The queries in Online Analytical Processing (OLAP) are user-guided. OLAP is based on a multidimensional data model for complex analytical and ad-hoc queries with a rapid execution time. Those queries are either routed or on-demand revolved around the OLAP task. Most such queries are reusable and optimized in the system. Therefore, the queries recorded in the query logs for completing various OLAP tasks may be reusable. The query logs usually contain a sequence of SQL queries that show the action flows of users for their preference, their interests, and their behaviours during the action. This research investigates the feature extraction to identify query patterns and user behaviours from historical query logs. The expected results will be used to recommend forthcoming queries to help decision makers with data analysis. The purpose of this work is to improve the efficiency and effectiveness of OLAP in terms of computation cost and response time. Furthermore, the proposed OLAP system will be able to adjust some parameters for finding common behaviours from different users that make the recommendation system flexible and user-adaptive.
|Cite as: Yang, Y. and Cao, J. (2012). Feature-based recommendation framework on OLAP. In Proc. Australasian Database Conference (ADC 2012) Melbourne, Australia. CRPIT, 124. Zhang, R. and Zhang, Y. Eds., ACS. 81-88 |
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