Multi-tenant elastic extension tables data management

Yaish, H; Goyal, M; Feuerlicht, G

Multi-tenant elastic extension tables data management

Yaish, H Goyal, M

Feuerlicht, G

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Publication Type:: Journal Article
Citation:: Procedia Computer Science, 2014, 29 pp. 2168 - 2181
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Yaish, H	en_US
dc.contributor.author	Goyal, M https://orcid.org/0000-0003-2853-9393	en_US
dc.contributor.author	Feuerlicht, G	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Procedia Computer Science, 2014, 29 pp. 2168 - 2181	en_US
dc.identifier.issn	1877-0509	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30477
dc.identifier.uri	http://hdl.handle.net/10453/37326
dc.description.abstract	Multi-tenant database is a new database solution which is significant for Software as a service (SaaS) and Big Data applications in the context of cloud computing paradigm. This multi-tenant database has significant design challenges to develop a solution that ensures a high level of data quality, accessibility, and manageability for the tenants using this database. In this paper, we propose a multi-tenant data management service called Elastic Extension Tables Schema Handler Service (EETSHS), which is based on a multi-tenant database schema called Elastic Extension Tables (EET). This data management service satisfies tenants' different business requirements, by creating, managing, organizing, and administrating large volumes of structured, semi-structured, and unstructured data. Furthermore, it combines traditional relational data with virtual relational data in a single database schema and allows tenants to manage this data by calling functions from this service. We present algorithms for frequently used functions of this service, and perform several experiments to measure the feasibility and effectiveness of managing multi-tenant data using these functions. We report experimental results of query execution times for managing tenants' virtual and traditional relational data showing that EET schema is a good candidate for the management of multi-tenant data for SaaS and Big Data applications. © The Authors. Published by Elsevier B.V.	en_US
dc.relation.ispartof	Procedia Computer Science	en_US
dc.relation.isbasedon	10.1016/j.procs.2014.05.202	en_US
dc.title	Multi-tenant elastic extension tables data management	en_US
dc.type	Journal Article
utslib.citation.volume	29	en_US
utslib.for	080110 Simulation and Modelling	en_US
utslib.for	080599 Distributed Computing not elsewhere classified	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	10 Technology	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - HCTD - Human Centred Technology Design
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	29	en_US

Abstract:

Multi-tenant database is a new database solution which is significant for Software as a service (SaaS) and Big Data applications in the context of cloud computing paradigm. This multi-tenant database has significant design challenges to develop a solution that ensures a high level of data quality, accessibility, and manageability for the tenants using this database. In this paper, we propose a multi-tenant data management service called Elastic Extension Tables Schema Handler Service (EETSHS), which is based on a multi-tenant database schema called Elastic Extension Tables (EET). This data management service satisfies tenants' different business requirements, by creating, managing, organizing, and administrating large volumes of structured, semi-structured, and unstructured data. Furthermore, it combines traditional relational data with virtual relational data in a single database schema and allows tenants to manage this data by calling functions from this service. We present algorithms for frequently used functions of this service, and perform several experiments to measure the feasibility and effectiveness of managing multi-tenant data using these functions. We report experimental results of query execution times for managing tenants' virtual and traditional relational data showing that EET schema is a good candidate for the management of multi-tenant data for SaaS and Big Data applications. © The Authors. Published by Elsevier B.V.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/30477