A practice mining system for the delivery of sustainable retirement villages

Xin Hu, Bo Xia*, Qing Chen, Martin Skitmore, Laurie Buys, Peng Wu

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)
1 Downloads (Pure)

Abstract

With the wide recognition of sustainable development, a range of sustainable practices has been incorporated into the development and operation of retirement villages to provide a sustainable living environment for residents in Australia. The retirement village sector is seeking effective methods of reusing these historical practices to facilitate the future development and operation of sustainable retirement villages. However, this is challenging and there has been no research to date into this issue. Therefore, this study aims to develop a practice mining system (PMS) to address the research gap. By using multiple case studies for data collection and case-based reasoning (CBR) for data mining, the study develops the CBR-PMS, which comprises a Data Transforming and Location System, a Data Warehouse, and a Data Mining and Reusing Engine. The CBR-PMS is a data management and mining system that can be adopted to retain, capture, reuse, and revise prior sustainable practices to facilitate the future development and operation of sustainable retirement villages. Case studies and expert judgements are used in its demonstrations and validation, and satisfactory performance is achieved. It is concluded that the CBR-PMS is an effective tool for retaining and transferring prior practices and acts as an innovative tool of knowledge management and organizational learning in the retirement living sector. Although the CBR-PMS is at its conceptual stage and requires some automation to make it user-friendly, it provides practical insights into the development of a sustainable living environment and benefits the development of data mining systems for other sustainability initiatives.

Original languageEnglish
Pages (from-to)943-956
Number of pages14
JournalJournal of Cleaner Production
Volume203
Early online date6 Sep 2018
DOIs
Publication statusPublished - 1 Dec 2018
Externally publishedYes

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