Mining Lifecycle Event Logs for Enhancing Service-based Applications

Mining Lifecycle Event Logs for Enhancing Service-based Applications

Schahram Dustdar (Vienna University of Technology, Austria), Philipp Leitner (Vienna University of Technology, Austria), Franco Maria Nardini (ISTI-CNR, Pisa, Italy), Fabrizio Silvestri (ISTI-CNR, Pisa, Italy) and Gabriele Tolomei (ISTI-CNR, Pisa, Italy)
DOI: 10.4018/978-1-4666-2089-6.ch007
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Service-Oriented Architectures (SOAs), and traditional enterprise systems in general, record a variety of events (e.g., messages being sent and received between service components) to proper log files, i.e., event logs. These files constitute a huge and valuable source of knowledge that may be extracted through data mining techniques. To this end, process mining is increasingly gaining interest across the SOA community. The goal of process mining is to build models without a priori knowledge, i.e., to discover structured process models derived from specific patterns that are present in actual traces of service executions recorded in event logs. However, in this work, the authors focus on detecting frequent sequential patterns, thus considering process mining as a specific instance of the more general sequential pattern mining problem. Furthermore, they apply two sequential pattern mining algorithms to a real event log provided by the Vienna Runtime Environment for Service-oriented Computing, i.e., VRESCo. The obtained results show that the authors are able to find services that are frequently invoked together within the same sequence. Such knowledge could be useful at design-time, when service-based application developers could be provided with service recommendation tools that are able to predict and thus to suggest next services that should be included in the current service composition.
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In this work, we present a use case for event log mining in service-based systems. This idea bears some resemblance to the established idea of business activity management (BAM) (Kochar, 2005). BAM considers the event-driven governance of business processes, and is, hence, mostly a term from the business domain. Technically, BAM is enabled by monitoring runtime of services and their interactions within company SOAs. To this end, event-based monitoring approaches (Zeng, Lei, & Chang, 2007, Baresi, Guinea, Pistore, & Trainotti, 2009, Wetzstein, Strauch, & Leymann, 2009) produce a steady stream of low-level lifecycle events, similarly to the lifecycle events discussed in Section 1.2 and to the event logs produced by VRESCo. These low-level events need to be aggregated so that real business information can be gained from them. Existing techniques to do this include SLA aggregation (Unger, Leymann, & Scheibler, 2008) or event-based SLA monitoring (Sahai, Machiraju, Sayal, van Moorsel, & Casati, 2002, Michlmayr, Rosenberg, Leitner, & Dustdar, 2009). Related to the ideas of BAM is research work by (Mulo, Zdun, & Dustdar, 2008), which considers event-based monitoring of business compliance. Our research, specifically mining for invocation sequences that lead to failed service invocations, is complementary to BAM. While BAM is mostly concerned with discovering failures, our research can be used to identify or predict them in advance.

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