Principles of artificial intelligence
Principles of artificial intelligence
ACM Transactions on Database Systems (TODS)
Statistical profile estimation in database systems
ACM Computing Surveys (CSUR)
EDBT '94 Proceedings of the 4th international conference on extending database technology: Advances in database technology
DynaMat: a dynamic view management system for data warehouses
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Semantic caching via query matching for web sources
Proceedings of the eighth international conference on Information and knowledge management
Self maintenance of multiple views in data warehousing
Proceedings of the eighth international conference on Information and knowledge management
Efficient and extensible algorithms for multi query optimization
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
NiagaraCQ: a scalable continuous query system for Internet databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Query Optimization in Database Systems
ACM Computing Surveys (CSUR)
Continual Queries for Internet Scale Event-Driven Information Delivery
IEEE Transactions on Knowledge and Data Engineering
Selection of Views to Materialize in a Data Warehouse
ICDT '97 Proceedings of the 6th International Conference on Database Theory
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
LeedsCQ: A Scalable Continual Queries System
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
Computing queries from derived relations
VLDB '85 Proceedings of the 11th international conference on Very Large Data Bases - Volume 11
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Continual Queries (CQs) allow users to receive new information as it becomes available. CQ systems need to support a large number of CQs due to the scale of the Internet. One approach to this problem is to group CQs so that they share their computation on the assumption that many CQs have similar structure. Grouping queries optimizes the evaluation of the queries by executing common operations in the group of queries just once. However, traditional grouping techniques are not suitable for CQs because their grouping raises new issues. In this paper we propose a scalable and dynamic CQ grouping technique. Our grouping strategy is incremental in that it scales to a large number of queries. It also re-groups existing grouped queries dynamically to maintain the effectiveness of the groups.