Towards cloud-based analytics-as-a-service (CLAaaS) for big data analytics in the cloud

Farhana Zulkernine, Patrick Martin, Ying Zou, Michael Bauer, Femida Gwadry-Sridhar, Ashraf Aboulnaga

Research output: Chapter in Book/Report/Conference proceedingConference contribution

29 Citations (Scopus)

Abstract

Data Analytics has proven its importance in knowledge discovery and decision support in different data and application domains. Big data analytics poses a serious challenge in terms of the necessary hardware and software resources. The cloud technology today offers a promising solution to this challenge by enabling ubiquitous and scalable provisioning of the computing resources. However, there are further challenges that remain to be addressed such as the availability of the required analytic software for various application domains, estimation and subscription of necessary resources for the analytic job or workflow, management of data in the cloud, and design, verification and execution of analytic workflows. We present a taxonomy for analytic workflow systems to highlight the important features in existing systems. Based on the taxonomy and a study of the existing analytic software and systems, we propose the conceptual architecture of CLoud-based Analytics-as-a-Service (CLAaaS), a big data analytics service provisioning platform, in the cloud. We outline the features that are important for CLAaaS as a service provisioning system such as user and domain specific customization and assistance, collaboration, modular architecture for scalable deployment and Service Level Agreement.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE International Congress on Big Data, BigData 2013
Pages62-69
Number of pages8
DOIs
Publication statusPublished - 28 Oct 2013
Externally publishedYes
Event2013 IEEE International Congress on Big Data, BigData 2013 - Santa Clara, CA, United States
Duration: 27 Jun 20132 Jul 2013

Other

Other2013 IEEE International Congress on Big Data, BigData 2013
CountryUnited States
CitySanta Clara, CA
Period27/6/132/7/13

Fingerprint

Taxonomies
Data mining
Availability
Hardware
Big data

Keywords

  • AaaS
  • analysis
  • Analytics
  • CLAaaS
  • cloud
  • scientific workflow management system
  • service
  • taxonomy
  • workflow

ASJC Scopus subject areas

  • Computer Science Applications

Cite this

Zulkernine, F., Martin, P., Zou, Y., Bauer, M., Gwadry-Sridhar, F., & Aboulnaga, A. (2013). Towards cloud-based analytics-as-a-service (CLAaaS) for big data analytics in the cloud. In Proceedings - 2013 IEEE International Congress on Big Data, BigData 2013 (pp. 62-69). [6597120] https://doi.org/10.1109/BigData.Congress.2013.18

Towards cloud-based analytics-as-a-service (CLAaaS) for big data analytics in the cloud. / Zulkernine, Farhana; Martin, Patrick; Zou, Ying; Bauer, Michael; Gwadry-Sridhar, Femida; Aboulnaga, Ashraf.

Proceedings - 2013 IEEE International Congress on Big Data, BigData 2013. 2013. p. 62-69 6597120.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zulkernine, F, Martin, P, Zou, Y, Bauer, M, Gwadry-Sridhar, F & Aboulnaga, A 2013, Towards cloud-based analytics-as-a-service (CLAaaS) for big data analytics in the cloud. in Proceedings - 2013 IEEE International Congress on Big Data, BigData 2013., 6597120, pp. 62-69, 2013 IEEE International Congress on Big Data, BigData 2013, Santa Clara, CA, United States, 27/6/13. https://doi.org/10.1109/BigData.Congress.2013.18
Zulkernine F, Martin P, Zou Y, Bauer M, Gwadry-Sridhar F, Aboulnaga A. Towards cloud-based analytics-as-a-service (CLAaaS) for big data analytics in the cloud. In Proceedings - 2013 IEEE International Congress on Big Data, BigData 2013. 2013. p. 62-69. 6597120 https://doi.org/10.1109/BigData.Congress.2013.18
Zulkernine, Farhana ; Martin, Patrick ; Zou, Ying ; Bauer, Michael ; Gwadry-Sridhar, Femida ; Aboulnaga, Ashraf. / Towards cloud-based analytics-as-a-service (CLAaaS) for big data analytics in the cloud. Proceedings - 2013 IEEE International Congress on Big Data, BigData 2013. 2013. pp. 62-69
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