ACM Transactions on Database Systems (TODS)
Efficient and extensible algorithms for multi query optimization
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Materialized view selection and maintenance using multi-query optimization
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Algorithms and Theory of Computation Handbook
Algorithms and Theory of Computation Handbook
Continuously adaptive continuous queries over streams
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Exploiting Punctuation Semantics in Continuous Data Streams
IEEE Transactions on Knowledge and Data Engineering
Issues in data stream management
ACM SIGMOD Record
Design and Evaluation of Alternative Selection Placement Strategies in Optimizing Continuous Queries
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Aurora: a new model and architecture for data stream management
The VLDB Journal — The International Journal on Very Large Data Bases
Adaptive Caching for Continuous Queries
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
SemCast: Semantic Multicast for Content-Based Data Dissemination
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Multiple aggregations over data streams
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Content-based routing: different plans for different data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Nile-PDT: a phenomenon detection and tracking framework for data stream management systems
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Run-time operator state spilling for memory intensive long-running queries
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
On-the-fly sharing for streamed aggregation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Streaming queries over streaming data
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Monitoring streams: a new class of data management applications
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Scheduling for shared window joins over data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Load shedding in a data stream manager
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Memory-limited execution of windowed stream joins
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Resource sharing in continuous sliding-window aggregates
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
The case for precision sharing
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
CAPE: continuous query engine with heterogeneous-grained adaptivity
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Multiple-Objective Compression of Data Cubes in Cooperative OLAP Environments
ADBIS '08 Proceedings of the 12th East European conference on Advances in Databases and Information Systems
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
An Optimization Technique for Multiple Continuous Multiple Joins over Data Streams
DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
A shared execution strategy for multiple pattern mining requests over streaming data
Proceedings of the VLDB Endowment
Distributed stream join query processing with semijoins
Distributed and Parallel Databases
Journal of Intelligent Information Systems
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
On-line sensing task optimization for shared sensors
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
E-Cube: multi-dimensional event sequence analysis using hierarchical pattern query sharing
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Optimized processing of multiple aggregate continuous queries
Proceedings of the 20th ACM international conference on Information and knowledge management
Adaptive optimization for multiple continuous queries
Data & Knowledge Engineering
Shared execution strategy for neighbor-based pattern mining requests over streaming windows
ACM Transactions on Database Systems (TODS)
Integrating a stream processing engine and databases for persistent streaming data management
DEXA'07 Proceedings of the 18th international conference on Database and Expert Systems Applications
Loyalty-based selection: retrieving objects that persistently satisfy criteria
Proceedings of the 21st ACM international conference on Information and knowledge management
Multi-query optimization for semantic news feed query
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
Hi-index | 0.00 |
Modern stream applications such as sensor monitoring systems and publish/subscription services necessitate the handling of large numbers of continuous queries specified over high volume data streams. Efficient sharing of computations among multiple continuous queries, especially for the memory- and CPU-intensive window-based operations, is critical. A novel challenge in this scenario is to allow resource sharing among similar queries, even if they employ windows of different lengths. This paper first reviews the existing sharing methods in the literature, and then illustrates the significant performance shortcomings of these methods.This paper then presents a novel paradigm for the sharing of window join queries. Namely we slice window states of a join operator into fine-grained window slices and form a chain of sliced window joins. By using an elaborate pipelining methodology, the number of joins after state slicing is reduced from quadratic to linear. This novel sharing paradigm enables us to push selections down into the chain and flexibly select subsequences of such sliced window joins for computation sharing among queries with different window sizes. Based on the state-slice sharing paradigm, two algorithms are proposed for the chain buildup. One minimizes the memory consumption while the other minimizes the CPU usage. The algorithms are proven to find the optimal chain with respect to memory or CPU usage for a given query workload. We have implemented the slice-share paradigm within the data stream management system CAPE. The experimental results show that our strategy provides the best performance over a diverse range of workload settings among all alternate solutions in the literature.