Linear clustering of objects with multiple attributes
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Sleepers and workaholics: caching strategies in mobile environments
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
Broadcast protocols to support efficient retrieval from databases by mobile users
ACM Transactions on Database Systems (TODS)
Client-server computing in mobile environments
ACM Computing Surveys (CSUR)
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
Location dependent query processing
Proceedings of the 2nd ACM international workshop on Data engineering for wireless and mobile access
Data Management for Mobile Computing
Data Management for Mobile Computing
Data on Air: Organization and Access
IEEE Transactions on Knowledge and Data Engineering
Mobile Computing and Databases-A Survey
IEEE Transactions on Knowledge and Data Engineering
Location Dependent Data and its Management in Mobile Databases
DEXA '98 Proceedings of the 9th International Workshop on Database and Expert Systems Applications
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Location-dependent applications (LDA) are becoming very popular in mobile computing environments. To improve system performance and facilitate disconnection in LDA, the caching policy of the mobile host (MH) is also crucial to such applications. So, most studies of LDA are concentrated on caching policies of MH. How to broadcast data is also important in order to improve system performance and minimize energy expenditure of the MH. Broadcasting method is another issue in mobile environments. We propose a broadcasting method that minimizes energy expenditure by reducing the number of download of the MH. We divide cell region into square grids and construct broadcast schedule so that the data of the adjacent grids are linearly clustered. We propose to use space-filling curves to cluster the data linearly. And we evaluate performance of each clustering method by measuring the average setup time of MHs.