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