A predicate matching algorithm for database rule systems
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Selection predicate indexing for active databases using interval skip lists
Information Systems
Matching events in a content-based subscription system
Proceedings of the eighteenth annual ACM symposium on Principles of distributed computing
Filtering algorithms and implementation for very fast publish/subscribe systems
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Efficient filtering in publish-subscribe systems using binary decision diagrams
ICSE '01 Proceedings of the 23rd International Conference on Software Engineering
Machine Learning
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Flexible pattern matching in strings: practical on-line search algorithms for texts and biological sequences
Interval query indexing for efficient stream processing
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Query indexing with containment-encoded intervals for efficient stream processing
Knowledge and Information Systems
Streaming queries over streaming data
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Adaptive two-level optimization for selection predicates of multiple continuous queries
Journal of Intelligent Information Systems
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Filtering queries are widely used in data stream applications. As more and more filtering queries are registered in high-speed data stream management system, the processing efficiency becomes crucial. This paper presents an efficient query index structure based on decision tree. The index structure makes full use of predicate indices on single attributes, as well as the conjunction relationship between predicates in a single query. It is easy to integrate various predicate indices into this structure. How to select dividing attributes during construction is crucial to the performance of the index tree. Two dividing attribute selection algorithms are described. One is based on information gain (IG) and the other is based on estimated time cost (ETC). The latter takes some sample tuples as a training data set and is able to build more efficient trees. Our experiments demonstrate that.