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With the development of modern information technologies and more frequent utilization of information systems to operation and maintenance (Ou0026M) management, a great amount of Ou0026M 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 Ou0026M 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 Ou0026M 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 Ou0026M management.

A Data-Driven Approach to Improve the Operation and Maintenance Management of Large Public Buildings