Asset management data warehouse data modelling
Mathew, Avin D. (2008) Asset management data warehouse data modelling. PhD thesis, Queensland University of Technology.
Data are the lifeblood of an organisation, being employed by virtually all business functions within a firm. Data management, therefore, is a critical process in prolonging the life of a company and determining the success of each of an organisation’s business functions. The last decade and a half has seen data warehousing rising in priority within corporate data management as it provides an effective supporting platform for decision support tools. A cross-sectional survey conducted by this research showed that data warehousing is starting to be used within organisations for their engineering asset management, however the industry uptake is slow and has much room for development and improvement. This conclusion is also evidenced by the lack of systematic scholarly research within asset management data warehousing as compared to data warehousing for other business areas. This research is motivated by the lack of dedicated research into asset management data warehousing and attempts to provide original contributions to the area, focussing on data modelling. Integration is a fundamental characteristic of a data warehouse and facilitates the analysis of data from multiple sources. While several integration models exist for asset management, these only cover select areas of asset management. This research presents a novel conceptual data warehousing data model that integrates the numerous asset management data areas. The comprehensive ethnographic modelling methodology involved a diverse set of inputs (including data model patterns, standards, information system data models, and business process models) that described asset management data. Used as an integrated data source, the conceptual data model was verified by more than 20 experts in asset management and validated against four case studies. A large section of asset management data are stored in a relational format due to the maturity and pervasiveness of relational database management systems. Data warehousing offers the alternative approach of structuring data in a dimensional format, which suggests increased data retrieval speeds in addition to reducing analysis complexity for end users. To investigate the benefits of moving asset management data from a relational to multidimensional format, this research presents an innovative relational vs. multidimensional model evaluation procedure. To undertake an equitable comparison, the compared multidimensional are derived from an asset management relational model and as such, this research presents an original multidimensional modelling derivation methodology for asset management relational models. Multidimensional models were derived from the relational models in the asset management data exchange standard, MIMOSA OSA-EAI. The multidimensional and relational models were compared through a series of queries. It was discovered that multidimensional schemas reduced the data size and subsequently data insertion time, decreased the complexity of query conceptualisation, and improved the query execution performance across a range of query types. To facilitate the quicker uptake of these data warehouse multidimensional models within organisations, an alternate modelling methodology was investigated. This research presents an innovative approach of using a case-based reasoning methodology for data warehouse schema design. Using unique case representation and indexing techniques, the system also uses a business vocabulary repository to augment case searching and adaptation. The system was validated through a case-study where multidimensional schema design speed and accuracy was measured. It was found that the case-based reasoning system provided a marginal benefit, with a greater benefits gained when confronted with more difficult scenarios.
Impact and interest:
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||QUT Thesis (PhD)|
|Supervisor:||Ma, Lin, Hargreaves, Douglas, & Narasimhan, Lakshmi|
|Keywords:||asset management, data warehousing, conceptual data model, multidimensional model, star schemas, case-based reasoning|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering|
|Institution:||Queensland University of Technology|
|Deposited On:||31 Mar 2009 10:12|
|Last Modified:||29 Oct 2011 05:52|
Repository Staff Only: item control page