The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 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
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Semantic Caching and Query Processing
IEEE Transactions on Knowledge and Data Engineering
Semantic Data Caching and Replacement
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Semantic Caching in Location-Dependent Query Processing
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
A selective push algorithm for cooperative cache consistency maintenance over MANETs
EUC'07 Proceedings of the 2007 international conference on Embedded and ubiquitous computing
Collaborative spatial data sharing among mobile lightweight devices
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
Efficient Top-k Keyword Search Over Multidimensional Databases
International Journal of Data Warehousing and Mining
| Hi-index | 0.00 |
Caching has been an important technique for saving network traffic and reducing response time, especially in mobile environments where bandwidth is often a scarce resource. In this paper, we propose a novel approach for caching multidimensional data in a cluster of mobile devices. In particular, we focus on the most common types of multi-dimensional queries, namely range and k-nearest neighbor queries, by computing a cacheable region for every query, caching the result at the client, and indexing it in an R*-tree at the cluster gateway. Subsequent queries are first issued to the R*-tree and only remainder queries or queries that cannot be guaranteed exact answers are sent to the remote data server. To the best of our knowledge, our work is the first to study caching results from complex multi-dimensional queries (e.g., kNN query) and propose to build an R*-tree on previously fetched query results in a cluster of mobile devices. Rigorous experiments show that our approach significantly reduces network traffic and response time.