2014
|
4. | Kawa Nazemi Adaptive Semantics Visualization PhD Thesis Technische Universität Darmstadt, 2014, (Reprint by Eugraphics Association (EG)). @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 = {},
pubstate = {published},
tppubtype = {phdthesis}
}
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. |
3. | Kawa Nazemi; Dirk Burkhardt; Reimond Retz; Arjan Kuijper; Jörn Kohlhammer Adaptive Visualization of Linked-Data Proceedings Article 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. @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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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. |
2011
|
2. | Matthias Breyer; Kawa Nazemi; Christian Stab; Dirk Burkhardt; Arjan Kuijper A Comprehensive Reference Model for Personalized Recommender Systems Book Chapter In: M. J. Smith; G. Salvendy (Ed.): Human Interface and the Management of Information. Interacting with Information: Symposium on Human Interface 2011, Orlando, FL, USA., pp. 528–537, Springer Berlin Heidelberg, Berlin, Heidelberg, 2011, ISBN: 978-3-642-21793-7. @inbook{Breyer2011,
title = {A Comprehensive Reference Model for Personalized Recommender Systems},
author = {Matthias Breyer and Kawa Nazemi and Christian Stab and Dirk Burkhardt and Arjan Kuijper},
editor = {M. J. Smith and G. Salvendy},
url = {https://link.springer.com/chapter/10.1007%2F978-3-642-21793-7_60},
doi = {10.1007/978-3-642-21793-7_60},
isbn = {978-3-642-21793-7},
year = {2011},
date = {2011-01-01},
booktitle = {Human Interface and the Management of Information. Interacting with Information: Symposium on Human Interface 2011, Orlando, FL, USA.},
pages = {528--537},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
abstract = {Existing reference models for recommender systems are on an abstract level of detail or do not point out the processes and transitions of recommendation systems. However, this information is relevant for developers to design or improve recommendation systems. Even so, users need some background information of the calculation process to understand the process and accept or configure these systems proper. In this paper we present a comprehensive reference model for recommender systems which conjuncts the recommendation processes on an adequate level of detail. To achieve this, the processes of content-based and collaboration-based systems are merged and extended by the transitions and phases of hybrid systems. Furthermore, the algorithms which can be applied in the phases of the model are examined to identify the data flow between these phases. With our model those information of the recommendation calculation process can be identified, which encourages the traceability and thus the acceptance of recommendations.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
Existing reference models for recommender systems are on an abstract level of detail or do not point out the processes and transitions of recommendation systems. However, this information is relevant for developers to design or improve recommendation systems. Even so, users need some background information of the calculation process to understand the process and accept or configure these systems proper. In this paper we present a comprehensive reference model for recommender systems which conjuncts the recommendation processes on an adequate level of detail. To achieve this, the processes of content-based and collaboration-based systems are merged and extended by the transitions and phases of hybrid systems. Furthermore, the algorithms which can be applied in the phases of the model are examined to identify the data flow between these phases. With our model those information of the recommendation calculation process can be identified, which encourages the traceability and thus the acceptance of recommendations. |
2008
|
1. | Christoph Hornung; Andrina Granić; Maja Ćukušić; Kawa Nazemi eKnowledge Repositories in eLearning 2.0: UNITE - a European-Wide Network of Schools Book Chapter In: F. Li; J. Zhao; T. K Shih; R. Lau; Q. Li; D. McLeod (Ed.): Advances in Web Based Learning - ICWL 2008: 7th International Conference, Jinhua, China, August 20-22, 2008. Proceedings, pp. 99–110, Springer Berlin Heidelberg, Berlin, Heidelberg, 2008, ISBN: 978-3-540-85033-5. @inbook{Hornung2008,
title = {eKnowledge Repositories in eLearning 2.0: UNITE - a European-Wide Network of Schools},
author = {Christoph Hornung and Andrina Granić and Maja Ćukušić and Kawa Nazemi},
editor = {F. Li and J. Zhao and T. K Shih and R. Lau and Q. Li and D. McLeod},
url = {https://doi.org/10.1007/978-3-540-85033-5_11
https://link.springer.com/chapter/10.1007/978-3-540-85033-5_11},
doi = {10.1007/978-3-540-85033-5_11},
isbn = {978-3-540-85033-5},
year = {2008},
date = {2008-01-01},
booktitle = {Advances in Web Based Learning - ICWL 2008: 7th International Conference, Jinhua, China, August 20-22, 2008. Proceedings},
pages = {99--110},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
abstract = {The upcoming Web 2.0 technologies change the aspects of eLearning fundamentally. The traditional paradigm of classroom teaching and homework learning will develop further towards sharing experiences and knowledge in word-wide social communities. Moreover, knowledge capturing in ambient environments gains more and more importance. These aspects characterize the so-called eLearning 2.0. This paper describes a prototype of an eLearning 2.0 system covering the different aspects such as platform, pedagogy and scenarios. The concepts presented here have been applied in the EU-project UNITE. The implementation of this system in the setting of a European network of fourteen schools is presented as an iterative four stage process, covering scenario planning and implementation, validation in addition to platform and process improvement. Achieved intermediate results from the first iteration of the implementation process are discussed and future work is presented.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
The upcoming Web 2.0 technologies change the aspects of eLearning fundamentally. The traditional paradigm of classroom teaching and homework learning will develop further towards sharing experiences and knowledge in word-wide social communities. Moreover, knowledge capturing in ambient environments gains more and more importance. These aspects characterize the so-called eLearning 2.0. This paper describes a prototype of an eLearning 2.0 system covering the different aspects such as platform, pedagogy and scenarios. The concepts presented here have been applied in the EU-project UNITE. The implementation of this system in the setting of a European network of fourteen schools is presented as an iterative four stage process, covering scenario planning and implementation, validation in addition to platform and process improvement. Achieved intermediate results from the first iteration of the implementation process are discussed and future work is presented. |