Applying Process Mining in SOA Environments (original) (raw)
Related papers
This paper presents a novel reverse engineering technique for obtaining real-life event logs from distributed systems. This allows us to analyze the operational processes of software systems under real-life conditions, and use process mining techniques to obtain precise and formal models. Hence, the work can be positioned in-between reverse engineering and process mining. We present a formal definition, implementation and an instrumentation strategy based the joinpoint-pointcut model. Two case studies are used to evaluate our approach. These concrete examples demonstrate the feasibility and usefulness of our approach.
Mining Lifecycle Event Logs for Enhancing Service-Based Applications
Concepts, Methodologies, Tools, and Applications
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 we focus on detecting frequent sequential patterns, thus considering process mining as a specific instance of the more general sequential pattern mining problem. Furthermore, we apply two sequential pattern mining algorithms to a real event log provided by the Vienna Runtime Environment for Serviceoriented Computing, i.e., VRESCO. The obtained results show that we 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.
Beyond Process Mining: From the Past to Present and Future
Traditionally, process mining has been used to extract models from event logs and to check or extend existing models. This has shown to be useful for improving processes and their IT support. Process mining techniques analyze historic information hidden in event logs to provide surprising insights for managers, system developers, auditors, and end users. However, thus far, process mining is mainly used in an offline fashion and not for operational decision support. While existing process mining techniques focus on the process as a whole, this paper focuses on individual process instances (cases) that have not yet completed. For these running cases, process mining can used to check conformance, predict the future, and recommend appropriate actions. This paper presents a framework for operational support using process mining and details a coherent set of approaches that focuses on time information. Time-based operational support can be used to detect deadline violations, predict the remaining processing time, and recommend activities that minimize flow times. All of this has been implemented in ProM and initial experiences using this toolset are reported in this paper.
LECTURE NOTES IN BUSINESS INFORMATION PROCESSING, 2011
Process mining techniques are able to extract knowledge from event logs commonly available in today's information systems. These techniques provide new means to discover, monitor, and improve processes in a variety of application domains. There are two main drivers for the growing interest in process mining. On the one hand, more and more events are being recorded, thus, providing detailed information about the history of processes. On the other hand, there is a need to improve and support business processes in competitive ...
From Zero to Hero: A Process Mining Tutorial
Lecture Notes in Computer Science, 2017
Process mining is an emerging area that synergically combines model-based and data-oriented analysis techniques to obtain useful insights on how business processes are executed within an organization. This tutorial aims at providing an introduction to the key analysis techniques in process mining that allow decision makers to discover process models from data, compare expected and actual behaviors, and enrich models with key information about the actual process executions. In addition, the tutorial will present concrete tools and will provide practical skills for applying process mining in a variety of application domains, including the one of software development.
Process Mining: A 360 Degree Overview
Process mining enables organizations to uncover their actual processes, provide insights, diagnose problems, and automatically trigger corrective actions. Process mining is an emerging scientific discipline positioned at the intersection between process science and data science. The combination of process modeling and analysis with the event data present in today's information systems provides new means to tackle compliance and performance problems. This chapter provides an overview of the field of process mining introducing the different types of process mining (e.g., process discovery and conformance checking) and the basic ingredients, i.e., process models and event data. To prepare for later chapters, event logs are introduced in detail (including pointers to standards for event data such as XES and OCEL). Moreover, a brief overview of process mining applications and software is given.
Web services are an emerging technology to implement and integrate business processes within and across enterprises. Service-orientation can be used to decompose complex systems into loosely coupled software components that may run remotely. However, the distributed nature of services complicates the design and analysis of service-oriented systems that support end-to-end business processes. Fortunately, services leave trails in so-called event logs and recent breakthroughs in process mining research make it possible to discover, analyze, and improve business processes based on such logs. Recently, the Task Force on Process Mining released the Process Mining Manifesto. This manifesto is supported by 53 organizations and 77 process mining experts contributed to it. The active participation from end-users, tool vendors, consultants, analysts, and researchers illustrate the growing significance of process mining as a bridge between data mining and business process modeling. In this paper, we focus on the opportunities and challenges for service mining, i.e., applying process mining techniques to services. We discuss the guiding principles and challenges listed in the Process Mining Manifesto and also highlight challenges specific for service-orientated systems.
A systematic mapping study of process mining
Enterprise Information Systems, 2017
Web service mining and verification of properties: An approach based on event calculus 2006 C IEEE Int. Conf. on Computer Supported Cooperative Work in Design (CSCWD) Yan, L.; Yuqiang, F. Design of an automatic workflow modeling method in cooperative WFMS 2006 C Int. WS on Data Engineering Issues in E-Commerce and Services (DEECS) Gaaloul, W.; Baina, K.; Godart, C. A bottom-up workflow mining approach for workflow applications analysis 2006 C Int. WS on Database and Expert Systems Applications (DEXA) Curia, R.; Gallucci, L.; Ruffolo, M. Knowledge management in health care: An architectural framework for clinical process management systems 2006 C Eur. WS on Inductive Databases and Constraint Based Mining (EWIDCBM)