A Reference Model for Adaptive Visualization Systems

Nazemi, Kawa; Stab, Christian; Kuijper, Arjan

doi:10.1007/978-3-642-21602-2_52

Cited by 30 publications

(13 citation statements)

References 24 publications

(35 reference statements)

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“…The most established is the reference model for visualization from Card et al [38], but there are also pipelines for supporting special types of visualizations and data e.g. the Semantics Visualization pipeline by Nazemi et al [39]. The process support is than part of the process control.…”

Section: Concept For User-supporting Process-driven Information VImentioning

confidence: 99%

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Towards process-oriented Information Visualization for supporting users

Burkhardt

Ruppert

Nazemi

2012

2012 15th International Conference on Interactive Collaborative Learning (ICL)

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Nowadays daily office work consists of dealing with big numbers of data and data sources, and furthermore of working with complex computer programs. In consequence many users have problems to use such programs effective and efficient. In particular beginners have significant problems to use the programs correctly due to complex functionality and interaction options. To avoid this overload of the user, the Information Visualization community has recently developed some approaches that aim to support the users. Unfortunately, these approaches are limited to one special aspect, and sometimes they are just appropriate for one special task. Thus, in this paper we introduce a process-oriented user-supporting approach. It allows selecting adequate supporting techniques in correlation to a performed process and activity to guide the user and help him to solve his task. Furthermore, we show the benefits of designing programs and applications, which implement process definitions for the existing tasks to provide the user with better process orientation. This guides the user through difficult and complex processes.

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Section: Concept For User-supporting Process-driven Information VImentioning

confidence: 99%

“…for an adaption module. We have created a module to adapt the visualization based on the user's interaction [39]. Because of the missing concrete process definition, the integration had to be implemented directly in the source code and was adapted to the framework-based technical environment.…”

Section: E Application In Information Visualization Systemsmentioning

confidence: 99%

Towards process-oriented Information Visualization for supporting users

Burkhardt

Ruppert

Nazemi

2012

2012 15th International Conference on Interactive Collaborative Learning (ICL)

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“…The quantitative information enables us to choose an appropriate set of visual layouts for the results of the users' search [20,21]. The number of entities is important to choose a visualization that is capable to illustrate that amount of data, and to categorize the entities by their concepts to support the exploration process.…”

Section: Quantitative Analysis Of Datamentioning

confidence: 99%

Measuring context relevance for adaptive semantics visualizations

Nazemi

Kuijper

Hutter

et al. 2014

Proceedings of the 14th International Conference on Knowledge Technologies and Data-Driven Business

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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 pro- vide 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 in-stances, and thus gain more information at once, without confusing and frustrating users

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“…Although they can be used alternatively for each another, based on the user's parameters, one of them may seem more suitable. [11] Nazemi et al get content and users' information using two modules: conten and interaction analysis [45]. Their content analysis module extracts data properties,and their interaction module extracts users' information from different sensors.…”

Section: Adaptive Visualisation Systemsmentioning

confidence: 99%

“…Figure 2.2: The model of the adaptive visualisation system [45] Saving changes that the users make to visualisations can be useful in tracking users' preferences and adapting the system by them as time passes. Golemati et al [25] system has a similar approach however it is not designed as an automatic visualisation system.…”

Section: Adaptive Visualisation Systemsmentioning

confidence: 99%

Adaptive Initial Users’ Overview of a Map Visualisation

Mianji¹

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<p>The ability to customise different aspects of a visualisation system for users is typically lacking in current applications[30]. This results in less immediate and relevant visualisation information to users, as all users are presented with the same overview irrespective of their persona. This thesis proposes a dynamic, adaptive visualisation system that customises the initial view of the visualisation based on the information received from users. This adaptive approach improves the upfront user experience connected with the first stage in Shneiderman’s Information Seeking Mantra ’overview first’[57]. This thesis aims to explore whether and how the users’ interactions in the first four steps of Shneiderman’s tasks [57] can be used to create the appropriate early experience for future users with the same goals or characteristics. The evaluation confirms the possibility of the idea and raises further questions and issues.</p>

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A Reference Model for Adaptive Visualization Systems

Cited by 30 publications

References 24 publications

Towards process-oriented Information Visualization for supporting users

Towards process-oriented Information Visualization for supporting users

Measuring context relevance for adaptive semantics visualizations

Adaptive Initial Users’ Overview of a Map Visualisation

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