Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Using semantic caching to manage location dependent data in mobile computing
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Exploiting location information for infostation-based hoarding
Proceedings of the 7th annual international conference on Mobile computing and networking
Mining frequent neighboring class sets in spatial databases
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Cache Invalidation and Replacement Strategies for Location-Dependent Data in Mobile Environments
IEEE Transactions on Computers
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
New prediction model for pre-fetching in mobile database
Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services
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In this paper we propose a prefetching algorithm called STAP (Spatial and Temporal Association based Prefetching algorithm). Our methods are based on the analysis of the spatial and temporal associations of the user's request using data mining techniques. First, we exploit an ”associative class set” consisting of an itemset of service classes that is close both spatially and temporally and frequently requested together. With the first method, our prefetching algorithm can select a candidate set that is spatially and temporally associated with the previous request of a user. It is shown that through performance experiments STAP is effective in improving system performance.