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

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

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.

Original language	English
Title of host publication	Proceedings of SPIE-IS and T Electronic Imaging - Visualization and Data Analysis 2014
DOIs	https://doi.org/10.1117/12.2040533
State	Published - 3 Mar 2014
Externally published	Yes
Event	21st annual IS and T/SPIE Conference on Visualization and Analysis, VDA 2014 - San Francisco, CA, United States Duration: 3 Feb 2014 → 5 Feb 2014

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	9017
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Conference

Conference	21st annual IS and T/SPIE Conference on Visualization and Analysis, VDA 2014
Country/Territory	United States
City	San Francisco, CA
Period	3/02/14 → 5/02/14

Keywords

Streaming data
design patterns
visual analytics architecture
web-scale

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

Access to Document

10.1117/12.2040533

Cite this

Kandogan, E., Soroker, D., Rohall, S., Bak, P., Van Ham, F., Lu, J., Ship, H. J., Wang, C. F., & Lai, J. (2014). A reference web architecture and patterns for real-time visual analytics on large streaming data. In Proceedings of SPIE-IS and T Electronic Imaging - Visualization and Data Analysis 2014 Article 901708 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 9017). https://doi.org/10.1117/12.2040533

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

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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author = "Eser Kandogan and Danny Soroker and Steven Rohall and Peter Bak and {Van Ham}, Frank and Jie Lu and Ship, {Harold Jeffrey} and Wang, {Chun Fu} and Jennifer Lai",

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month = mar,

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doi = "10.1117/12.2040533",

language = "English",

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series = "Proceedings of SPIE - The International Society for Optical Engineering",

booktitle = "Proceedings of SPIE-IS and T Electronic Imaging - Visualization and Data Analysis 2014",

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Kandogan, E, Soroker, D, Rohall, S, Bak, P, Van Ham, F, Lu, J, Ship, HJ, Wang, CF & Lai, J 2014, A reference web architecture and patterns for real-time visual analytics on large streaming data. in Proceedings of SPIE-IS and T Electronic Imaging - Visualization and Data Analysis 2014., 901708, Proceedings of SPIE - The International Society for Optical Engineering, vol. 9017, 21st annual IS and T/SPIE Conference on Visualization and Analysis, VDA 2014, San Francisco, CA, United States, 3/02/14. https://doi.org/10.1117/12.2040533

A reference web architecture and patterns for real-time visual analytics on large streaming data. / Kandogan, Eser; Soroker, Danny; Rohall, Steven et al.
Proceedings of SPIE-IS and T Electronic Imaging - Visualization and Data Analysis 2014. 2014. 901708 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 9017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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