@inproceedings{Nazemi2014e,

title = {User Similarity and Deviation Analysis for Adaptive Visualizations},

author = {Kawa Nazemi and Wilhelm Retz and Jörn Kohlhammer and Arjan Kuijper},

editor = {Sakae Yamamoto},

url = {https://link.springer.com/chapter/10.1007/978-3-319-07731-4_7, Springer link},

doi = {10.1007/978-3-319-07731-4_7},

isbn = {978-3-319-07731-7},

year  = {2014},

date = {2014-08-01},

booktitle = {International Conference on Human Interface and the Management of Information (HMI 2014). Human Interface and the Management of Information. Information and Knowledge Design and Evaluation.},

pages = {64--75},

publisher = {Springer International Publishing },

address = {Cham},

series = {LNCS 8521},

abstract = {Adaptive visualizations support users in information acquisition and exploration and therewith in human access of data. Their adaptation effect is often based on approaches that require the training by an expert. Further the effects often aims to support just the individual aptitudes. This paper introduces an approach for modeling a canonical user that makes the predefined training-files dispensable and enables an adaptation of visualizations for the majority of users. With the introduced user deviation algorithm, the behavior of individuals can be compared to the average user behavior represented in the canonical user model to identify behavioral anomalies. The further introduced similarity measurements allow to cluster similar deviated behavioral patterns as groups and provide them effective visual adaptations.},

keywords = {Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Data Analytics, reference model, Semantic visualization, Semantics visualization, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{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{7038782,

title = {Visual explanation of government-data for policy making through open-data inclusion},

author = {Dirk Burkhardt and Kawa Nazemi and Wilhelm Retz and Jörn Kohlhammer},

url = {https://ieeexplore.ieee.org/document/7038782/, IEEE Xplore},

doi = {10.1109/ICITST.2014.7038782},

isbn = {978-1-908320-39-1},

year  = {2014},

date = {2014-01-01},

booktitle = {The 9th International Conference for Internet Technology and Secured Transactions (ICITST-2014)},

pages = {83-89},

publisher = {IEEE},

abstract = {Commonly, data used in public authorities are statistical data about certain indicator. Such valid kind of data allows an objective observation about indicator developments over time. In case of a significant deviation from the normal indicator level, it is difficult to understand the reasons for upcoming problems. In our paper we present an approach that allows an enhanced information gathering through an improved information overview about the depending aspects to such an indicator by considering governmental data-sources that provide also other types of data than just statistics. Even more, our approach integrates a system that allows generating explanations for Open Government Data, especially to specific indicators, based on Linked-Open Data. This enables decision-makers to get hints for unexpected reasons of concrete problems that may influence an indicator.},

keywords = {eGovernance, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Interaction Design, Policy modeling, Semantic visualization, User-centered design},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nazemi2014,

title = {Adaptive Visualization of Social Media Data for Policy Modeling},

author = {Kawa Nazemi and Dirk Burkhardt and Wilhelm Retz and Jörn Kohlhammer},

editor = {George Bebis and Richard Boyle and Bahram Parvin and Darko Koracin and Ryan McMahan and Jason Jerald and Hui Zhang and Steven M Drucker and Chandra Kambhamettu and Maha El Choubassi and Zhigang Deng and Mark Carlson},

url = {https://link.springer.com/chapter/10.1007/978-3-319-14249-4_32, Springer link},

doi = {10.1007/978-3-319-14249-4_32},

isbn = {978-3-319-14249-4},

year  = {2014},

date = {2014-01-01},

booktitle = {Proceeding of the International Symposium on Visual Computing (ISVC 2014). Advances in Visual Computing.},

pages = {333--344},

publisher = {Springer International Publishing},

address = {Cham},

series = {LNCS 8887},

abstract = {The visual analysis of social media data emerged a huge number of interactive visual representations that use different characteristics of the data to enable the process of information acquisition. The social data are used in the domain of policy modeling to gather information about citizens' demands, opinions, and requirements and help to decide about political policies. Although existing systems already provide a huge number of visual analysis tools, the search and exploration paradigm is not really clear. Furthermore, the systems commonly do not provide any kind of human centered adaptation for the different stakeholders involved in the policy making process. In this paper, we introduce a novel approach that investigates the exploration and search paradigm from two different perspectives and enables a visual adaptation to support the exploration and analysis process.},

keywords = {Adaptive information visualization, Adaptive user interfaces, Adaptive visualization, Data Analytics, Human Factors, Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Intelligent Systems, Interaction analysis, Interaction Design, personalization, Semantic visualization, Semantic web, User behavior, User Interactions, User Interface, User modeling, User-centered design, Visual analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{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{burkhardt2013interactive,

title = {Interactive Visualizations in the Process of Policy Modelling.},

author = {Dirk Burkhardt and Kawa Nazemi and Peter Sonntagbauer and Susanne Sonntagbauer and Jörn Kohlhammer},

editor = {Maria Wimmer and Marjin Janssen and Ann Macintosh and Hans J. Scholl and Efthimios Tambouris},

url = {https://subs.emis.de/LNI/Proceedings/Proceedings221/104.pdf, LNI GI- full text

https://pdfs.semanticscholar.org/78a3/e0732eabaeb7c84b50a28a70bcddde40f562.pdf, Semantic scholars - full text},

isbn = {978-3-88579-615-2},

year  = {2013},

date = {2013-01-01},

booktitle = {Electronic Government and Electronic Participation Joint Proceedings of Ongoing Research of IFIP EGOV and IFIP ePart 2013},

pages = {104--115},

publisher = {Gesellschaft für Informatik e.V. (GI)},

keywords = {eGovernance, Interaction Design, Policy modeling, Semantic visualization, Semantic web, User Interactions, User-centered design},

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}

}

@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}

}

@inproceedings{Stab2011,

title = {Interacting with Semantics and Time},

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

editor = {Julie A Jacko},

url = {https://link.springer.com/chapter/10.1007/978-3-642-21619-0_64, Springer link

},

doi = {10.1007/978-3-642-21619-0_64},

isbn = {978-3-642-21619-0},

year  = {2011},

date = {2011-01-01},

booktitle = {Human-Computer Interaction. Users and Applications. Proceedings of HCI International 2011},

volume = {4},

pages = {520--529},

publisher = {Springer Berlin Heidelberg},

address = {Berlin, Heidelberg},

series = {LNCS 6764},

abstract = {Time appears in many different semantic information systems like historical databases, multimedia systems or social communities as a common attribute. Beside the temporal information, the resources in these domains are categorized in a domain-specific schema and interconnected by semantic relations. Nevertheless, the high potential of these systems is not yet exhausted completely. Even today most of these knowledge systems present time-dependent semantic knowledge in textual form, what makes it difficult for the average user to understand temporal structures and dependencies. For bridging this gap between human and computer and for simplifying the exploration of time-dependent semantic knowledge, we developed a new interactive timeline visualization called SemaTime. The new designed temporal navigation concept offers an intuitive way for exploring and filtering time-depended resources. Additionally SemaTime offers navigation and visual filtering methods on the conceptual layer of the domain and is able to depict semantic relations. In this paper we describe the conceptual design of SemaTime and illustrate its application potentials in semantic search environments.},

keywords = {Data Analytics, Human Factors, Human-centered user interfaces, Information visualization, Ontology visualization, Semantic visualization, Semantic web, Temporal Visualization, User behavior},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nazemi2011c,

title = {Interacting with Semantics: A User-Centered Visualization Adaptation Based on Semantics Data},

author = {Kawa Nazemi and Matthias Breyer and Jeanette Forster and Dirk Burkhardt and Arjan Kuijper},

editor = {Michael J. Smith and Gavriel Salvendy},

url = {https://link.springer.com/chapter/10.1007/978-3-642-21793-7_28, Springer link

},

doi = {10.1007/978-3-642-21793-7_28},

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.},

pages = {239--248},

publisher = {Springer Berlin Heidelberg},

address = {Berlin, Heidelberg},

series = {LNCS 6771},

abstract = {Semantically annotated data gain more and more importance in future information acquiring processes. Especially the Linked Open Data (LOD) format has already experienced a great growth. However, the user-interfaces of web-applications mostly do not reflect the added value of semantics data. The following paper describes a new approach of user-centered data-adaptive semantics visualization, which makes use of the advantages of semantics data combined with an adaptive composition of information visualization techniques. It starts with a related work section, where existing LOD systems and information visualization techniques are described. After that, the new approach will bridge the gap between semantically annotated data (LOD) and information visualization and introduces a visualization system that adapts the composition of visualizations based on the underlying data structure. A case study of an example case will conclude this paper.},

keywords = {Human-centered user interfaces, Human-computer interaction (HCI), Information visualization, Interaction Design, Semantic visualization, Semantic web, User behavior, User Interactions},

pubstate = {published},

tppubtype = {inproceedings}

}