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
Hauptverfasser: Pecchia, Antonio, Weber, Ingo, Cinque, Marcello, Ma, Yu
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container_start_page 105054
container_title Knowledge-based systems
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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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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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