Operation Sequence Mining Based OLAP Intelligence Query Recommendation Method

Recently, with the development of Database and On-Line Analysis Processing (OLAP), its related services and products became the focus of industry. But OLAP query analysis is complex, which cannot be expertly mastered in a short time by ordinary users. How to help ordinary users to increase the efficiency of query during the process of analysis is the imperative problem of using OLAP.An operation sequence mining based OLAP query recommendation method is proposed to counter the low efficiency problem caused by the complexity of OLAP query operations, and an entire system for OLAP query sequences mining is also proposed to predict user's future query.First, abstract query sequences, in the form of numerical array, are extracted from continuous MDX (multidimensional expression) query operations. Then, a query sequences mining algorithm is exploited to obtain the frequent sequential patterns from query sequences, and a matrix of probabilities is established upon mined patterns and their sub-patterns. Finally, the next operation of current user is predicted by searching candidate patterns matched with the user's query operation or query sequence, and the prediction results are ranked according to the magnitude of probabilities. To sum up, the key contributions and main contents in this paper are as follows:(1) This paper provides a method of analyzing MDX multidimensional query language in operating. On condition of the given multidimensional database, it picks up information of dimensionality level and type of operating from sequential MDX query sentences, abstracts and transforms the information to numbers in order to change the sequential query sequence to single dimension array. It supports cross-dimension and cross-level query, as well as the types of OLAP query operating in operating (e.g. drill, slice and pivot).(2) According to the character of abstractly transferred query sequences, the thesis provides a query sequences mining algorithm based on PrefixSpan. It studies the query predicted problem based on probability, and expatiate the method of setting up a probability matrix to calculate future query operating, as well as presenting the recommendation query and result to users.(3) The performance of the proposed query recommendation method was evaluated with an OLAP query operation dataset recorded form seven professional OLAP users. The results show that with user-specific recommendation models, the average accuracy rates of the top five recommendations and the first recommendation are 92.20% and 77.06% respectively, with a common recommendation model, the average accuracy rates of the top five recommendations and the first recommendation are 81.89% and 60.85% respectively. The experiment demonstrates that the OLAP query recommendation technology advanced by this paper is adapt to the predication and recommendation both in the area of related and unrelated users.