NiagaraCQ: a scalable continuous query system for Internet databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On computing correlated aggregates over continual data streams
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Processing complex aggregate queries over data streams
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
SEQ: A Model for Sequence Databases
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Surfing Wavelets on Streams: One-Pass Summaries for Approximate Aggregate Queries
Proceedings of the 27th International Conference on Very Large Data Bases
SRQL: Sorted Relational Query Language
SSDBM '98 Proceedings of the 10th International Conference on Scientific and Statistical Database Management
Gigascope: a stream database for network applications
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
TelegraphCQ: continuous dataflow processing
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Load Shedding for Aggregation Queries over Data Streams
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Tributaries and deltas: efficient and robust aggregation in sensor network streams
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Semantics and evaluation techniques for window aggregates in data streams
Proceedings of the 2005 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
Resource sharing in continuous sliding-window aggregates
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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One of the most important uses of aggregate queries over data streams is sampling. Typically, aggregation is performed over sliding windows where queries return new results whenever the window contents change, a concept referred to as a continuous query. Existing data models and query languages for streams are not capable of expressing many practical user-defined samplings over streams. To this end we propose a new data stream model, referred to as the sequence model, and a query language for specifying aggregate queries over data streams. We show that the sequence model can readily express a superset of the aggregate queries expressible in the previously proposed time-based data stream model, thus providing a declarative and formal semantics to understand and reason about continuous aggregate queries. Defined on top of the sequence model, our query language supports existing sliding window operators and a novel frequency operator. By using the frequency operator one is capable of expressing useful sampling queries, such as queries with user-defined group-based sampling and nested aggregation over either the input stream or the result stream. Such capabilities are beyond those of previously proposed query languages over streams. Finally, we conduct a preliminary experimental study that shows our language is effective and efficient in practice.