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Distributed context-aware visualization

Authors: Harald Sanftmann; Nazario Cipriani; Daniel Weiskopf;

Distributed context-aware visualization

Abstract

We present a visualization framework integrated in a context-aware system that uses a common underlying stream processing middleware for tight integration of data accessing, processing, and visualization. Context-aware systems are often realized on mobile devices that do not have the computational power to perform complex tasks. Therefore, a dedicated hardware infrastructure might be required for data processing. In our case, stream processing is used, supporting parallelism on distributed and shared memory multiprocessors. We present the integration of visualization modules into a Java-based stream processing framework for context-aware systems, with focus on efficient communication and parallelization. Our approach is demonstrated for the example of a flow visualization scenario.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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