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Abstract
With the development of modern information technologies and more frequent utilization of information systems to operation and maintenance (O&M) management, a great amount of O&M data are collected nowadays. However, because of the large volume and poor quality, as well as a lack of effective data analysis techniques, these data are rarely analyzed and translated into useful knowledge for O&M decisions. This study presents a data model, which is named as datacube with multi-dimensional and unrestrained characteristics, for these data to better support data mining algorithms. The model organizes all the different data in both relational database and in the memories and is able to support analysis-requirements-oriented data extractions. Based on this datacube, an O&M data mining approach is proposed with procedures of data preparation, data clustering and data mining. The proposed datacube-based data mining approach was applied to the Kunming Chang Shui international airport terminal. More than 7 years on-site repairing data were used for data mining and the outcomes verified the model and the approach to be feasible and valuable for improving O&M management.
Highlights
Operation and maintenance (O&M) phase lasts the longest and costs the most within the building lifecycle [1]
The proposed datacube-based data mining approach was applied to the Kunming Chang Shui international airport terminal
A DATA MINING APPROACH BASED ON THE DATACUBE This study proposes an approach for utilizing raw data generated during O&M management to improve the O&M management performance
Summary
Operation and maintenance (O&M) phase lasts the longest and costs the most within the building lifecycle [1]. C: SERVICE INFORMATION The O&M management within a large public building usually involves multiple aspects of services such as property management, business management and cleaning/water supply, which can be used as the first level nodes of service data hierarchy according to the hierarchical structure. The datacube is suitable and capable to represent and integrate the information within these processes In this repair work example, the raw data scattered across multiple tables are first integrated into one data warehouse. Considering that the reports and feedbacks are usually recorded in natural languages, such as the location and fault descriptions in the report details and the feedback information, different with other clustering algorithms, this research first adopts text segmentation to obtain the keywords of the raw data, and the combination of the keywords is TABLE 2. With requirements on more accurate analyses, further clustering can be conducted by the facility names or other characteristics
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