Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Balancing push and pull for data broadcast
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Dynamic multicast information dissemination in hybrid satellite-wireless networks
Proceedings of the 1st ACM international workshop on Data engineering for wireless and mobile access
Distributed selective dissemination of information
PDIS '94 Proceedings of the third international conference on on Parallel and distributed information systems
Handbook of wireless networks and mobile computing
Exploiting Data Mining Techniques for Broadcasting Data in Mobile Computing Environments
IEEE Transactions on Knowledge and Data Engineering
Adaptive Broadcast Protocols to Support Power Conservant Retrieval by Mobile Users
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Data Staging for On-Demand Broadcast
Proceedings of the 27th International Conference on Very Large Data Bases
Disseminating Updates on Broadcast Disks
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Adaptive Data Broadcast in Hybrid Networks
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Pull-based data broadcast with dependencies: be fair to users, not to items
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Two-phase exclusion based broadcast adaptation in wireless networks
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
Customized newspaper broadcast: data broadcast with dependencies
LATIN'06 Proceedings of the 7th Latin American conference on Theoretical Informatics
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A key element in many mobile application systems is the realization of efficient data delivery from server to mobile clients. Although broadcast has been proved to be an efficient data dissemination technique, selection of broadcast data is still an active research area. In this paper, we mainly studied the functions of the implicit regularities attached to clients' request data in the selection of broadcast data. Furthermore, we put forward a correlation-based broadcast model, which selects broadcast data items according to data access frequencies as well as their correlations. The primary rationale underneath is the fact that some data are prone to be accessed if certain data are reached. The results from extensive simulation experiments shows that the correlation-based broadcast can significantly improve the mean response time and reduce the number of client requests.