A Generalized Scalable Software Architecture for Analyzing Temporally Structured Big Data in the Cloud

Magnus Westerlund , Ulf Hedlund, Göran Pulkkis, Kaj Mikael Björk

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

6 Citations (Scopus)

Abstract

Software architectures that allow researchers to explore advanced modeling by scaling horizontally in the cloud can lead to new insights and improved accuracy of modeling results. We propose a generalized highly scalable information system architecture that researchers can employ in predictive analytics research for working with both historical data and real-time temporally structured big data. The proposed architecture is fully automated and uses the same analytical software for both training and live predictions.

Original languageEnglish
Title of host publicationNew Perspectives in Information Systems and Technologies
Number of pages11
Volume1
PublisherSpringer Verlag
Publication date01.01.2014
Pages559-569
ISBN (Print)978-3-319-05950-1
ISBN (Electronic)978-3-319-05951-8
DOIs
Publication statusPublished - 01.01.2014
MoE publication typeA4 Article in conference proceedings
Event2014 World Conference on Information Systems and Technologies, WorldCIST 2014 - Madeira, Portugal
Duration: 15.04.201418.04.2014

Publication series

NameAdvances in Intelligent Systems and Computing (AISC)
Volume275
ISSN (Print)2194-5357

Keywords

  • Predictive analytics
  • Runtime models
  • Selfadaptive systems
  • System-level design
  • Temporal data
  • 512 Business and Management
  • predictive analytics
  • temporal data
  • system-level design
  • selfadaptive systems
  • runtime models

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