A reference web architecture and patterns for real-time visual analytics on large streaming data

Eser Kandogan; Danny Soroker; Steven Rohall; Peter Bak; Frank van Ham; Jie Lu; Harold-Jeffrey Ship; Chun-Fu Wang; Jennifer Lai

doi:10.1117/12.2040533

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3 February 2014 A reference web architecture and patterns for real-time visual analytics on large streaming data

Eser Kandogan, Danny Soroker, Steven Rohall, Peter Bak, Frank van Ham, Jie Lu, Harold-Jeffrey Ship, Chun-Fu Wang, Jennifer Lai

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Proceedings Volume 9017, Visualization and Data Analysis 2014; 901708 (2014) https://doi.org/10.1117/12.2040533
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States

Abstract

Monitoring and analysis of streaming data, such as social media, sensors, and news feeds, has become increasingly important for business and government. The volume and velocity of incoming data are key challenges. To effectively support monitoring and analysis, statistical and visual analytics techniques need to be seamlessly integrated; analytic techniques for a variety of data types (e.g., text, numerical) and scope (e.g., incremental, rolling-window, global) must be properly accommodated; interaction, collaboration, and coordination among several visualizations must be supported in an efficient manner; and the system should support the use of different analytics techniques in a pluggable manner. Especially in web-based environments, these requirements pose restrictions on the basic visual analytics architecture for streaming data. In this paper we report on our experience of building a reference web architecture for real-time visual analytics of streaming data, identify and discuss architectural patterns that address these challenges, and report on applying the reference architecture for real-time Twitter monitoring and analysis.

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Eser Kandogan, Danny Soroker, Steven Rohall, Peter Bak, Frank van Ham, Jie Lu, Harold-Jeffrey Ship, Chun-Fu Wang, and Jennifer Lai "A reference web architecture and patterns for real-time visual analytics on large streaming data", Proc. SPIE 9017, Visualization and Data Analysis 2014, 901708 (3 February 2014); https://doi.org/10.1117/12.2040533

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