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

Chenhui Li, George Baciu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

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
PublisherIEEE
Pages686-692
Number of pages7
ISBN (Electronic)9781479965137
DOIs
Publication statusPublished - 1 Jan 2015
Event13th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2014 - Beijing, China
Duration: 24 Sep 201426 Sep 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

ASJC Scopus subject areas

  • Computer Science Applications
  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications

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