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| creator |
Nippl, Clara
| | Rantzau, Ralf
| | Mitschang, Bernhard
| | date |
2000-09-18
| | | | description |
Today many applications routinely generate large quantities of data.
The data often takes the form of (time) series, or more generally
streams, i.e. an ordered se-quence of records. Analysis of this data
requires stream processing techniques which differ in significant
ways from what current database analysis and query tech-niques have
been optimized for. In this paper we present a new operator, called
StreamJoin, that can efficiently be used to solve stream-related
problems of various appli-cations, such as universal quantification,
pattern recog-nition and data mining. Contrary to other approaches,
StreamJoin processing provides rapid response times, a non-blocking
execution as well as economical resource utilization. Adaptability
to different application scenarios is realized by means of
parameters. In addition, the StreamJoin operator can be efficiently
embedded into the database engine, thus implicitly using the
optimization and parallelization capabilities for the benefit of the
ap-plication. The paper focuses on the applicability of StreamJoin
to integrate application semantics into the DBMS.
| | format |
application/postscript
| | 1215071 Bytes | |