Intelligent caching and indexing techniques for relational database systems
Information Systems
Answering queries using views (extended abstract)
PODS '95 Proceedings of the fourteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Caching multidimensional queries using chunks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Matching events in a content-based subscription system
Proceedings of the eighteenth annual ACM symposium on Principles of distributed computing
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Semantic Data Caching and Replacement
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Active Query Caching for Database Web Servers
Selected papers from the Third International Workshop WebDB 2000 on The World Wide Web and Databases
A predicate-based caching scheme for client-server database architectures
The VLDB Journal — The International Journal on Very Large Data Bases
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Semantic database caching is a self-managing approach to dynamic materialization of "semantic" slices of back-end databases on servers at the edge of the network. It can be used to enhance the performance of distributed Web servers, information integration applications, and Web applications of oaded to edge servers. Such semantic caches often rely on update propagation protocols to maintain consistency with the back-end database system. However, the scalability of such update propagation protocols continues to be a major challenge. In this paper, we focus on the scalability of update propagation from back-end databases to the edge server caches. In particular, we propose a publish-subscribe like scheme for aggregating cache subscriptions at the back-end site to enhance the scalability of the ltering step required to route updates to the target caches. Our proposal exploits the template-rich nature of Web applications and promises signi cantly better scalability. In this paper, we describe our approach, discuss the tradeoffs that arise in its implementation, and estimate its scalability compared to naive update propagation schemes.