Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Diag-Join: An Opportunistic Join Algorithm for 1:N Relationships
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Exploiting Punctuation Semantics in Continuous Data Streams
IEEE Transactions on Knowledge and Data Engineering
Approximate join processing over data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Efficient processing of joins on set-valued attributes
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Interval query indexing for efficient stream processing
Proceedings of the thirteenth ACM international conference on Information and knowledge management
The VLDB Journal — The International Journal on Very Large Data Bases
Flexible time management in data stream systems
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
TAG: a Tiny AGgregation service for Ad-Hoc sensor networks
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Stream window join: tracking moving objects in sensor-network databases
SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
Monitoring streams: a new class of data management applications
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Load shedding in a data stream manager
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Processing sliding window multi-joins in continuous queries over data streams
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
GrubJoin: An Adaptive, Multi-Way, Windowed Stream Join with Time Correlation-Aware CPU Load Shedding
IEEE Transactions on Knowledge and Data Engineering
Executing stream joins on the cell processor
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Utility-driven load shedding for xml stream processing
Proceedings of the 17th international conference on World Wide Web
Discovering frequent sets from data streams with CPU constraint
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Parallelizing query optimization
Proceedings of the VLDB Endowment
Load Shedding for Shared Window Join over Real-Time Data Streams
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
Processing exact results for sliding window joins over data streams using disk storage
International Journal of Intelligent Information and Database Systems
E-Cube: multi-dimensional event sequence analysis using hierarchical pattern query sharing
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
BNCOD'11 Proceedings of the 28th British national conference on Advances in databases
An adaptive algorithm for finding frequent sets in landmark windows
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
High-performance complex event processing using continuous sliding views
Proceedings of the 16th International Conference on Extending Database Technology
Input data organization for batch processing in time window based computations
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Multi-query scheduling for time-critical data stream applications
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
| Hi-index | 0.00 |
We present an adaptive load shedding approach for windowed stream joins. In contrast to the conventional approach of dropping tuples from the input streams, we explore the concept of selective processing for load shedding. We allow stream tuples to be stored in the windows and shed excessive CPU load by performing the join operations, not on the entire set of tuples within the windows, but on a dynamically changing subset of tuples that are learned to be highly beneficial. We support such dynamic selective processing through three forms of runtime adaptations: adaptation to input stream rates, adaptation to time correlation between the streams and adaptation to join directions. Indexes are used to further speed up the execution of stream joins. Experiments are conducted to evaluate our adaptive load shedding in terms of output rate. The results show that our selective processing approach to load shedding is very effective and significantly outperforms the approach that drops tuples from the input streams.