How to improve business process performance using process mining

Due to the increased use of information systems by organizations to support process execution, detailed information on the implementation of business processes is being recorded. This fact enables using process mining projects as a powerful tool for improving performance. Process Mining is a relative young research discipline that sits between data science on the one hand and process modelling and analysis on the other hand. Process mining allows gaining knowledge of the organization’s actual business processes by extracting data from existing information systems mediums such as event logs, transaction logs etc. The purpose of this presentation is to demonstrate how a process for conducting process mining projects was designed, developed and applied in some organizational units. The process was implemented through nine research steps, inspired by the V-model, where elements on the right-hand side aim to answer questions presented in steps on the left-hand side. In the first two steps, the research problem and the research objectives were defined. A literature review was performed in step 3. In the fourth step, requirements for the process were identified and implemented. In step 5, a running example was carried out to test the process. Verification and validation of the process were performed in step 6 and step 7. Step 8 covered the discussion of results. The last step concludes the research, including checking if the research problem was solved. Organizations seeking for performance improvement can now benefit of a process that explicitly states which process mining tools, techniques and algorithms to be used in process mining projects

Towards an ontology for process monitoring and mining

Business Process Analysis (BPA) aims at monitoring, diagnosing, simulating and mining enacted processes in order to support the analysis and enhancement of process models. An effective BPA solution must provide the means for analysing existing e-businesses at three levels of abstraction: the Business Level, the Process Level and the IT Level. BPA requires semantic information that spans these layers of abstraction and which should be easily retrieved from audit trails. To cater for this, we describe the Process Mining Ontology and the Events Ontology which aim to support the analysis of enacted processes at different levels of abstraction spanning from fine grain technical details to coarse grain aspects at the Business Level

Artifact Lifecycle Discovery

Artifact-centric modeling is a promising approach for modeling business processes based on the so-called business artifacts - key entities driving the company's operations and whose lifecycles define the overall business process. While artifact-centric modeling shows significant advantages, the overwhelming majority of existing process mining methods cannot be applied (directly) as they are tailored to discover monolithic process models. This paper addresses the problem by proposing a chain of methods that can be applied to discover artifact lifecycle models in Guard-Stage-Milestone notation. We decompose the problem in such a way that a wide range of existing (non-artifact-centric) process discovery and analysis methods can be reused in a flexible manner. The methods presented in this paper are implemented as software plug-ins for ProM, a generic open-source framework and architecture for implementing process mining tools

Issues in Process Variants Mining

In today's dynamic business world economic success of an enterprise increasingly depends on its ability to react to internal and external changes in a quick and flexible way. In response to this need, process-aware information systems (PAIS) emerged, which support the modeling, orchestration and monitoring of business processes and services respectively. Recently, a new generation of flexible PAIS was introduced, which additionally allows for dynamic process and service changes. This, in turn, will lead to a large number of process variants, which are created from the same original process model, but might slightly differ from each other. This paper deals with issues related to the mining of such process variant collections. Our overall goal is to learn from process changes and to merge the resulting model variants into a generic process model in the best possible way. By adopting this generic process model in the PAIS, future cost of process change and need for process adaptations will decrease. Finally, we compare our approach with existing process mining techniques, and show that process variants mining is additionally needed to learn from process changes

From zero to hero: A process mining tutorial

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