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ConsAD: a real-time consistency anomalies detector

Published: 20 May 2012 Publication History

Abstract

In this demonstration, we present ConsAD, a tool that detects consistency anomalies for arbitrary multi-tier applications that use lower levels of isolation than serializability. As the application is running, ConsAD detects and quantifies anomalies indicating exactly the transactions and data items involved. Furthermore, it classifies the detected anomalies into patterns showing the business methods involved as well as their occurrence frequency. ConsAD can guide designers to either choose an isolation level for which their application shows few anomalies or change their transaction design to avoid the anomalies. Its graphical interface shows detailed information about detected anomalies as they occur and analyzes their patterns as well as their distribution.

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K. Zellag and B. Kemme. Real-time quantification and classification of consistency anomalies in multi-tier architectures. In ICDE, pages 613--624, 2011.

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cover image ACM Conferences
SIGMOD '12: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
May 2012
886 pages
ISBN:9781450312479
DOI:10.1145/2213836
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 May 2012

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Author Tags

  1. consistency
  2. multi-tier architectures
  3. serializability

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  • Demonstration

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SIGMOD/PODS '12
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SIGMOD '12 Paper Acceptance Rate 48 of 289 submissions, 17%;
Overall Acceptance Rate 785 of 4,003 submissions, 20%

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