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
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
Tribeca: A Stream Database Manager for Network Traffic Analysis
VLDB '96 Proceedings of the 22th 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
Issues in data stream management
ACM SIGMOD Record
Gigascope: a stream database for network applications
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Aurora: a new model and architecture for data stream management
The VLDB Journal — The International Journal on Very Large Data Bases
Characterizing memory requirements for queries over continuous data streams
ACM Transactions on Database Systems (TODS)
The BEA streaming XQuery processor
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
Frequency Operators for Condensative Queries over Data Streams
ICEBE '05 Proceedings of the IEEE International Conference on e-Business Engineering
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
Foundations of preferences in database systems
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
Resource sharing in continuous sliding-window aggregates
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
FluXQuery: an optimizing XQuery processor for streaming XML data
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Selectively storing XML data in relations
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
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In contrast to traditional database queries, a query on stream data is continuous in that it is periodically evaluated over fractions (sliding windows) of the data stream. This introduces challenges beyond those encountered when processing traditional queries. Over a traditional DBMS (Database Management System), the answer to an aggregate query is usually much smaller than the answer to a similar non-aggregate query making query processing condensative. Current proposals for declarative query languages over data streams do not support such condensative processing. Nor is it yet well understood what query constructs and what semantics should be adopted for continuous query languages. In order to make existing stream query languages more expressive, a novel stream query language CSQL (Condensative Stream Query Language) are proposed over a sequence-based stream model (Ma & Nutt 2005). It is shown that the sequence model supports a precise tuple-based semantics that is lacking in previous time-based models, and thereby provides a formal semantics to understand and reason about continuous queries. CSQL supports sliding window operators found in previous languages and processes a declarative semantics that allows one to specify and reason about the different meanings of the frequency by which a query returns answer tuples, which are beyond previous query languages over streams. In addition, a novel condensative stream algebra is defined by extending an existing stream algebra with a new frequency operator, to capture the condensative property. It is shown that a condensative stream algebra enables the generation of efficient continuous query plans, and can be used to validate query optimisation. Finally, it is shown via an experimental study that the proposed operators are effective and efficient in practice.