Cloudet: A cloud-driven visual cognition of large streaming data

George Baciu, Chenhui Li, Yunzhe Wang, Xiujun Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Streaming data cognition has become a dominant problem in interactive visual analytics for event detection, meteorology, cosmology, security, and smart city applications. In order to interact with streaming data patterns in an elastic cloud environment, we present a new elastic framework for big data visual analytics in the cloud, the Cloudet. The Cloudet is a self- Adaptive cloud-based platform that treats both data and compute nodes as elastic objects. The main objective is to readily achieve the scalability and elasticity of cloud computing platforms in order to process large streaming data and adapt to potential interactions between data stream features. Our main contributions include a robust cloud-based framework called the Cloudet. This is a cloud profile manager that attempts to optimize resource parameters in order to achieve expressivity, scalability, reliability, and the proper aggregation of the compute nodes and data streams into several density maps for the purpose of dynamic visualization.
Original languageEnglish
Pages (from-to)12-31
Number of pages20
JournalInternational Journal of Cognitive Informatics and Natural Intelligence
Volume10
Issue number1
DOIs
Publication statusPublished - 1 Jan 2016

Keywords

  • Big data
  • Cloud computing
  • Cognitive visualization
  • Streaming data
  • Visual cognition

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Artificial Intelligence

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