Leveraging distributed big data storage support in CLAaaS for WINGS workflow management system

Hadeel Alghamdi; Farhana Zulkernine; Patrick Martin

doi:10.1109/BigData.2017.8258200

Leveraging distributed big data storage support in CLAaaS for WINGS workflow management system

Hadeel Alghamdi
, Farhana Zulkernine
, Patrick Martin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Cloud-based Analytics-as-a-Service (CLAaaS) was developed by Zulkernine et al. with a goal to simplifying big data analytics users. It provides software-as-a-service access to a variety of back end analytics tools and data stores. One of the tools is the Workflow Instance Generation and Selection (WINGS). WINGS allows users to reuse predefined workflows and their components containing semantic meta-data to define new workflows; late binding of the workflows to data at the time of execution to enable the use of most recent data, and definition of domain specific software code as custom analytic components in workflows. How ever, the data used in WINGS for the workflows are mostly flat files that are stored on the WINGS server or shared directories. The goal of this project is to add support for big data storage systems to WINGS and validate the extensions using multiple data analytic workflows of different complexities with data residing in a variety of back end data sources. The extension allows the CLAaaS users to create, validate and execute analytic workflows in a distributed environment and use data from multiple big data storage systems. We validate our work using four big data storage systems in WINGS workflows namely, Apache HBase, MongoDB, MySQL with a front-end interface.

Original language	English
Title of host publication	Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
Editors	Jian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2426-2432
Number of pages	7
ISBN (Electronic)	9781538627143
DOIs	https://doi.org/10.1109/BigData.2017.8258200
State	Published - 1 Jul 2017
Externally published	Yes
Event	5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States Duration: 11 Dec 2017 → 14 Dec 2017

Publication series

Name	Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
Volume	2018-January

Conference

Conference	5th IEEE International Conference on Big Data, Big Data 2017
Country/Territory	United States
City	Boston
Period	11/12/17 → 14/12/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

CLAaaS
Components
Distributed environment
HBase
MongoDB
WINGS

ASJC Scopus subject areas

Computer Networks and Communications
Hardware and Architecture
Information Systems
Information Systems and Management
Control and Optimization

Access to Document

10.1109/BigData.2017.8258200

Cite this

Alghamdi, H., Zulkernine, F., & Martin, P. (2017). Leveraging distributed big data storage support in CLAaaS for WINGS workflow management system. In J.-Y. Nie, Z. Obradovic, T. Suzumura, R. Ghosh, R. Nambiar, C. Wang, H. Zang, R. Baeza-Yates, R. Baeza-Yates, X. Hu, J. Kepner, A. Cuzzocrea, J. Tang, & M. Toyoda (Eds.), Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017 (pp. 2426-2432). (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2017.8258200

Alghamdi, Hadeel ; Zulkernine, Farhana ; Martin, Patrick. / Leveraging distributed big data storage support in CLAaaS for WINGS workflow management system. Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. editor / Jian-Yun Nie ; Zoran Obradovic ; Toyotaro Suzumura ; Rumi Ghosh ; Raghunath Nambiar ; Chonggang Wang ; Hui Zang ; Ricardo Baeza-Yates ; Ricardo Baeza-Yates ; Xiaohua Hu ; Jeremy Kepner ; Alfredo Cuzzocrea ; Jian Tang ; Masashi Toyoda. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 2426-2432 (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017).

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title = "Leveraging distributed big data storage support in CLAaaS for WINGS workflow management system",

abstract = "Cloud-based Analytics-as-a-Service (CLAaaS) was developed by Zulkernine et al. with a goal to simplifying big data analytics users. It provides software-as-a-service access to a variety of back end analytics tools and data stores. One of the tools is the Workflow Instance Generation and Selection (WINGS). WINGS allows users to reuse predefined workflows and their components containing semantic meta-data to define new workflows; late binding of the workflows to data at the time of execution to enable the use of most recent data, and definition of domain specific software code as custom analytic components in workflows. How ever, the data used in WINGS for the workflows are mostly flat files that are stored on the WINGS server or shared directories. The goal of this project is to add support for big data storage systems to WINGS and validate the extensions using multiple data analytic workflows of different complexities with data residing in a variety of back end data sources. The extension allows the CLAaaS users to create, validate and execute analytic workflows in a distributed environment and use data from multiple big data storage systems. We validate our work using four big data storage systems in WINGS workflows namely, Apache HBase, MongoDB, MySQL with a front-end interface.",

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Alghamdi, H, Zulkernine, F & Martin, P 2017, Leveraging distributed big data storage support in CLAaaS for WINGS workflow management system. in J-Y Nie, Z Obradovic, T Suzumura, R Ghosh, R Nambiar, C Wang, H Zang, R Baeza-Yates, R Baeza-Yates, X Hu, J Kepner, A Cuzzocrea, J Tang & M Toyoda (eds), Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 2426-2432, 5th IEEE International Conference on Big Data, Big Data 2017, Boston, United States, 11/12/17. https://doi.org/10.1109/BigData.2017.8258200

Leveraging distributed big data storage support in CLAaaS for WINGS workflow management system. / Alghamdi, Hadeel; Zulkernine, Farhana; Martin, Patrick.
Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. ed. / Jian-Yun Nie; Zoran Obradovic; Toyotaro Suzumura; Rumi Ghosh; Raghunath Nambiar; Chonggang Wang; Hui Zang; Ricardo Baeza-Yates; Ricardo Baeza-Yates; Xiaohua Hu; Jeremy Kepner; Alfredo Cuzzocrea; Jian Tang; Masashi Toyoda. Institute of Electrical and Electronics Engineers Inc., 2017. p. 2426-2432 (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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A2 - Obradovic, Zoran

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A2 - Ghosh, Rumi

A2 - Nambiar, Raghunath

A2 - Wang, Chonggang

A2 - Zang, Hui

A2 - Baeza-Yates, Ricardo

A2 - Hu, Xiaohua

A2 - Kepner, Jeremy

A2 - Cuzzocrea, Alfredo

A2 - Tang, Jian

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PB - Institute of Electrical and Electronics Engineers Inc.

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Alghamdi H, Zulkernine F, Martin P. Leveraging distributed big data storage support in CLAaaS for WINGS workflow management system. In Nie JY, Obradovic Z, Suzumura T, Ghosh R, Nambiar R, Wang C, Zang H, Baeza-Yates R, Baeza-Yates R, Hu X, Kepner J, Cuzzocrea A, Tang J, Toyoda M, editors, Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 2426-2432. (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017). doi: 10.1109/BigData.2017.8258200

Leveraging distributed big data storage support in CLAaaS for WINGS workflow management system

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this