Symptom matching for event streams.
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| Title: | Symptom matching for event streams. |
|---|---|
| Authors: | Wang, M.1, Holub, V.1, Parsons, T.1, O'Sullivan, P.2, Murphy, J.1 |
| Source: | IET Software (Institution of Engineering & Technology). Aug2012, Vol. 6 Issue 4, p296-306. 11p. 4 Diagrams, 4 Charts. |
| Subjects: | Statistical matching, Application logging (Computer science), Information processing, Computer systems, Business enterprises, XPath (Computer program language), Databases, System analysis |
| Abstract: | Enterprise systems produce a vast amount of logging data. This critical and valuable information must be processed automatically for timely system analysis and recovery. As a result of industry demands, a standard database containing known issues has been introduced - a symptom database. Each symptom consists of a rule pattern and corresponding solutions. Patterns used for symptom identification are encoded as a XPath expression and matched against a stream of events in a standardised WSGI format common base event. The ability of an efficient matching for symptom patterns has been raised as an important requirement by industries. The authors present a real-time symptom identification in a stream of events. The implementation will allow multiple autonomic computing components such as self-monitoring sensors to effectively match known patterns in large datasets in run time. Unlike current state of the art approaches, the proposed solution allows users to define patterns using all the complex XPath functions in addition to standard numeric and Boolean operators. In particular, it was aimed at efficient simultaneous matching of a large set of XPath-based symptom patterns against a high-volume event stream, which is crucial for symptom identification but was not addressed efficiently by currently available XPath-matching engines. [ABSTRACT FROM AUTHOR] |
| Copyright of IET Software (Institution of Engineering & Technology) is the property of Institution of Engineering & Technology and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 80228923 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Symptom matching for event streams. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+M%2E%22">Wang, M.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Holub%2C+V%2E%22">Holub, V.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Parsons%2C+T%2E%22">Parsons, T.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22O'Sullivan%2C+P%2E%22">O'Sullivan, P.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Murphy%2C+J%2E%22">Murphy, J.</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IET+Software+%28Institution+of+Engineering+%26+Technology%29%22">IET Software (Institution of Engineering & Technology)</searchLink>. Aug2012, Vol. 6 Issue 4, p296-306. 11p. 4 Diagrams, 4 Charts. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Statistical+matching%22">Statistical matching</searchLink><br /><searchLink fieldCode="DE" term="%22Application+logging+%28Computer+science%29%22">Application logging (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Information+processing%22">Information processing</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+systems%22">Computer systems</searchLink><br /><searchLink fieldCode="DE" term="%22Business+enterprises%22">Business enterprises</searchLink><br /><searchLink fieldCode="DE" term="%22XPath+%28Computer+program+language%29%22">XPath (Computer program language)</searchLink><br /><searchLink fieldCode="DE" term="%22Databases%22">Databases</searchLink><br /><searchLink fieldCode="DE" term="%22System+analysis%22">System analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Enterprise systems produce a vast amount of logging data. This critical and valuable information must be processed automatically for timely system analysis and recovery. As a result of industry demands, a standard database containing known issues has been introduced - a symptom database. Each symptom consists of a rule pattern and corresponding solutions. Patterns used for symptom identification are encoded as a XPath expression and matched against a stream of events in a standardised WSGI format common base event. The ability of an efficient matching for symptom patterns has been raised as an important requirement by industries. The authors present a real-time symptom identification in a stream of events. The implementation will allow multiple autonomic computing components such as self-monitoring sensors to effectively match known patterns in large datasets in run time. Unlike current state of the art approaches, the proposed solution allows users to define patterns using all the complex XPath functions in addition to standard numeric and Boolean operators. In particular, it was aimed at efficient simultaneous matching of a large set of XPath-based symptom patterns against a high-volume event stream, which is crucial for symptom identification but was not addressed efficiently by currently available XPath-matching engines. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IET Software (Institution of Engineering & Technology) is the property of Institution of Engineering & Technology and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1049/iet-sen.2011.0091 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 296 Subjects: – SubjectFull: Statistical matching Type: general – SubjectFull: Application logging (Computer science) Type: general – SubjectFull: Information processing Type: general – SubjectFull: Computer systems Type: general – SubjectFull: Business enterprises Type: general – SubjectFull: XPath (Computer program language) Type: general – SubjectFull: Databases Type: general – SubjectFull: System analysis Type: general Titles: – TitleFull: Symptom matching for event streams. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, M. – PersonEntity: Name: NameFull: Holub, V. – PersonEntity: Name: NameFull: Parsons, T. – PersonEntity: Name: NameFull: O'Sullivan, P. – PersonEntity: Name: NameFull: Murphy, J. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2012 Type: published Y: 2012 Identifiers: – Type: issn-print Value: 17518806 Numbering: – Type: volume Value: 6 – Type: issue Value: 4 Titles: – TitleFull: IET Software (Institution of Engineering & Technology) Type: main |
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