Publikationen
Nazemi, Kawa Adaptive Semantics Visualization Promotionsarbeit Technische Universität Darmstadt, 2014, (Reprint by Eugraphics Association (EG)). Abstract | Links | BibTeX | Schlagwörter: Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Computer based learning, Data Analytics, E-Learning, Exploratory learning, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, Ontology visualization, personalization, Policy modeling, reference model, Semantic data modeling, Semantic visualization, Semantic web, Semantics visualization, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics Burkhardt, Dirk; Nazemi, Kawa; Kohlhammer, Jörn Visual Process Support to Assist Users in Policy Making Buchkapitel In: Sonntagbauer, Peter; Nazemi, Kawa; Sonntagbauer, Susanne; Prister, Giorgio; Burkhardt, Dirk (Hrsg.): Handbook of Research on Advanced ICT Integration for Governance and Policy Modeling, S. 149–162, IGI Global, 2014, ISBN: 978-1-466-66236-0. Abstract | Links | BibTeX | Schlagwörter: Information visualization, Interaction analysis, Process Support, Semantic visualization, Visual analytics Nazemi, Kawa; Burkhardt, Dirk; Retz, Reimond; Kuijper, Arjan; Kohlhammer, Jörn Adaptive Visualization of Linked-Data Proceedings Article In: Bebis, George; Boyle, Richard; Parvin, Bahram; Koracin, Darko; McMahan, Ryan; Jerald, Jason; Zhang, Hui; Drucker, Steven M; Kambhamettu, Chandra; Choubassi, Maha El; Deng, Zhigang; Carlson, Mark (Hrsg.): Proceedings of International Symposium on Visual Computing (ISVC 2014). Advances in Visual Computing., S. 872–883, Springer International Publishing, Cham, 2014, ISBN: 978-3-319-14364-4. Abstract | Links | BibTeX | Schlagwörter: Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Data Analytics, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, personalization, reference model, Semantic visualization, Semantic web, User behavior, User modeling, User-centered design, Visual analytics Nazemi, Kawa; Burkhardt, Dirk; Retz, Wilhelm; Kohlhammer, Jörn Adaptive Visualization of Social Media Data for Policy Modeling Proceedings Article In: Bebis, George; Boyle, Richard; Parvin, Bahram; Koracin, Darko; McMahan, Ryan; Jerald, Jason; Zhang, Hui; Drucker, Steven M; Kambhamettu, Chandra; Choubassi, Maha El; Deng, Zhigang; Carlson, Mark (Hrsg.): Proceeding of the International Symposium on Visual Computing (ISVC 2014). Advances in Visual Computing., S. 333–344, Springer International Publishing, Cham, 2014, ISBN: 978-3-319-14249-4. Abstract | Links | BibTeX | Schlagwörter: Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Data Analytics, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, personalization, Semantic visualization, Semantic web, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics Nazemi, Kawa; Kohlhammer, Jörn Visual Variables in Adaptive Visualizations. Proceedings Article In: Berkovsky, Shlomo; Herder, Eelco; Lops, Pasquale; Santos, Olga C. (Hrsg.): 21st Conference on User Modeling, Adaptation, and Personalization. UMAP 2013 Extended Proceedings. Proceeding of 1st International Workshop on User-Adaptive Visualizations., CEUR Workshop Proceedings, Rome, Italy,, 2013, ISSN: 1613-0073. Abstract | Links | BibTeX | Schlagwörter: Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Human Factors, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, Semantic visualization Nazemi, Kawa; Retz, Reimond; Bernard, Jürgen; Kohlhammer, Jörn; Fellner, Dieter Adaptive Semantic Visualization for Bibliographic Entries Proceedings Article In: Bebis, George; Boyle, Richard; Parvin, Bahram; Koracin, Darko; Li, Baoxin; Porikli, Fatih; Zordan, Victor; Klosowski, James; Coquillart, Sabine; Luo, Xun; Chen, Min; Gotz, David (Hrsg.): Proceedings of International Symposium on Visual Computing (ISVC 2013). Advances in Visual Computing., S. 13–24, Springer Berlin Heidelberg, Berlin, Heidelberg, 2013, ISBN: 978-3-642-41939-3. Abstract | Links | BibTeX | Schlagwörter: Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Data Analytics, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, personalization, Semantic visualization, Semantic web, User behavior, User Interactions, Visual analytics Nazemi, Kawa; Burkhardt, Dirk; Breyer, Matthias; Kuijper, Arjan Modeling Users for Adaptive Semantics Visualizations Proceedings Article In: Stephanidis, Constantine (Hrsg.): International Conference on Universal Access in Human-Computer Interaction. Universal Access in Human-Computer Interaction. Users Diversity. , S. 88–97, Springer Berlin Heidelberg, Berlin, Heidelberg, 2011, ISBN: 978-3-642-21663-3. Abstract | Links | BibTeX | Schlagwörter: Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Intelligent Systems, Interaction analysis, Interaction Design, User modeling Nazemi, Kawa; Stab, Christian; Fellner, Dieter W. IEEE International Conference on Intelligent Computing and Intelligent Systems, IEEE IEEE Press, 2010, ISBN: 978-1-4244-6582-8. Abstract | Links | BibTeX | Schlagwörter: Adaptive user interfaces, Interaction analysis, Statistics, User behavior, User modeling Nazemi, Kawa; Stab, Christian; Fellner, Dieter W Interaction Analysis for Adaptive User Interfaces Konferenz 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 | Schlagwörter: Adaptive information visualization, Adaptive user interfaces, Interaction analysis, Probabilistic models, User modeling2014
@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.@inbook{burkhardt2014visual,
title = {Visual Process Support to Assist Users in Policy Making},
author = {Dirk Burkhardt and Kawa Nazemi and Jörn Kohlhammer},
editor = {Peter Sonntagbauer and Kawa Nazemi and Susanne Sonntagbauer and Giorgio Prister and Dirk Burkhardt},
url = {https://www.igi-global.com/chapter/visual-process-support-to-assist-users-in-policy-making/116661, IGI Global},
doi = {10.4018/978-1-4666-6236-0.ch009},
isbn = {978-1-466-66236-0},
year = {2014},
date = {2014-06-01},
booktitle = {Handbook of Research on Advanced ICT Integration for Governance and Policy Modeling},
journal = {Handbook of Research on Advanced ICT Integration for Governance and Policy Modeling},
pages = {149--162},
publisher = {IGI Global},
series = {Handbook of Research},
crossref = {Sonntagbauer2014},
abstract = {The policy making process requires the involvement of various stakeholders, who bring in very heterogeneous experiences and skills concerning the policymaking domain, as well as experiences of ICT solutions. Current solutions are primarily designed to provide “one-solution-fits-all” answers, which in most cases fail the needs of all stakeholders. In this chapter, the authors introduce a new approach to assist users based on their tasks. Therefore, the system observes the interaction of the user and recognizes the current phase of the policymaking process and the profile of the user to assist him more sufficiently in solving his task. For this purpose, the system automatically enables or disables supporting features such as visualization, tools, and supporting techniques.},
keywords = {Information visualization, Interaction analysis, Process Support, Semantic visualization, Visual analytics},
pubstate = {published},
tppubtype = {inbook}
}
@inproceedings{Nazemi2014b,
title = {Adaptive Visualization of Linked-Data},
author = {Kawa Nazemi and Dirk Burkhardt and Reimond Retz and Arjan Kuijper and Jörn Kohlhammer},
editor = {George Bebis and Richard Boyle and Bahram Parvin and Darko Koracin and Ryan McMahan and Jason Jerald and Hui Zhang and Steven M Drucker and Chandra Kambhamettu and Maha El Choubassi and Zhigang Deng and Mark Carlson},
url = {https://link.springer.com/chapter/10.1007/978-3-319-14364-4_84, Springer link},
doi = {10.1007/978-3-319-14364-4_84},
isbn = {978-3-319-14364-4},
year = {2014},
date = {2014-03-01},
booktitle = {Proceedings of International Symposium on Visual Computing (ISVC 2014). Advances in Visual Computing.},
pages = {872--883},
publisher = {Springer International Publishing},
address = {Cham},
series = {LNCS 8888},
abstract = {Adaptive visualizations reduces the required cognitive effort to comprehend interactive visual pictures and amplify cognition. Although the research on adaptive visualizations grew in the last years, the existing approaches do not consider the transformation pipeline from data to visual representation for a more efficient and effective adaptation. Further todays systems commonly require an initial training by experts from the field and are limited to adaptation based either on user behavior or on data characteristics. A combination of both is not proposed to our knowledge. This paper introduces an enhanced instantiation of our previously proposed model that combines both: involving different influencing factors for and adapting various levels of visual peculiarities, on content, visual layout, visual presentation, and visual interface. Based on data type and users’ behavior, our system adapts a set of applicable visualization types. Moreover, retinal variables of each visualization type are adapted to meet individual or canonical requirements on both, data types and users’ behavior. Our system does not require an initial expert modeling.},
keywords = {Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Data Analytics, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, personalization, reference model, Semantic visualization, Semantic web, User behavior, User modeling, User-centered design, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Nazemi2014,
title = {Adaptive Visualization of Social Media Data for Policy Modeling},
author = {Kawa Nazemi and Dirk Burkhardt and Wilhelm Retz and Jörn Kohlhammer},
editor = {George Bebis and Richard Boyle and Bahram Parvin and Darko Koracin and Ryan McMahan and Jason Jerald and Hui Zhang and Steven M Drucker and Chandra Kambhamettu and Maha El Choubassi and Zhigang Deng and Mark Carlson},
url = {https://link.springer.com/chapter/10.1007/978-3-319-14249-4_32, Springer link},
doi = {10.1007/978-3-319-14249-4_32},
isbn = {978-3-319-14249-4},
year = {2014},
date = {2014-01-01},
booktitle = {Proceeding of the International Symposium on Visual Computing (ISVC 2014). Advances in Visual Computing.},
pages = {333--344},
publisher = {Springer International Publishing},
address = {Cham},
series = {LNCS 8887},
abstract = {The visual analysis of social media data emerged a huge number of interactive visual representations that use different characteristics of the data to enable the process of information acquisition. The social data are used in the domain of policy modeling to gather information about citizens' demands, opinions, and requirements and help to decide about political policies. Although existing systems already provide a huge number of visual analysis tools, the search and exploration paradigm is not really clear. Furthermore, the systems commonly do not provide any kind of human centered adaptation for the different stakeholders involved in the policy making process. In this paper, we introduce a novel approach that investigates the exploration and search paradigm from two different perspectives and enables a visual adaptation to support the exploration and analysis process.},
keywords = {Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Data Analytics, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, personalization, Semantic visualization, Semantic web, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
2013
@inproceedings{nazemi2013visual,
title = {Visual Variables in Adaptive Visualizations.},
author = {Kawa Nazemi and Jörn Kohlhammer},
editor = {Shlomo Berkovsky and Eelco Herder and Pasquale Lops and Olga C. Santos },
url = {https://ceur-ws.org/Vol-997/wuav2013_paper_06.pdf, full text},
issn = {1613-0073},
year = {2013},
date = {2013-06-01},
booktitle = {21st Conference on User Modeling, Adaptation, and Personalization. UMAP 2013 Extended Proceedings. Proceeding of 1st International Workshop on User-Adaptive Visualizations.},
publisher = {CEUR Workshop Proceedings},
address = {Rome, Italy,},
series = {Vol. 997},
abstract = {Visualizations provide various variables for the adaptation to the usage context and the users. Today’s adaptive visualizations make use of various visual variables to order or filter information or visualizations. However, the capabilities of visual variables in context of human information processing and tasks are not comprehensively exploited. This paper discusses the value of the different visual variables providing beneficial and more accurately adapted information visualizations.},
keywords = {Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Human Factors, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, Semantic visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{Nazemi2013,
title = {Adaptive Semantic Visualization for Bibliographic Entries},
author = {Kawa Nazemi and Reimond Retz and Jürgen Bernard and Jörn Kohlhammer and Dieter Fellner},
editor = {George Bebis and Richard Boyle and Bahram Parvin and Darko Koracin and Baoxin Li and Fatih Porikli and Victor Zordan and James Klosowski and Sabine Coquillart and Xun Luo and Min Chen and David Gotz},
url = {https://link.springer.com/chapter/10.1007/978-3-642-41939-3_2, Springer link},
doi = {10.1007/978-3-642-41939-3_2},
isbn = {978-3-642-41939-3},
year = {2013},
date = {2013-01-01},
booktitle = {Proceedings of International Symposium on Visual Computing (ISVC 2013). Advances in Visual Computing.},
pages = {13--24},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
series = {LNCS 8034},
abstract = {Adaptive visualizations aim to reduce the complexity of visual representations and convey information using interactive visualizations. Although the research on adaptive visualizations grew in the last years, the existing approaches do not make use of the variety of adaptable visual variables. Further the existing approaches often premises experts, who has to model the initial visualization design. In addition, current approaches either incorporate user behavior or data types. A combination of both is not proposed to our knowledge. This paper introduces the instantiation of our previously proposed model that combines both: involving different influencing factors for and adapting various levels of visual peculiarities, on visual layout and visual presentation in a multiple visualization environment. Based on data type and users’ behavior, our system adapts a set of applicable visualization types. Moreover, retinal variables of each visualization type are adapted to meet individual or canonic requirements on both, data types and users’ behavior. Our system does not require an initial expert modeling.},
keywords = {Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Data Analytics, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, personalization, Semantic visualization, Semantic web, User behavior, User Interactions, Visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
2011
@inproceedings{Nazemi2011d,
title = {Modeling Users for Adaptive Semantics Visualizations},
author = {Kawa Nazemi and Dirk Burkhardt and Matthias Breyer and Arjan Kuijper},
editor = {Constantine Stephanidis},
url = {https://link.springer.com/chapter/10.1007/978-3-642-21663-3_10, Springer link},
doi = {10.1007/978-3-642-21663-3_10},
isbn = {978-3-642-21663-3},
year = {2011},
date = {2011-01-01},
booktitle = {International Conference on Universal Access in Human-Computer Interaction. Universal Access in Human-Computer Interaction. Users Diversity. },
volume = {2},
pages = {88--97},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
series = {LNCS 6766},
abstract = {The automatic adaptation of information visualization systems to the requirements of users plays a key-role in today's research. Different approaches from both disciplines try to face this phenomenon. The modeling of user is an essential part of a user-centered adaptation of visualization. In this paper we introduce a new approach for modeling users especially for semantic visualization systems. The approach consists of a three dimensional model, where semantic data, user and visualization are set in relation in different abstraction layer.},
keywords = {Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Intelligent Systems, Interaction analysis, Interaction Design, User modeling},
pubstate = {published},
tppubtype = {inproceedings}
}
2010
@conference{C35-P-21676,
title = {Interaction Analysis: An Algorithm for Interaction Prediction and Activity Recognition in Adaptive Systems},
author = {Kawa Nazemi and Christian Stab and Dieter W. Fellner},
url = {https://ieeexplore.ieee.org/document/5658514/?reload=true, IEEE Xplore},
doi = {10.1109/ICICISYS.2010.5658514},
isbn = {978-1-4244-6582-8},
year = {2010},
date = {2010-01-01},
booktitle = {IEEE International Conference on Intelligent Computing and Intelligent Systems},
pages = {607-612},
publisher = {IEEE Press},
organization = {IEEE},
abstract = {Predictive statistical models are used in the area of adaptive user interfaces to model user behavior and to infer user information from interaction events in an implicit and non-intrusive way. This information constitutes the basis for tailoring the user interface to the needs of the individual user. Consequently, the user analysis process should model the user with information, which can be used in various systems to recognize user activities, intentions and roles to accomplish an adequate adaptation to the given user and his current task.
In this paper we present the improved prediction algorithm KO*/19, which is able to recognize, beside interaction predictions, behavioral patterns for recognizing user activities. By means of this extension, the evaluation shows that the KO*/19-Algorithm improves the Mean Prediction Rank more than 19% compared to other well-established prediction algorithms.},
keywords = {Adaptive user interfaces, Interaction analysis, Statistics, User behavior, User modeling},
pubstate = {published},
tppubtype = {conference}
}
In this paper we present the improved prediction algorithm KO*/19, which is able to recognize, beside interaction predictions, behavioral patterns for recognizing user activities. By means of this extension, the evaluation shows that the KO*/19-Algorithm improves the Mean Prediction Rank more than 19% compared to other well-established prediction algorithms.@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}
}