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Author Khalifa, Shadi ♦ Elshater, Yehia ♦ Sundaravarathan, Kiran ♦ Bhat, Aparna ♦ Martin, Patrick ♦ Imam, Fahim ♦ Rope, Dan ♦ Mcroberts, Mike ♦ Statchuk, Craig
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2016
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Orchestration ♦ Analytics talent gap ♦ Consumable analytics
Abstract With almost everything now online, organizations look at the Big Data collected to gain insights for improving their services. In the analytics process, derivation of such insights requires experimenting-with and integrating different analytics techniques, while handling the Big Data high arrival velocity and large volumes. Existing solutions cover bits-and-pieces of the analytics process, leaving it to organizations to assemble their own ecosystem or buy an off-the-shelf ecosystem that can have unnecessary components to them. We build on this point by dividing the Big Data Analytics problem into six main pillars. We characterize and show examples of solutions designed for each of these pillars. We then integrate these six pillars into a taxonomy to provide an overview of the possible state-of-the-art analytics ecosystems. In the process, we highlight a number of ecosystems to meet organizations different needs. Finally, we identify possible areas of research for building future Big Data Analytics Ecosystems.
Description Author Affiliation: Queen's University, ON, Canada (Khalifa, Shadi; Elshater, Yehia; Sundaravarathan, Kiran; Bhat, Aparna; Martin, Patrick; Imam, Fahim); IBM, Washington D.C., United States (Rope, Dan; Mcroberts, Mike); IBM, Canada, Ottawa, ON, Canada (Statchuk, Craig)
ISSN 03600300
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2016-08-02
Publisher Place New York
e-ISSN 15577341
Journal ACM Computing Surveys (CSUR)
Volume Number 49
Issue Number 2
Page Count 36
Starting Page 1
Ending Page 36


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Source: ACM Digital Library