ACM Transactions on Database Systems (TODS)
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Exploiting Punctuation Semantics in Continuous Data Streams
IEEE Transactions on Knowledge and Data Engineering
Update-pattern-aware modeling and processing of continuous queries
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
The 8 requirements of real-time stream processing
ACM SIGMOD Record
On-the-fly sharing for streamed aggregation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
The CQL continuous query language: semantic foundations and query execution
The VLDB Journal — The International Journal on Very Large Data Bases
Multi-query optimization of sliding window aggregates by schedule synchronization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Scheduling for shared window joins over data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Resource sharing in continuous sliding-window aggregates
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Towards a streaming SQL standard
Proceedings of the VLDB Endowment
Multi-granular Time-Based Sliding Windows over Data Streams
TIME '10 Proceedings of the 2010 17th International Symposium on Temporal Representation and Reasoning
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Shared evaluation of multiple user requests is an utmost priority for stream processing engines in order to achieve high throughput and provide timely results. Given that most continuous queries specify windowing constraints, we suggest a multi-level scheme for concurrent evaluation of time-based sliding windows seeking for potential subsumptions among them. As requests may be registered or suspended dynamically, we develop a technique for choosing the most suitable embedding of a given window into a group composed of multi-grained time frames already employed for other queries. Intuitively, the proposed methodology "clusters" windowed operators into common hierarchical constructs, thus drastically reducing the need for their separate evaluation. Our empirical study confirms that such a scheme achieves dramatic memory savings with almost negligible maintenance cost.