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
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Continual Queries for Internet Scale Event-Driven Information Delivery
IEEE Transactions on Knowledge and Data Engineering
Dynamic Querying of Streaming Data with the dQUOB System
IEEE Transactions on Parallel and Distributed Systems
dQUOB: Managing Large Data Flows Using Dynamic Embedded Queries
HPDC '00 Proceedings of the 9th IEEE International Symposium on High Performance Distributed Computing
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
Software Approach To Hazard Detection Using On-Line Analysis Of Safety Constraints
SRDS '97 Proceedings of the 16th Symposium on Reliable Distributed Systems
Fjording the Stream: An Architecture for Queries Over Streaming Sensor Data
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
GATES: A Grid-Based Middleware for Processing Distributed Data Streams
HPDC '04 Proceedings of the 13th IEEE International Symposium on High Performance Distributed Computing
Supporting dynamic migration in tightly coupled grid applications
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
Stream processing in data-driven computational science
GRID '06 Proceedings of the 7th IEEE/ACM International Conference on Grid Computing
| Hi-index | 0.00 |
Continuous query systems are an intuitive way for users to access streaming data in large-scale scientific applications containing many hundreds of streams. A challenge in these systems is to join streams in such a way that memory is conserved. Storing events that could not possibly participate in a join any longer wastes memory and limits scalability of the query processing system. This paper reports an experimentwe conducted to validate an algorithm we developed for adaptive rate, adjustable join windows. We posit that a rate-based strategy can result in memory savings, can be sufficiently responsive to rapid changes in stream rates, and can execute with suitably low overhead. Based on the results, we conclude that the algorithm adds between 0.007% and 2.6% overhead, with significant gains in memory utilization possible depending on the particular workload.