Mining Lifecycle Event Logs for Enhancing Service-Based Applications

Data Mining ◽  
2013 ◽  
pp. 658-668
Author(s):  
Schahram Dustdar ◽  
Philipp Leitner ◽  
Franco Maria Nardini ◽  
Fabrizio Silvestri ◽  
Gabriele Tolomei
Keyword(s):  
Pattern Mining ◽  
Process Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Process Models ◽  
Service Oriented Architectures ◽  
Event Logs ◽  
Runtime Environment ◽  
Service Oriented ◽  
General Record

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.

Mining Lifecycle Event Logs for Enhancing Service-based Applications

2012 ◽  
pp. 196-206
Author(s):  
Schahram Dustdar ◽  
Philipp Leitner ◽  
Franco Maria Nardini ◽  
Fabrizio Silvestri ◽  
Gabriele Tolomei
Keyword(s):  
Pattern Mining ◽  
Process Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Process Models ◽  
Service Oriented Architectures ◽  
Event Logs ◽  
Runtime Environment ◽  
Service Oriented ◽  
General Record

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.

Path Mining and Process Mining for Workflow Management Systems

2011 ◽  
pp. 1489-1496 ◽  
Author(s):  
Jorge Cardoso ◽  
W.M.P. van der Aalst
Keyword(s):  
Information Systems ◽  
Business Processes ◽  
Process Mining ◽  
Workflow Management ◽  
Process Models ◽  
Management Systems ◽  
Workflow Technology ◽  
X Ray ◽  
Event Logs ◽  
Service Oriented

Business process management systems (Smith and Fingar 2003) provide a fundamental infrastructure to define and manage business processes and workflows. These systems are often called process aware information systems (Dumas, Aalst et al. 2005) since they coordinate the automation of interconnected tasks. Well-known systems include Tibco, WebSphere MQ Workflow, FileNet, COSA, etc. Other types of systems, such as ERP, CRM, SCM, and B2B, are also driven by explicit process models and are configured on the basis of a workflow model specifying the order in which tasks need to be executed. When process models or workflows are executed, the underlying management system generates data describing the activities being carried out which is stored in a log file. This log of data can be used to discover and extract knowledge about the execution and structure of processes. The goal of process mining is to extract information about processes from logs. When observing recent developments with respect to process aware information systems (Dumas, Aalst et al. 2005) three trends can be identified. First of all, workflow technology is being embedded in service oriented architectures. Second, there is a trend towards providing more flexibility. It is obvious that in the end business processes interface with people. Traditional workflow solutions expect the people to adapt to the system. However, it is clear that in many situations this is not acceptable. Therefore, systems are becoming more flexible and adaptable. The third trend is the omnipresence of event logs in today’s systems. Current systems ranging from cross-organizational systems to embedded systems provide detailed event logs. In a service oriented architecture events can be monitored in various ways. Moreover, physical devices start to record events. Already today many professional systems (X-ray machines, wafer stepper, high-end copiers, etc.) are connected to the internet. For example, Philips Medical Systems is able to monitor all events taking place in their X-ray machines. The three trends mentioned above are important enablers for path mining and process mining. The abundance of recorded events in structured format is an important enabler for the analysis of run-time behavior. Moreover, the desire to be flexible and adaptable also triggers the need for monitoring. If processes are not enforced by some system, it is relevant to find out what is actually happening, e.g., how frequently do people deviate from the default procedure.

scholarly journals Cross-Organization Emergency Response Process Mining: An Approach Based on Petri Nets

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Cong Liu ◽  
Huiling Li ◽  
Qingtian Zeng ◽  
Ting Lu ◽  
Caihong Li
Keyword(s):  
Petri Nets ◽  
Emergency Response ◽  
Resource Sharing ◽  
Process Mining ◽  
Process Models ◽  
Response Process ◽  
Extensive Experience ◽  
Event Logs ◽  
Collaboration Patterns ◽  
Sharing Patterns

To support effective emergency disposal, organizations need to collaborate with each other to complete the emergency mission that cannot be handled by a single organization. In general, emergency disposal that involves multiple organizations is typically organized as a group of interactive processes, known as cross-organization emergency response processes (CERPs). The construction of CERPs is a time-consuming and error-prone task that requires practitioners to have extensive experience and business background. Process mining aims to construct process models by analyzing event logs. However, existing process mining techniques cannot be applied directly to discover CERPs since we have to consider the complexity of various collaborations among different organizations, e.g., message exchange and resource sharing patterns. To tackle this challenge, a CERP model mining method is proposed in this paper. More specifically, we first extend classical Petri nets with resource and message attributes, known as resource and message aware Petri nets (RMPNs). Then, intra-organization emergency response process (IERP) models that are represented as RMPNs are discovered from emergency drilling event logs. Next, collaboration patterns among emergency organizations are formally defined and discovered. Finally, CERP models are obtained by merging IERP models and collaboration patterns. Through comparative experimental evaluation using the fire emergency drilling event log, we illustrate that the proposed approach facilitates the discovery of high-quality CERP models than existing state-of-the-art approaches.

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