ESTABLISHING A STANDARD SCIENTIFIC GUIDELINE FOR THE EVALUATION AND ADOPTION OF MULTI-TENANT DATABASE

2.50
Hdl Handle:
http://hdl.handle.net/2436/620518
Title:
ESTABLISHING A STANDARD SCIENTIFIC GUIDELINE FOR THE EVALUATION AND ADOPTION OF MULTI-TENANT DATABASE
Authors:
MATTHEW, OLUMUYIWA
Abstract:
A Multi-tenant database (MTD) is a way of deploying a Database as a Service (DaaS). A multi-tenant database refers to a principle where a single instance of a Database Management System (DBMS) runs on a server, serving multiple clients organisations (tenants). This technology has helped to discard the large-scale investments in hardware and software resources, in upgrading them regularly and in expensive licences of application software used on in-house hosted database systems. This is gaining momentum with significant increase in the number of organisations ready to take advantage of the technology. The benefits of MTD are potentially enormous but for any organisation to venture into its adoption, there are some salient factors which must be well understood and examined before venturing into the concept. This research examines these factors, different models of MTD, consider the requirements and challenges of implementing MTDs. Investigation of the degree of impact each of these factors has on the adoption of MTD is conducted in this research which focused mainly on public organisations. The methodology adopted in undertaking this study is a mixed method which involved both qualitative and quantitative research approaches. These strategies are used here to cover statistics (quantifiable data) and experts’ knowledge and experiences (abstract data) in order to satisfactorily achieve the aim and objectives and complete the research. Following the involvement of these strategies, a framework was developed and further refined after a second survey was carried out with a quantitative approach. This framework will help prospective tenants to make informed decisions about the adoption of the concept. The research also considers the direction of decisions about MTDs in situations where two or more factors are combined. A new MTD framework is presented that improves the decision making process of MTD adoption. Also, an Expert System (ES) is developed from the framework which was validated via a survey and analysed with the aid of SPSS software. The findings from the validation indicated that the framework is valuable and suitable for use in practice since majority of respondents accepted the research findings and recommendations for success. Likewise, the ES was validated with majority of participants accepting it and embracing the high level of its friendliness.
Issue Date:
2016
URI:
http://hdl.handle.net/2436/620518
Type:
Thesis
Language:
en
Description:
A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.
Appears in Collections:
E-Theses

Full metadata record

DC FieldValue Language
dc.contributor.authorMATTHEW, OLUMUYIWAen
dc.date.accessioned2017-06-15T15:23:12Z-
dc.date.available2017-06-15T15:23:12Z-
dc.date.issued2016-
dc.identifier.urihttp://hdl.handle.net/2436/620518-
dc.descriptionA thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.en
dc.description.abstractA Multi-tenant database (MTD) is a way of deploying a Database as a Service (DaaS). A multi-tenant database refers to a principle where a single instance of a Database Management System (DBMS) runs on a server, serving multiple clients organisations (tenants). This technology has helped to discard the large-scale investments in hardware and software resources, in upgrading them regularly and in expensive licences of application software used on in-house hosted database systems. This is gaining momentum with significant increase in the number of organisations ready to take advantage of the technology. The benefits of MTD are potentially enormous but for any organisation to venture into its adoption, there are some salient factors which must be well understood and examined before venturing into the concept. This research examines these factors, different models of MTD, consider the requirements and challenges of implementing MTDs. Investigation of the degree of impact each of these factors has on the adoption of MTD is conducted in this research which focused mainly on public organisations. The methodology adopted in undertaking this study is a mixed method which involved both qualitative and quantitative research approaches. These strategies are used here to cover statistics (quantifiable data) and experts’ knowledge and experiences (abstract data) in order to satisfactorily achieve the aim and objectives and complete the research. Following the involvement of these strategies, a framework was developed and further refined after a second survey was carried out with a quantitative approach. This framework will help prospective tenants to make informed decisions about the adoption of the concept. The research also considers the direction of decisions about MTDs in situations where two or more factors are combined. A new MTD framework is presented that improves the decision making process of MTD adoption. Also, an Expert System (ES) is developed from the framework which was validated via a survey and analysed with the aid of SPSS software. The findings from the validation indicated that the framework is valuable and suitable for use in practice since majority of respondents accepted the research findings and recommendations for success. Likewise, the ES was validated with majority of participants accepting it and embracing the high level of its friendliness.en
dc.language.isoenen
dc.subjectDATABASEen
dc.subjectMULTI-TENANTen
dc.subjectDATABASE AS A SERVICEen
dc.subjectDECISIONen
dc.subjectFRAMEWORKen
dc.subjectGUIDELINESen
dc.subjectEXPERT SYSTEMen
dc.subjectEVALUATIONen
dc.subjectADOPTIONen
dc.subjectREJECTIONen
dc.subjectMODELen
dc.titleESTABLISHING A STANDARD SCIENTIFIC GUIDELINE FOR THE EVALUATION AND ADOPTION OF MULTI-TENANT DATABASEen
dc.typeThesisen
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