Integrated Measurement for Pre-Fetching in Mobile Environment

摘要

Pre-fetching is used to predict next query of data items before any problems occur due to network congestion, delays, and latency problems. Lately, pre-fetching strategies become more complicated in which to support new types of application especially for mobile devices. Sometime the pre-fetched data items are not interested to the users. Due to this complication, an intelligent technique is introduced where an integrated measurement using data mining with Bayesian approach is proposed to improve the query performance. In previous study, the pre-fetched data items were filtered using data driven measurement. The data was generated based on the data frequency metrics whereby the structure of the query pattern is quantified using statistical methods. The measurement is not good enough to solve sequence query in mobile environment. In this paper, a new technique is proposed to generate new and potential pre-fetching set for the users. A subjective measurement is used to determine the pre-fetching set based on user interestingness. The integrated measurement generates strong and weak association rules based on the data and user interestingness criterions. The result shows that the performance is significantly improved whereby the technique managed to quantify the uncertainty of users' expectation in the next possible query.