Integrating artificial intelligence into data warehousing and data mining

Nelson Sizwe Madonsela, Paulin Mbecke, Charles Mbohwa

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

Abstract

Knowledge engineering is key for enhancing organizational capabilities to gain a competitive edge and adapt and respond to an unpredictable market environment. Such knowledge can be generated from collected data which is often considered complex. Organizations are collecting vast amounts of data to transform them into real-Time information in order to attain successful decision-making support systems. It is more than likely that such processes can be challenging; yet such knowledge must be extracted from thoughtfully designed and implemented data warehousing, and mined to obtain the required information. This paper explores appropriate techniques, technologies and trends to facilitate the integration of artificial intelligence into data warehousing and data mining. It provides an insightful overview of data warehousing and data mining, and it highlights the techniques and the limitations of analyzing and interpreting enormous data.

Original languageEnglish
Title of host publicationWCECS 2015 - World Congress on Engineering and Computer Science 2015
EditorsCraig Douglas, Jon Burgstone, Warren S. Grundfest, Jon Burgstone, Craig Douglas, S. I. Ao
PublisherNewswood Limited
Pages819-823
Number of pages5
ISBN (Electronic)9789881404725
Publication statusPublished - 2015
Event2015 World Congress on Engineering and Computer Science, WCECS 2015 - San Francisco, United States
Duration: 21 Oct 201523 Oct 2015

Publication series

NameLecture Notes in Engineering and Computer Science
Volume2220
ISSN (Print)2078-0958

Conference

Conference2015 World Congress on Engineering and Computer Science, WCECS 2015
Country/TerritoryUnited States
CitySan Francisco
Period21/10/1523/10/15

Keywords

  • Artificial intelligence
  • Business intelligence
  • Data mining
  • Data warehousing
  • Knowledge discovery

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

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