CQ: a personalized update monitoring toolkit
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Towards interoperable heterogeneous information systems: an experiment using the DIOM approach
SAC '97 Proceedings of the 1997 ACM symposium on Applied computing
NiagaraCQ: a scalable continuous query system for Internet databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Continually evaluating similarity-based pattern queries on a streaming time series
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Information Monitoring on the Web: A Scalable Solution
World Wide Web
Scaling Access to Heterogeneous Data Sources with DISCO
IEEE Transactions on Knowledge and Data Engineering
Continual Queries for Internet Scale Event-Driven Information Delivery
IEEE Transactions on Knowledge and Data Engineering
LeedsCQ: A Scalable Continual Queries System
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
A learning-based approach to estimate statistics of operators in continuous queries: a case study
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Towards scalable location-aware services: requirements and research issues
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
K2/Kleisli and GUS: experiments in integrated access to genomic data sources
IBM Systems Journal - Deep computing for the life sciences
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We define continual queries as a useful tool for monitoring of updated information. Continual queries are standing queries that monitor the source data and notify the users whenever new data matches the query. In addition to periodic refresh, continual queries include Epsilon Transaction concepts to allow users to specify query refresh based on the magnitude of updates. To support efficient processing of continual queries, we propose a differential re-evaluation algorithm (DRA), which exploits the structure and information contained in both the query expressions and the database update operations. The DRA design can be seen as a synthesis of previous research on differential files, incremental view maintenance, and active databases.