research-article

Microsoft CEP server and online behavioral targeting

Proceedings of the VLDB Endowment, Volume 2, Issue 2

Pages 1558 - 1561

https://doi.org/10.14778/1687553.1687590

Published: 01 August 2009 Publication History

Abstract

In this demo, we present the Microsoft Complex Event Processing (CEP) Server, Microsoft CEP for short. Microsoft CEP is an event stream processing system featured by its declarative query language and its multiple consistency levels of stream query processing. Query composability, query fusing, and operator sharing are key features in the Microsoft CEP query processor. Moreover, the debugging and supportability tools of Microsoft CEP provide visibility of system internals to users.

Web click analysis has been crucial to behavior-based online marketing. Streams of web click events provide a typical workload for a CEP server. Meanwhile, a CEP server with its processing capabilities plays a key role in web click analysis. This demo highlights the features of Microsoft CEP under a workload of web click events.

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Cited By

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Zhang YZhang FLi HZhang SGuo XChen YPan ADu X(2024)Data-Aware Adaptive Compression for Stream ProcessingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.337771036:9(4531-4549)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3377710
Verwiebe JGrulich PTraub JMarkl V(2023)Survey of window types for aggregation in stream processing systemsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-022-00778-632:5(985-1011)Online publication date: 17-Feb-2023
https://dl.acm.org/doi/10.1007/s00778-022-00778-6
Verma RBrazauskas JSafronov VDanish MLewis IMortier R(2021)RACERProceedings of the 29th International Conference on Advances in Geographic Information Systems10.1145/3474717.3484270(634-637)Online publication date: 2-Nov-2021
https://dl.acm.org/doi/10.1145/3474717.3484270
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Index Terms

Microsoft CEP server and online behavioral targeting
1. Information systems
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Database query processing and optimization (theory)

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Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 2, Issue 2

August 2009

367 pages

ISSN:2150-8097

Editors:
Serge Abiteboul,
Tova Milo,
Jignesh Patel,
Philippe Rigaux

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VLDB Endowment

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Published: 01 August 2009

Published in PVLDB Volume 2, Issue 2

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Zhang YZhang FLi HZhang SGuo XChen YPan ADu X(2024)Data-Aware Adaptive Compression for Stream ProcessingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.337771036:9(4531-4549)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3377710
Verwiebe JGrulich PTraub JMarkl V(2023)Survey of window types for aggregation in stream processing systemsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-022-00778-632:5(985-1011)Online publication date: 17-Feb-2023
https://dl.acm.org/doi/10.1007/s00778-022-00778-6
Verma RBrazauskas JSafronov VDanish MLewis IMortier R(2021)RACERProceedings of the 29th International Conference on Advances in Geographic Information Systems10.1145/3474717.3484270(634-637)Online publication date: 2-Nov-2021
https://dl.acm.org/doi/10.1145/3474717.3484270
Al-Ghossein MAbdessalem TBARRÉ A(2021)A Survey on Stream-Based Recommender SystemsACM Computing Surveys10.1145/345344354:5(1-36)Online publication date: 25-May-2021
https://dl.acm.org/doi/10.1145/3453443
Körber MGlombiewski NMorgen ASeeger B(2021)TPStream: low-latency and high-throughput temporal pattern matching on event streamsDistributed and Parallel Databases10.1007/s10619-019-07272-z39:2(361-412)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1007/s10619-019-07272-z
Zeitler ERisch T(2020)Massive scale-out of expensive continuous queriesProceedings of the VLDB Endowment10.14778/3402707.34027524:11(1181-1188)Online publication date: 3-Jun-2020
https://dl.acm.org/doi/10.14778/3402707.3402752
Tangwongsan KHirzel MSchneider S(2019)Optimal and general out-of-order sliding-window aggregationProceedings of the VLDB Endowment10.14778/3339490.333949912:10(1167-1180)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.14778/3339490.3339499
Hirzel MBaudart GBonifati ADella Valle ESakr SAkrivi Vlachou A(2018)Stream Processing Languages in the Big Data EraACM SIGMOD Record10.1145/3299887.329989247:2(29-40)Online publication date: 11-Dec-2018
https://dl.acm.org/doi/10.1145/3299887.3299892
Palyvos-Giannas DGulisano VPapatriantafilou MPierre GFerreira PShrira L(2018)GeneaLogProceedings of the 19th International Middleware Conference10.1145/3274808.3274826(227-238)Online publication date: 26-Nov-2018
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Galić ZMešković EOsmanović D(2017)Distributed processing of big mobility data as spatio-temporal data streamsGeoinformatica10.1007/s10707-016-0264-z21:2(263-291)Online publication date: 1-Apr-2017
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