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Proceedings Paper

A reference web architecture and patterns for real-time visual analytics on large streaming data
Author(s): 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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Paper 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.

Paper Details

Date Published: 3 February 2014
PDF: 15 pages
Proc. SPIE 9017, Visualization and Data Analysis 2014, 901708 (3 February 2014); doi: 10.1117/12.2040533
Show Author Affiliations
Eser Kandogan, IBM Research (United States)
Danny Soroker, IBM Research (United States)
Steven Rohall, IBM Research (United States)
Peter Bak, IBM Research (United States)
Frank van Ham, IBM Research (United States)
Jie Lu, IBM Research (United States)
Harold-Jeffrey Ship, IBM Research (United States)
Chun-Fu Wang, Univ. of California, Davis (United States)
Jennifer Lai, IBM Research (United States)


Published in SPIE Proceedings Vol. 9017:
Visualization and Data Analysis 2014
Pak Chung Wong; David L. Kao; Ming C. Hao; Chaomei Chen, Editor(s)

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