Using active rules to maintain data consistency in data warehouse systems

Data warehousing is a popular technology, which aims at improving decision-making ability. As the result of an increasingly competitive environment, many companies are adopting a "bottom-up" approach to construct a data warehouse, since it is more likely to be on time and within budget. However, multiple independent data marts/cubes can easily cause problematic data inconsistency for anomalous update transactions, which leads to biased decision-making. This research focuses on solving the data inconsistency problem and proposing a temporal-based data consistency mechanism (TDCM) to maintain data consistency. From a relative time perspective, we use an active rule (standard ECA rule) to monitor the user query event and use a metadata approach to record related information. This both builds relationships between the different data cubes, and allows a user to define a VIT (valid interval temporal) threshold to identify the validity of interval that is a threshold to maintain data consistency. Moreover, we propose a consistency update method to update inconsistent data cubes, which can ensure all pieces of information are temporally consistent.