scholarly journals Full Support for Efficiently Mining Multi-Perspective Declarative Constraints from Process Logs

Information ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 29 ◽  
Author(s):  
Christian Sturm ◽  
Myriel Fichtner ◽  
Stefan Schönig
Keyword(s):  
Process Management ◽  
Process Mining ◽  
Distributed Processing ◽  
Real Life ◽  
Research Problem ◽  
Process Models ◽  
Mining Technique ◽  
Event Logs ◽  
Satisfactory State ◽  
Mining Algorithms

Declarative process management has emerged as an alternative solution for describing flexible workflows. In turn, the modelling opportunities with languages such as Declare are less intuitive and hard to implement. The area of process discovery covers the automatic discovery of process models. It has been shown that the performance of process mining algorithms, particularly when considering the multi-perspective declarative process models, are not satisfactory. State-of-the-art mining tools do not support multi-perspective declarative models at this moment. We address this open research problem by proposing an efficient mining framework that leverages the latest big data analysis technology and builds upon the distributed processing method MapReduce. The paper at hand further completes the research on multi-perspective declarative process mining by extending our previous work in various ways; in particular, we introduce algorithms and descriptions for the full set of commonly accepted types of MP-Declare constraints. Additionally, we provide a novel implementation concept allowing an easy introduction and discovery of customised constraint templates. We evaluated the mining performance and effectiveness of the presented approach on several real-life event logs. The results highlight that, with our efficient mining technique, multi-perspective declarative process models can be extracted in reasonable time.

An Integrated Infrastructure Using Process Mining Techniques for Software Process Verification

2013 ◽  
pp. 364-382 ◽  
Author(s):  
Tuğba Gürgen ◽  
Ayça Tarhan ◽  
N. Alpay Karagöz
Keyword(s):  
Case Studies ◽  
Process Management ◽  
Process Mining ◽  
Software Process ◽  
Process Models ◽  
Task Management ◽  
Process Verification ◽  
Real Process ◽  
Management Processes ◽  
Mining Algorithms

The verification of process implementations according to specifications is a critical step of process management. This verification must be practiced according to objective criteria and evidence. This study explains an integrated infrastructure that utilizes process mining for software process verification and case studies carried out by using this infrastructure. Specific software providing the utilization of process mining algorithms for software process verification is developed as a plugin to an open-source EPF Composer tool that supports the management of software and system engineering processes. With three case studies, bug management, task management, and defect management processes are verified against defined and established process models (modeled by using EPF Composer) by using this plugin over real process data. Among these, the results of the case study performed in a large, leading IT solutions company in Turkey are remarkable in demonstrating the opportunities for process improvement.

scholarly journals Process Modelling Based on Event Logs

2018 ◽  
Vol 1 (1) ◽  
pp. 385-392
Author(s):  
Edyta Brzychczy
Keyword(s):  
Process Management ◽  
Process Mining ◽  
Process Analysis ◽  
Process Modelling ◽  
Process Models ◽  
Sources Of Information ◽  
Event Logs ◽  
It Systems ◽  
Many Sources

Abstract Process modelling is a very important stage in a Business Process Management cycle enabling process analysis and its redesign. Many sources of information for process modelling purposes exist. It may be an analysis of documentation related directly or indirectly to the process being analysed, observations or participation in the process. Nowadays, for this purpose, it is increasingly proposed to use the event logs from organization’s IT systems. Event logs could be analysed with process mining techniques to create process models expressed by various notations (i.e. Petri Nets, BPMN, EPC). Process mining enables also conformance checking and enhancement analysis of the processes. In the paper issues related to process modelling and process mining are briefly discussed. A case study, an example of delivery process modelling with process mining technique is presented.

An Integrated Infrastructure Using Process Mining Techniques for Software Process Verification

2017 ◽  
pp. 1503-1522 ◽  
Author(s):  
Tuğba Gürgen ◽  
Ayça Tarhan ◽  
N. Alpay Karagöz
Keyword(s):  
Case Studies ◽  
Process Management ◽  
Process Mining ◽  
Software Process ◽  
Process Models ◽  
Task Management ◽  
Process Verification ◽  
Real Process ◽  
Management Processes ◽  
Mining Algorithms

The verification of process implementations according to specifications is a critical step of process management. This verification must be practiced according to objective criteria and evidence. This study explains an integrated infrastructure that utilizes process mining for software process verification and case studies carried out by using this infrastructure. Specific software providing the utilization of process mining algorithms for software process verification is developed as a plugin to an open-source EPF Composer tool that supports the management of software and system engineering processes. With three case studies, bug management, task management, and defect management processes are verified against defined and established process models (modeled by using EPF Composer) by using this plugin over real process data. Among these, the results of the case study performed in a large, leading IT solutions company in Turkey are remarkable in demonstrating the opportunities for process improvement.

scholarly journals Detecting Complex Control-Flow Constructs for Choosing Process Discovery Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 1389-1393
Keyword(s):  
Business Process ◽  
Free Choice ◽  
Process Mining ◽  
Control Flow ◽  
Process Models ◽  
Event Data ◽  
Process Discovery ◽  
Event Logs ◽  
Complex Process ◽  
Mining Algorithms

Process models are the analytical illustration of an organization’s activity. They are very primordial to map out the current business process of an organization, build a baseline of process enhancement and construct future processes where the enhancements are incorporated. To achieve this, in the field of process mining, algorithms have been proposed to build process models using the information recorded in the event logs. However, for complex process configurations, these algorithms cannot correctly build complex process structures. These structures are invisible tasks, non-free choice constructs, and short loops. The ability of each discovery algorithm in discovering the process constructs is different. In this work, we propose a framework responsible of detecting from event logs the complex constructs existing in the data. By identifying the existing constructs, one can choose the process discovery techniques suitable for the event data in question. The proposed framework has been implemented in ProM as a plugin. The evaluation results demonstrate that the constructs can correctly be identified.

An Integrated Infrastructure Using Process Mining Techniques for Software Process Verification

2015 ◽  
pp. 818-835
Author(s):  
Tuğba Gürgen ◽  
Ayça Tarhan ◽  
N. Alpay Karagöz
Keyword(s):  
Case Studies ◽  
Process Management ◽  
Process Mining ◽  
Software Process ◽  
Process Models ◽  
Task Management ◽  
Process Verification ◽  
Real Process ◽  
Management Processes ◽  
Mining Algorithms

The verification of process implementations according to specifications is a critical step of process management. This verification must be practiced according to objective criteria and evidence. This study explains an integrated infrastructure that utilizes process mining for software process verification and case studies carried out by using this infrastructure. Specific software providing the utilization of process mining algorithms for software process verification is developed as a plugin to an open-source EPF Composer tool that supports the management of software and system engineering processes. With three case studies, bug management, task management, and defect management processes are verified against defined and established process models (modeled by using EPF Composer) by using this plugin over real process data. Among these, the results of the case study performed in a large, leading IT solutions company in Turkey are remarkable in demonstrating the opportunities for process improvement.

scholarly journals Discovering high-level BPMN process models from event data

2019 ◽  
Vol 25 (5) ◽  
pp. 995-1019 ◽  
Author(s):  
Anna Kalenkova ◽  
Andrea Burattin ◽  
Massimiliano de Leoni ◽  
Wil van der Aalst ◽  
Alessandro Sperduti
Keyword(s):  
Process Model ◽  
Process Mining ◽  
Real Life ◽  
Process Models ◽  
Holistic View ◽  
Life Event ◽  
Content Type ◽  
Event Logs ◽  
Wide Range ◽  
High Level

Purpose The purpose of this paper is to demonstrate that process mining techniques can help to discover process models from event logs, using conventional high-level process modeling languages, such as Business Process Model and Notation (BPMN), leveraging their representational bias. Design/methodology/approach The integrated discovery approach presented in this work is aimed to mine: control, data and resource perspectives within one process diagram, and, if possible, construct a hierarchy of subprocesses improving the model readability. The proposed approach is defined as a sequence of steps, performed to discover a model, containing various perspectives and presenting a holistic view of a process. This approach was implemented within an open-source process mining framework called ProM and proved its applicability for the analysis of real-life event logs. Findings This paper shows that the proposed integrated approach can be applied to real-life event logs of information systems from different domains. The multi-perspective process diagrams obtained within the approach are of good quality and better than models discovered using a technique that does not consider hierarchy. Moreover, due to the decomposition methods applied, the proposed approach can deal with large event logs, which cannot be handled by methods that do not use decomposition. Originality/value The paper consolidates various process mining techniques, which were never integrated before and presents a novel approach for the discovery of multi-perspective hierarchical BPMN models. This approach bridges the gap between well-known process mining techniques and a wide range of BPMN-complaint tools.

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.

scholarly journals Business process mining

2017 ◽  
Vol 01 (01) ◽  
pp. 1630004 ◽  
Author(s):  
Asef Pourmasoumi ◽  
Ebrahim Bagheri
Keyword(s):  
Process Mining ◽  
Status Quo ◽  
Process Models ◽  
Added Value ◽  
Conformance Checking ◽  
Event Logs ◽  
Organizational Information ◽  
Organization Process ◽  
The Status ◽  
Business Process Mining

One of the most valuable assets of an organization is its organizational data. The analysis and mining of this potential hidden treasure can lead to much added-value for the organization. Process mining is an emerging area that can be useful in helping organizations understand the status quo, check for compliance and plan for improving their processes. The aim of process mining is to extract knowledge from event logs of today’s organizational information systems. Process mining includes three main types: discovering process models from event logs, conformance checking and organizational mining. In this paper, we briefly introduce process mining and review some of its most important techniques. Also, we investigate some of the applications of process mining in industry and present some of the most important challenges that are faced in this area.

scholarly journals Towards Aspects Identification in Business Process Through Process Mining

2015 ◽  
Author(s):  
Bruna Brandão ◽  
Flávia Santoro ◽  
Leonardo Azevedo
Keyword(s):  
Programming Languages ◽  
Business Process ◽  
Process Model ◽  
Process Mining ◽  
Process Models ◽  
Preliminary Evaluation ◽  
Crosscutting Concerns ◽  
Business Process Models ◽  
Event Logs ◽  
Initial Results

In business process models, elements can be scattered (repeated) within different processes, making it difficult to handle changes, analyze process for improvements, or check crosscutting impacts. These scattered elements are named as Aspects. Similar to the aspect-oriented paradigm in programming languages, in BPM, aspect handling has the goal to modularize the crosscutting concerns spread across the models. This process modularization facilitates the management of the process (reuse, maintenance and understanding). The current approaches for aspect identification are made manually; thus, resulting in the problem of subjectivity and lack of systematization. This paper proposes a method to automatically identify aspects in business process from its event logs. The method is based on mining techniques and it aims to solve the problem of the subjectivity identification made by specialists. The initial results from a preliminary evaluation showed evidences that the method identified correctly the aspects present in the process model.

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