Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Exploiting Punctuation Semantics in Continuous Data Streams
IEEE Transactions on Knowledge and Data Engineering
Aurora: a new model and architecture for data stream management
The VLDB Journal — The International Journal on Very Large Data Bases
Semantics and evaluation techniques for window aggregates in data streams
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Update-pattern-aware modeling and processing of continuous queries
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
A heartbeat mechanism and its application in gigascope
VLDB '05 Proceedings of the 31st international conference on Very large data bases
The 8 requirements of real-time stream processing
ACM SIGMOD Record
The CQL continuous query language: semantic foundations and query execution
The VLDB Journal — The International Journal on Very Large Data Bases
Incremental Evaluation of Sliding-Window Queries over Data Streams
IEEE Transactions on Knowledge and Data Engineering
Resource sharing in continuous sliding-window aggregates
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Linear road: a stream data management benchmark
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Using Punctuation Schemes to Characterize Strategies for Querying over Data Streams
IEEE Transactions on Knowledge and Data Engineering
Towards a streaming SQL standard
Proceedings of the VLDB Endowment
Window specification over data streams
EDBT'06 Proceedings of the 2006 international conference on Current Trends in Database Technology
An algebric window model for data stream management
Proceedings of the Ninth ACM International Workshop on Data Engineering for Wireless and Mobile Access
| Hi-index | 0.00 |
Continuous queries applied over nonterminating data streams usually specify windows in order to obtain an evolving ---yet restricted--- set of tuples and thus provide timely results. Among other typical variants, sliding windows are mostly employed in stream processing engines and several advanced techniques have been suggested for their incremental evaluation. In this paper, we set out to study the existence of monotonic-related semantics in windowing constructs towards a more efficient maintenance of their changing contents. We investigate update patterns observed in common window variants as well as their impact on windowed adaptations of typical operators (like selection, join or aggregation), offering more insight towards design and implementation of stream processing mechanisms. Finally, to demonstrate its significance, this framework is validated for several windowed operations against streaming datasets with simulations at diverse arrival rates and window sizes.