Continually evaluating similarity-based pattern queries on a streaming time series
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Stream processing of XPath queries with predicates
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
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
Path sharing and predicate evaluation for high-performance XML filtering
ACM Transactions on Database Systems (TODS)
Dynamic plan migration for continuous queries over data streams
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Implementing a scalable XML publish/subscribe system using relational database systems
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Continuous Similarity-Based Queries on Streaming Time Series
IEEE Transactions on Knowledge and Data Engineering
Efficient scheduling of heterogeneous continuous queries
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
State-slice: new paradigm of multi-query optimization of window-based stream queries
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Physical Database Design: the database professional's guide to exploiting indexes, views, storage, and more
Scalable event matching for overlapping subscriptions in pub/sub systems
Proceedings of the 2007 inaugural international conference on Distributed event-based systems
Algorithms and metrics for processing multiple heterogeneous continuous queries
ACM Transactions on Database Systems (TODS)
Information Sciences: an International Journal
Query processing of multi-way stream window joins
The VLDB Journal — The International Journal on Very Large Data Bases
Near-optimal algorithms for shared filter evaluation in data stream systems
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Prefilter: predicate pushdown at streaming speeds
SSPS '08 Proceedings of the 2nd international workshop on Scalable stream processing system
Validated cost models for sensor network queries
Proceedings of the Sixth International Workshop on Data Management for Sensor Networks
Query result caching for multiple event-driven continuous queries
Information Systems
Predicate indexing for incremental multi-query optimization
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Parallel processing of continuous queries over data streams
Distributed and Parallel Databases
Cheetah: a high performance, custom data warehouse on top of MapReduce
Proceedings of the VLDB Endowment
SNEE: a query processor for wireless sensor networks
Distributed and Parallel Databases
Adaptive optimization for multiple continuous queries
Data & Knowledge Engineering
Efficient in-network evaluation of multiple queries
HiPC'06 Proceedings of the 13th international conference on High Performance Computing
Shared execution strategy for neighbor-based pattern mining requests over streaming windows
ACM Transactions on Database Systems (TODS)
ARGUS: rete + DBMS = efficient persistent profile matching on large-volume data streams
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
An embedded co-processor for accelerating window joins over uncertain data streams
Microprocessors & Microsystems
Adaptive two-level optimization for selection predicates of multiple continuous queries
Journal of Intelligent Information Systems
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In this paper, we design and evaluate alternative selection placement strategies for optimizing a very large number of continuous queries in an Internet environment. Two grouping strategies, PushDown and PullUp, in which selections are either pushed below, or pulled above, joins are proposed and investigated. While our earlier research has demonstrated that the incremental group optimization can significantly outperform an ungrouped approach, the results from this paper show that different incremental group optimization strategies can have significantly different performance characteristics. Surprisingly, in our studies, PullUp, in which selections are pulled above joins, is often better and achieves an average 10-fold performance improvement over PushDown (occasionally 100 times faster). Furthermore, a revised algorithm of PullUp, termed filtered PullUp is proposed that is able to further reduce the cost of PullUp by 75% when the union of the selection predicates is selective. Detailed cost models, which consider several special parameters, including (1) characteristics of queries to be grouped, and (2) characteristics of data changes, are presented in this paper. Preliminary experiments using an implementation of both strategies show that our models are fairly accurate in predicting the results obtained from the implementation of these techniques in the Niagara system. This work can serve as the basis for building a cost-based incremental group query optimizer to choose a better grouping strategy.