Discovering process models for the analysis of application failures under uncertainty of event logs
Computer applications, such as servers, databases and middleware, ubiquitously emit execution traces stored in log files. The use of logs for the analysis of application failures is known since the early days of computers. Field data studies have shown that application logs are fraught with uncertai...
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Veröffentlicht in: | Knowledge-based systems 2020-02, Vol.189, p.105054, Article 105054 |
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creator | Pecchia, Antonio Weber, Ingo Cinque, Marcello Ma, Yu |
description | Computer applications, such as servers, databases and middleware, ubiquitously emit execution traces stored in log files. The use of logs for the analysis of application failures is known since the early days of computers. Field data studies have shown that application logs are fraught with uncertainty, i.e., missing or noisy events in the logs. A body of research that has dealt successfully with uncertainty in event logs is process mining from the business process management community, specifically by discovering process models. The literature has shown the value of process mining across several domains, but as yet there is no study that quantifies possible improvements from using process models, and the impact of uncertainty in the context of application failures. This work addresses the use of process mining for detecting failures from application logs. First, process models are discovered from logs; then conformance checking is used to detect deviations from the models. We contribute to knowledge engineering research with a systematic measurement study that quantifies the failure detection capability of conformance checking in spite of missing events, and its accuracy with respect to process models obtained from noisy logs. Analysis is done with a dataset of 55,462 execution traces from three independent real-life applications. We obtain a mixed answer depending on the application under test; our measurements provide insights into the use of process mining for failure analysis. |
doi_str_mv | 10.1016/j.knosys.2019.105054 |
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The use of logs for the analysis of application failures is known since the early days of computers. Field data studies have shown that application logs are fraught with uncertainty, i.e., missing or noisy events in the logs. A body of research that has dealt successfully with uncertainty in event logs is process mining from the business process management community, specifically by discovering process models. The literature has shown the value of process mining across several domains, but as yet there is no study that quantifies possible improvements from using process models, and the impact of uncertainty in the context of application failures. This work addresses the use of process mining for detecting failures from application logs. First, process models are discovered from logs; then conformance checking is used to detect deviations from the models. We contribute to knowledge engineering research with a systematic measurement study that quantifies the failure detection capability of conformance checking in spite of missing events, and its accuracy with respect to process models obtained from noisy logs. Analysis is done with a dataset of 55,462 execution traces from three independent real-life applications. 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We contribute to knowledge engineering research with a systematic measurement study that quantifies the failure detection capability of conformance checking in spite of missing events, and its accuracy with respect to process models obtained from noisy logs. Analysis is done with a dataset of 55,462 execution traces from three independent real-life applications. We obtain a mixed answer depending on the application under test; our measurements provide insights into the use of process mining for failure analysis.</description><subject>Application logs</subject><subject>Business process management</subject><subject>Computer simulation</subject><subject>Conformance checking</subject><subject>Engineering research</subject><subject>Failure analysis</subject><subject>Failure detection</subject><subject>Knowledge engineering</subject><subject>Middleware</subject><subject>Model accuracy</subject><subject>Process discovery</subject><subject>Servers</subject><subject>Uncertainty</subject><issn>0950-7051</issn><issn>1872-7409</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kEtrwzAQhEVpoenjH_Qg6NnpSrZs6VIo6RMCvbRnocjrVKljpVon4H9fB_fcyy4sM8POx9iNgLkAUd5t5t9dpIHmEoQZTwpUccJmQlcyqwowp2wGRkFWgRLn7IJoAwBSCj1j_jGQjwdMoVvzXYoeifg21tgSb2Li_Rdy17l2oEA8Ntztdm3wrg-x440L7T4h8X1XYxqnx9S70PXDUYkH7HrexjVdsbPGtYTXf_uSfT4_fSxes-X7y9viYZn5AqDPlHMaVSONkpXSqi5Wrs61bGSlG6NVIcpKFdo7WQifV2UtRWmMBr-SDsEbk1-y2yl37PGzR-rtJu7T-DxZmau8FFCWclQVk8qnSJSwsbsUti4NVoA94rQbO-G0R5x2wjna7ifbSAYPAZMlH3CsXIeEvrd1DP8H_AKLFIDY</recordid><startdate>20200215</startdate><enddate>20200215</enddate><creator>Pecchia, Antonio</creator><creator>Weber, Ingo</creator><creator>Cinque, Marcello</creator><creator>Ma, Yu</creator><general>Elsevier B.V</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>E3H</scope><scope>F2A</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20200215</creationdate><title>Discovering process models for the analysis of application failures under uncertainty of event logs</title><author>Pecchia, Antonio ; Weber, Ingo ; Cinque, Marcello ; Ma, Yu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c400t-5aa8e5f29527585d4bad382f278f9854167548ca241c376d2169980cb2ae0c993</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Application logs</topic><topic>Business process management</topic><topic>Computer simulation</topic><topic>Conformance checking</topic><topic>Engineering research</topic><topic>Failure analysis</topic><topic>Failure detection</topic><topic>Knowledge engineering</topic><topic>Middleware</topic><topic>Model accuracy</topic><topic>Process discovery</topic><topic>Servers</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pecchia, Antonio</creatorcontrib><creatorcontrib>Weber, Ingo</creatorcontrib><creatorcontrib>Cinque, Marcello</creatorcontrib><creatorcontrib>Ma, Yu</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>Library & Information Sciences Abstracts (LISA)</collection><collection>Library & Information Science Abstracts (LISA)</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Knowledge-based systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pecchia, Antonio</au><au>Weber, Ingo</au><au>Cinque, Marcello</au><au>Ma, Yu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Discovering process models for the analysis of application failures under uncertainty of event logs</atitle><jtitle>Knowledge-based systems</jtitle><date>2020-02-15</date><risdate>2020</risdate><volume>189</volume><spage>105054</spage><pages>105054-</pages><artnum>105054</artnum><issn>0950-7051</issn><eissn>1872-7409</eissn><abstract>Computer applications, such as servers, databases and middleware, ubiquitously emit execution traces stored in log files. 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subjects | Application logs Business process management Computer simulation Conformance checking Engineering research Failure analysis Failure detection Knowledge engineering Middleware Model accuracy Process discovery Servers Uncertainty |
title | Discovering process models for the analysis of application failures under uncertainty of event logs |
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