close 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. Close Springer link doi:10.1007/978-3-319-14249-4_32 Close
9.	Kawa Nazemi; Arjan Kuijper; Marco Hutter; Jörn Kohlhammer; Dieter W. Fellner Measuring Context Relevance for Adaptive Semantics Visualizations Inproceedings 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 @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 = {http://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} } Close 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. Close ACM DL doi:10.1145/2637748.2638416 Close
8.	Dirk Burkhardt; Kawa Nazemi; Jose Daniel Encarnacao; Wilhelm Retz; Jörn Kohlhammer Visualization Adaptation Based on Environmental Influencing Factors Inproceedings In: Masaaki Kurosu (Ed.): International Conference on Human-Computer (HCI 2014). Human-Computer Interaction. Theories, Methods, and Tools., pp. 411–422, Springer International Publishing, Cham, 2014, ISBN: 978-3-319-07233-3. Abstract \| Links \| BibTeX \| Tags: Adaptive Information Visualization, Adaptive User Interfaces, User modeling, User-centered design, Visual analytics @inproceedings{Burkhardt2014f, title = {Visualization Adaptation Based on Environmental Influencing Factors}, author = {Dirk Burkhardt and Kawa Nazemi and Jose Daniel Encarnacao and Wilhelm Retz and Jörn Kohlhammer}, editor = {Masaaki Kurosu}, url = {https://link.springer.com/chapter/10.1007/978-3-319-07233-3_38, Springer link}, isbn = {978-3-319-07233-3}, year = {2014}, date = {2014-01-01}, booktitle = {International Conference on Human-Computer (HCI 2014). Human-Computer Interaction. Theories, Methods, and Tools.}, pages = {411--422}, publisher = {Springer International Publishing}, address = {Cham}, series = {LNCS 8510}, abstract = {Working effectively with computer-based devices is challenging, especially under mobile conditions, due to the various environmental influences. In this paper a visualization adaptation approach is described, to support the user under discriminatory environmental conditions. For this purpose, a context model for environmental influencing factors is being defined. Based on this context model, an approach to adapt visualizations in regards of certain environmental influences is being evolved, such as the light intensity, air quality, or heavy vibrations.}, keywords = {Adaptive Information Visualization, Adaptive User Interfaces, User modeling, User-centered design, Visual analytics}, pubstate = {published}, tppubtype = {inproceedings} } Close Working effectively with computer-based devices is challenging, especially under mobile conditions, due to the various environmental influences. In this paper a visualization adaptation approach is described, to support the user under discriminatory environmental conditions. For this purpose, a context model for environmental influencing factors is being defined. Based on this context model, an approach to adapt visualizations in regards of certain environmental influences is being evolved, such as the light intensity, air quality, or heavy vibrations. Close Springer link Close
2013
7.	Kawa Nazemi; Jörn Kohlhammer Visual Variables in Adaptive Visualizations. Inproceedings In: Shlomo Berkovsky; Eelco Herder; Pasquale Lops; Olga C Santos (Ed.): 21st Conference on User Modeling, Adaptation, and Personalization. UMAP 2013 Extended Proceedings. Proceeding of 1st International Workshop on User-Adaptive Visualizations., CEUR Workshop Proceedings, Rome, Italy,, 2013, ISSN: 1613-0073. Abstract \| Links \| BibTeX \| Tags: Adaptive Information Visualization, Adaptive User Interfaces, Adaptive Visualization, Human Factors, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, Semantic visualization @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 = {http://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} } Close 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. Close full text Close
6.	Kawa Nazemi; Reimond Retz; Jürgen Bernard; Jörn Kohlhammer; Dieter W. Fellner Adaptive Semantic Visualization for Bibliographic Entries Inproceedings In: George Bebis; Richard Boyle; Bahram Parvin; Darko Koracin; Baoxin Li; Fatih Porikli; Victor Zordan; James Klosowski; Sabine Coquillart; Xun Luo; Min Chen; David Gotz (Ed.): Proceedings of International Symposium on Visual Computing (ISVC 2013). Advances in Visual Computing., pp. 13–24, Springer Berlin Heidelberg, Berlin, Heidelberg, 2013, ISBN: 978-3-642-41939-3. Abstract \| Links \| BibTeX \| Tags: Adaptive Information Visualization, Adaptive User Interfaces, Adaptive Visualization, Data Analytics, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, personalization, Semantic visualization, Semantic web, User behavior, User Interactions, Visual analytics @inproceedings{Nazemi2013b, title = {Adaptive Semantic Visualization for Bibliographic Entries}, author = {Kawa Nazemi and Reimond Retz and Jürgen Bernard and Jörn Kohlhammer and Dieter W. 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} } Close 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. Close Springer link doi:10.1007/978-3-642-41939-3_2 Close
2011
5.	Dirk Burkhardt; Matthias Breyer; Kawa Nazemi; Arjan Kuijper Search Intention Analysis for User-Centered Adaptive Visualizations Conference Universal Access in Human-Computer Interaction. Design for All and eInclusion. UAHCI 2011., LNCS 6765 Springer, Berlin, Heidelberg, 2011, ISBN: 978-3-642-21671-8. Abstract \| Links \| BibTeX \| Tags: Adaptive Information Visualization, Search result visualization, Semantic visualization, Semantic web, User behavior, User centered modeling @conference{C35-P-22190, title = {Search Intention Analysis for User-Centered Adaptive Visualizations}, author = {Dirk Burkhardt and Matthias Breyer and Kawa Nazemi and Arjan Kuijper}, editor = {C Stephanidis}, url = {https://link.springer.com/chapter/10.1007/978-3-642-21672-5_35, Springer link}, doi = {10.1007/978-3-642-21672-5_35}, isbn = {978-3-642-21671-8}, year = {2011}, date = {2011-01-01}, booktitle = {Universal Access in Human-Computer Interaction. Design for All and eInclusion. UAHCI 2011.}, pages = {317-326}, publisher = {Springer, Berlin, Heidelberg}, series = {LNCS 6765}, abstract = {Searching information on web turned to a matter of course in the last years. The visualization and filtering of the results of such search queries plays a key-role in different disciplines and is still today under research. In this paper a new approach for classifying the search intention of users' is presented. The approach uses existing and easy parameters for a differentiation between explorative and targeted search. The results of the classification are used for a differentiated presentation based on graphical visualization techniques.}, keywords = {Adaptive Information Visualization, Search result visualization, Semantic visualization, Semantic web, User behavior, User centered modeling}, pubstate = {published}, tppubtype = {conference} } Close Searching information on web turned to a matter of course in the last years. The visualization and filtering of the results of such search queries plays a key-role in different disciplines and is still today under research. In this paper a new approach for classifying the search intention of users' is presented. The approach uses existing and easy parameters for a differentiation between explorative and targeted search. The results of the classification are used for a differentiated presentation based on graphical visualization techniques. Close Springer link doi:10.1007/978-3-642-21672-5_35 Close
4.	Kawa Nazemi; Christian Stab; Arjan Kuijper A Reference Model for Adaptive Visualization Systems Inproceedings In: Julie A Jacko (Ed.): Human-Computer Interaction. Design and Development Approaches. HCI 2011., pp. 480-489, Springer, Berlin, Heidelberg, 2011, ISBN: 978-3-642-21601-5. Abstract \| Links \| BibTeX \| Tags: Adaptive Information Visualization, Ontology visualization, Reference models @inproceedings{C35-P-22194, title = {A Reference Model for Adaptive Visualization Systems}, author = {Kawa Nazemi and Christian Stab and Arjan Kuijper}, editor = {Julie A Jacko}, url = {https://link.springer.com/chapter/10.1007/978-3-642-21602-2_52, Springer Link}, doi = {10.1007/978-3-642-21602-2_52}, isbn = {978-3-642-21601-5}, year = {2011}, date = {2011-01-01}, booktitle = {Human-Computer Interaction. Design and Development Approaches. HCI 2011.}, pages = {480-489}, publisher = {Springer, Berlin, Heidelberg}, series = {LNCS 6761}, abstract = {One key issue of both Information Visualization as well as Adaptive User Interfaces is information overload. While both disciplines have already devised well performing algorithms, methods and applications, a real merging has not taken place yet. Only a few attempts bring the surplus values of both disciplines together, whereas a fine-grained investigation of visualization parameterization is not investigated. Today's systems focus either on the adaptation of visualization types or the parameterization of visualizations. This paper presents a reference Model for Adaptive Visualization Systems (MAVS) that allows the adaptation of both the visualization type and the visualization parameterization. Based on this model, a framework for the adaptive visualization of semantics data will be derived. A use case describing the interaction with an ädaptive visualization cockpit" covering different visualization metaphors concludes the paper.}, keywords = {Adaptive Information Visualization, Ontology visualization, Reference models}, pubstate = {published}, tppubtype = {inproceedings} } Close One key issue of both Information Visualization as well as Adaptive User Interfaces is information overload. While both disciplines have already devised well performing algorithms, methods and applications, a real merging has not taken place yet. Only a few attempts bring the surplus values of both disciplines together, whereas a fine-grained investigation of visualization parameterization is not investigated. Today's systems focus either on the adaptation of visualization types or the parameterization of visualizations. This paper presents a reference Model for Adaptive Visualization Systems (MAVS) that allows the adaptation of both the visualization type and the visualization parameterization. Based on this model, a framework for the adaptive visualization of semantics data will be derived. A use case describing the interaction with an ädaptive visualization cockpit" covering different visualization metaphors concludes the paper. Close Springer Link doi:10.1007/978-3-642-21602-2_52 Close
3.	Kawa Nazemi; Dirk Burkhardt; Alexander Praetorius; Matthias Breyer; Arjan Kuijper Adapting User Interfaces by Analyzing Data Characteristics for Determining Adequate Visualizations Inproceedings In: Masaaki Kurosu (Ed.): Human Centered Design, pp. 566–575, Springer Berlin Heidelberg, Berlin, Heidelberg, 2011, ISBN: 978-3-642-21753-1. Abstract \| Links \| BibTeX \| Tags: Adaptive Information Visualization, Adaptive User Interfaces, Adaptive Visualization, Data Analytics, Human Factors, Human-computer interaction (HCI), Information visualization, Intelligent Systems, personalization, reference model, Semantic visualization, Semantic web, User behavior @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} } Close 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. Close DOI Springer page doi:10.1007/978-3-642-21753-1_63 Close
2.	Kawa Nazemi; Dirk Burkhardt; Matthias Breyer; Arjan Kuijper Modeling Users for Adaptive Semantics Visualizations Inproceedings In: Constantine Stephanidis (Ed.): International Conference on Universal Access in Human-Computer Interaction. Universal Access in Human-Computer Interaction. Users Diversity., pp. 88–97, Springer Berlin Heidelberg, Berlin, Heidelberg, 2011, ISBN: 978-3-642-21663-3. Abstract \| Links \| BibTeX \| Tags: Adaptive Information Visualization, Adaptive User Interfaces, Adaptive Visualization, Intelligent Systems, Interaction analysis, Interaction Design, User modeling @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} } Close 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. Close Springer link doi:10.1007/978-3-642-21663-3_10 Close
2010
1.	Kawa Nazemi; Christian Stab; Dieter W. Fellner Interaction Analysis for Adaptive User Interfaces Conference Advanced Intelligent Computing Theories and Applications. International Conference on Intelligent Computing., LNCS 6215 Springer, Berlin, Heidelberg, 2010, ISBN: 978-3-642-14921-4. Abstract \| Links \| BibTeX \| Tags: Adaptive Information Visualization, Adaptive User Interfaces, Interaction analysis, Probabilistic models, User modeling @conference{C35-P-21532, title = {Interaction Analysis for Adaptive User Interfaces}, author = {Kawa Nazemi and Christian Stab and Dieter W. Fellner}, url = {https://link.springer.com/chapter/10.1007/978-3-642-14922-1_45, Springer page}, doi = {doi.org/10.1007/978-3-642-14922-1_45}, isbn = {978-3-642-14921-4}, year = {2010}, date = {2010-01-01}, booktitle = {Advanced Intelligent Computing Theories and Applications. International Conference on Intelligent Computing.}, pages = {362-371}, publisher = {Springer, Berlin, Heidelberg}, series = {LNCS 6215}, abstract = {Adaptive User Interfaces are able to facilitate the handling of computer systems through the automatic adaptation to users' needs and preferences. For the realization of these systems, information about the individual user is needed. This user information can be extracted from user events by applying analytical methods without the active information input by the user. In this paper we introduce a reusable interaction analysis system based on probabilistic methods that predicts user interactions, recognizes user activities and detects user preferences on different levels of abstraction. The evaluation reveals that the prediction quality of the developed algorithm outperforms the quality of other established prediction methods.}, keywords = {Adaptive Information Visualization, Adaptive User Interfaces, Interaction analysis, Probabilistic models, User modeling}, pubstate = {published}, tppubtype = {conference} } Close Adaptive User Interfaces are able to facilitate the handling of computer systems through the automatic adaptation to users' needs and preferences. For the realization of these systems, information about the individual user is needed. This user information can be extracted from user events by applying analytical methods without the active information input by the user. In this paper we introduce a reusable interaction analysis system based on probabilistic methods that predicts user interactions, recognizes user activities and detects user preferences on different levels of abstraction. The evaluation reveals that the prediction quality of the developed algorithm outperforms the quality of other established prediction methods. Close Springer page doi:doi.org/10.1007/978-3-642-14922-1_45 Close