@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}

}

@inproceedings{6337431,

title = {Interactive Exploration System: A User-Centered Interaction Approach in Semantics Visualizations},

author = {Dirk Burkhardt and Christian Stab and Martin Steiger and Matthias Breyer and Kawa Nazemi},

doi = {10.1109/CW.2012.45},

isbn = {978-1-4673-2736-7},

year  = {2012},

date = {2012-09-01},

booktitle = {2012 International Conference on Cyberworlds},

pages = {261-267},

publisher = {IEEE},

abstract = {Nowadays a wide range of input devices are available to users of technical systems. Especially modern alternative interaction devices, which are known from game consoles etc., provide a more natural way of interaction. In parallel to that the research on visualization of large amount of data advances very quickly. This research was also influenced by the semantic web and the idea of storing data in a structured and linked form. The semantically annotated data gains more and more importance in information acquisition processes. Especially the Linked Open Data (LOD) format already experienced a huge growth. However, the user-interfaces of web-applications mostly do not reflect the added value of semantics data. This paper describes the conceptual design and implementation of an Interactive Exploration System that offers a user-centered graphical environment of web-based knowledge repositories, to support and optimize explorative learning, and the integration of a taxonomy-based approach to enable the use of more natural interaction metaphors, as they are possible with modern devices like Wii Mote or Microsoft Kinect. Therefore we introduce a different classification for interaction devices, and current approaches for supporting the added values in semantics visualizations. Furthermore, we describe the concept of our IES, including a strategy to organize and structure today's existing input devices, and a semantics exploration system driven by user-experience. We conclude the paper with a description of the implementation of the IES and an application scenario.},

keywords = {Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Interaction Design, Semantics visualization, User-centered design, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{stab2012analytical,

title = {Analytical semantics visualization for discovering latent signals in large text collections},

author = {Christian Stab and Matthias Breyer and Dirk Burkhardt and Kawa Nazemi and Jörn Kohlhammer},

editor = {Andreas Kerren and Stefan Seipel},

url = {https://www.ep.liu.se/ecp/081/011/ecp12081011.pdf, full text},

isbn = {978-91-7519-723-4},

year  = {2012},

date = {2012-01-01},

booktitle = {Proceedings of SIGRAD 2012; Interactive Visual Analysis of Data; November 29-30; 2012; Växjö; Sweden},

number = {081},

pages = {83--86},

publisher = {Linköping University Electronic Press},

organization = {Linköping University},

abstract = {Considering the increasing pressure of competition and high dynamics of markets; the early identification and specific handling of novel developments and trends becomes more and more important for competitive companies. Today; those signals are encoded in large amounts of textual data like competitors’ web sites; news articles; scientific publications or blog entries which are freely available in the web. Processing large amounts of textual data is still a tremendous challenge for current business analysts and strategic decision makers. Although current information systems are able to process that amount of data and provide a wide range of information retrieval tools; it is almost impossible to keep track of each thread or opportunity. The presented approach combines semantic search and data mining techniques with interactive visualizations for analyzing and identifying weak signals in large text collections. Beside visual summarization tools; it includes an enhanced trend visualization that supports analysts in identifying latent topic-related relations between competitors and their temporal relevance. It includes a graph-based visualization tool for representing relations identified during semantic analysis. The interaction design allows analysts to verify their retrieved hypothesis by exploring the documents that are responsible for the current view.},

keywords = {Data Analytics, Data visualization, Semantic data modeling, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-642-21753-1_55,

title = {SemaZoom: Semantics Exploration by Using a Layer-Based Focus and Context Metaphor},

author = {Dirk Burkhardt and Kawa Nazemi and Matthias Breyer and Christian Stab and Arjan Kuijper},

editor = {Masaaki Kurosu},

url = {https://doi.org/10.1007/978-3-642-21753-1_55, DOI

https://link.springer.com/chapter/10.1007/978-3-642-21753-1_55, Springer page},

doi = {10.1007/978-3-642-21753-1_55},

isbn = {978-3-642-21753-1},

year  = {2011},

date = {2011-01-01},

booktitle = {Human Centered Design},

pages = {491--499},

publisher = {Springer Berlin Heidelberg},

address = {Berlin, Heidelberg},

series = {LNCS 6776},

abstract = {The Semantic Web is a powerful technology for organizing the data in our information based society. The collection and organization of information is an important step for showing important information to interested people. But the usage of such semantic-based data sources depends on effective and efficient information visualizations. Currently different kinds of visualizations in general and visualization metaphors do exist. Many of them are also applied for semantic data source, but often they are designed for semantic web experts and neglecting the normal user and his perception of an easy useable visualization. This kind of user needs less information, but rather a reduced qualitative view on the data. These two aspects of large amount of existing data and one for normal users easy to understand visualization is often not reconcilable. In this paper we create a concept for a visualization to show a bigger set of information to such normal users without overstraining them, because of layer-based data visualization, next to an integration of a Focus and Context metaphor.},

keywords = {Graph visualization, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, User behavior, User Interactions, User Interface, User interfaces, User-centered design, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}