ACM Transactions on Database Systems (TODS)
Continuous queries over append-only databases
SIGMOD '92 Proceedings of the 1992 ACM SIGMOD international conference on Management of data
Cost-based query scrambling for initial delays
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Dynamic Query Operator Scheduling for Wide-Area Remote Access
Distributed and Parallel Databases
Efficient and extensible algorithms for multi query optimization
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Eddies: continuously adaptive query processing
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
Cache investment: integrating query optimization and distributed data placement
ACM Transactions on Database Systems (TODS)
Scrambling query plans to cope with unexpected delays
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Continual Queries for Internet Scale Event-Driven Information Delivery
IEEE Transactions on Knowledge and Data Engineering
Optimizing Queries with Materialized Views
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Cache-on-Demand: Recycling with Certainty
Proceedings of the 17th International Conference on Data Engineering
Efficient Filtering of XML Documents for Selective Dissemination of Information
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Towards Sensor Database Systems
MDM '01 Proceedings of the Second International Conference on Mobile Data Management
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Danaïdes: continuous and progressive complex queries on RSS feeds
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Efficient processing of multiple DTW queries in time series databases
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Efficient algorithms for collaborative decision making for large scale settings
Proceedings of the 3rd international workshop on Collaborative information retrieval
Utility-driven data acquisition in participatory sensing
Proceedings of the 16th International Conference on Extending Database Technology
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Continuous queries are queries executed on data streams within a potentially open-ended time interval specified by the user and are usually long running. The data streams are likely to exhibit fluctuating characteristics such as varying inter-arrival times, as well as varying data characteristics during the query execution. In the presence of such unpredictable factors, continuous query systems must still be able to efficiently handle large number of queries, as well as to offer acceptable individual query performance.In this paper, we propose and discuss a novel framework, called AdaptiveCQ, for the efficient processing of multiple continuous queries. In our framework, multiple queries share intermediate results at a fine level of granularity. Unlike previous approaches to sharing or reusing that relied on materialization to disk, AdaptiveCQ allows on-the-fly sharing of results. We show that this feature improves both the initial query response time, and the overall response time. Finally, AdaptiveCQ, which extrapolates the idea proposed by the eddy query-processing model, adapts well to fluctuations of the data streams characteristics by this combination of fine grain and on-the-fly sharing. We implemented AdaptiveCQ from scratch in Java and made use of it to conduct the experiments. We present experimental results that substantiate our claim that AdaptiveCQ can provide substantial performance improvements over existing methods of reusing intermediate results that relied on materialization to disk. In addition, we also show that AdaptiveCQ can adapt well to fluctuations in the query environment.