VALID: A web framework for visual analytics of large streaming data

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Visual analytics of increasingly large data sets has become a challenge for traditional in-memory and off-line algorithms as well as in the cognitive process of understanding features at various scales of resolution. In this paper, we attempt a new web-based framework for the dynamic visualization of large data. The framework is based on the idea that no physical device can ever catch up to the analytical demand and the physical requirements of large data. Thus, we adopt a data streaming generator model that serializes the original data into multiple streams of data that can be contained on current hardware. Thus, the scalability of the visual analytics of large data is inherent in the streaming architecture supported by our platform. The platform is based on the traditional server-client model. However, the platform is enhanced by effective analytical methods that operate on data streams, such as binned points and bundling lines that reduce and enhance large streams of data for effective interactive visualization. We demonstrate the effectiveness of our framework on different types of large datasets.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages686-692
Number of pages7
ISBN (Electronic)9781479965137
DOIs
StatePublished - 15 Jan 2015
Externally publishedYes
Event13th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2014 - Beijing, China
Duration: 24 Sep 201426 Sep 2014

Publication series

NameProceedings - 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2014

Conference

Conference13th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2014
Country/TerritoryChina
CityBeijing
Period24/09/1426/09/14

Keywords

  • Big data
  • Dynamic visualization
  • Streaming data
  • Visual analytics

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