scholarly journals Visual Analytics in Process Mining for Supporting Business Process Improvement

2021 ◽  
Author(s):  
Antonia Kaouni ◽  
Georgia Theodoropoulou ◽  
Alexandros Bousdekis ◽  
Athanasios Voulodimos ◽  
Georgios Miaoulis
Keyword(s):  
Business Process ◽  
Visual Analytics ◽  
Domain Knowledge ◽  
Business Processes ◽  
Process Mining ◽  
Conceptual Modeling ◽  
Research Field ◽  
Process Models ◽  
Event Log ◽  
Business User

The increasing amounts of data have affected conceptual modeling as a research field. In this context, process mining involves a set of techniques aimed at extracting a process schema from an event log generated during process execution. While automatic algorithms for process mining and analysis are needed to filter out irrelevant data and to produce preliminary results, visual inspection, domain knowledge, human judgment and creativity are needed for proper interpretation of the results. Moreover, a process discovery on an event log usually results in complicated process models not easily comprehensible by the business user. To this end, visual analytics has the potential to enhance process mining towards the direction of explainability, interpretability and trustworthiness in order to better support human decisions. In this paper we propose an approach for identifying bottlenecks in business processes by analyzing event logs and visualizing the results. In this way, we exploit visual analytics in the process mining context in order to provide explainable and interpretable analytics results for business processes without exposing to the user complex process models that are not easily comprehensible. The proposed approach was applied to a manufacturing business process and the results show that visual analytics in the context of process mining is capable of identifying bottlenecks and other performance-related issues and exposing them to the business user in an intuitive and non-intrusive way.

scholarly journals Process Discovery of Business Processes Using Temporal Causal Relation

2019 ◽  
Vol 5 (2) ◽  
pp. 183
Author(s):  
Yutika Amelia Effendi ◽  
Nania Nuzulita
Keyword(s):  
Business Process ◽  
Business Processes ◽  
Process Mining ◽  
Causal Relation ◽  
Real Life ◽  
Process Models ◽  
Time Interval ◽  
Start Time ◽  
Process Discovery ◽  
Event Log

Background: Nowadays, enterprise computing manages business processes which has grown up rapidly. This situation triggers the production of a massive event log. One type of event log is double timestamp event log. The double timestamp has a start time and complete time of each activity executed in the business process. It also has a close relationship with temporal causal relation. The temporal causal relation is a pattern of event log that occurs from each activity performed in the process.Objective: In this paper, seven types of temporal causal relation between activities were presented as an extended version of relations used in the double timestamp event log. Since the event log was not always executed sequentially, therefore using temporal causal relation, the event log was divided into several small groups to determine the relations of activities and to mine the business process.Methods: In these experiments, the temporal causal relation based on time interval which were presented in Gantt chart also determined whether each case could be classified as sequential or parallel relations. Then to obtain the business process, each temporal causal relation was combined into one business process based on the timestamp of activity in the event log.Results: The experimental results, which were implemented in two real-life event logs, showed that using temporal causal relation and double timestamp event log could discover business process models.Conclusion: Considering the findings, this study concludes that business process models and their sequential and parallel AND, OR, XOR relations can be discovered by using temporal causal relation and double timestamp event log.Keywords:Business Process, Process Discovery, Process Mining, Temporal Causal Relation, Double Timestamp Event Log

scholarly journals Generating Block-Structured Parallel Process Models by Demonstration

2021 ◽  
Vol 11 (4) ◽  
pp. 1876
Author(s):  
Julijana Lekić ◽  
Dragan Milićev ◽  
Dragan Stanković
Keyword(s):  
Business Process ◽  
Business Processes ◽  
Process Mining ◽  
Original Method ◽  
Process Models ◽  
Programming By Demonstration ◽  
Event Logs ◽  
Process Specification ◽  
Common Purpose ◽  
Block Structured

Programming by demonstration (PBD) is a technique which allows end users to create, modify, accommodate, and expand programs by demonstrating what the program is supposed to do. Although the ideal of common-purpose programming by demonstration or by examples has been rejected as practically unrealistic, this approach has found its application and shown potentials when limited to specific narrow domains and ranges of applications. In this paper, the original method of applying the principles of programming by demonstration in the area of process mining (PM) to interactive construction of block-structured parallel business processes models is presented. A technique and tool that enable interactive process mining and incremental discovery of process models have been described in this paper. The idea is based on the following principle: using a demonstrational user interface, a user demonstrates scenarios of execution of parallel business process activities, and the system gives a generalized model process specification. A modified process mining technique with the α|| algorithm applied on weakly complete event logs is used for creating parallel business process models using demonstration.

An Integrated Approach for Discovering Process Models According to Business Process Types

2021 ◽  
Vol 16 ◽  
pp. 1-14
Author(s):  
Zineb Lamghari
Keyword(s):  
Business Process ◽  
Domain Knowledge ◽  
Process Model ◽  
Process Mining ◽  
Integrated Approach ◽  
Process Models ◽  
Management Support ◽  
Event Data ◽  
Process Discovery ◽  
Discovery Algorithms

Process discovery technique aims at automatically generating a process model that accurately describes a Business Process (BP) based on event data. Related discovery algorithms consider recorded events are only resulting from an operational BP type. While the management community defines three BP types, which are: Management, Support and Operational. They distinguish each BP type by different proprieties like the main business process objective as domain knowledge. This puts forward the lack of process discovery technique in obtaining process models according to business process types (Management and Support). In this paper, we demonstrate that business process types can guide the process discovery technique in generating process models. A special interest is given to the use of process mining to deal with this challenge.

Recovery and Refinement of Business Process Models for Web Applications

2018 ◽  
pp. 248-291
Author(s):  
Alessandro Marchetto ◽  
Chiara Di Francescomarino
Keyword(s):  
Business Process ◽  
Process Model ◽  
Business Processes ◽  
Web Applications ◽  
Process Mining ◽  
Relevant Information ◽  
Process Models ◽  
Model Complexity ◽  
Model Refinement ◽  
Business Process Models

Web Applications (WAs) have been often used to expose business processes to the users. WA modernization and evolution are complex and time-consuming activities that can be supported by software documentation (e.g., process models). When, as often happens, documentation is missing or is incomplete, documentation recovery and mining represent an important opportunity for reconstructing or completing it. Existing process-mining approaches, however, tend to recover models that are quite complex, rich, and intricate, thus difficult to understand and use for analysts and developers. Model refinement approaches have been presented in the literature to reduce the model complexity and intricateness while preserving the capability of representing the relevant information. In this chapter, the authors summarize approaches to mine first and refine later business process models from existing WAs. In particular, they present two process model refinement approaches: (1) re-modularization and (2) reduction. The authors introduce the techniques and show how to apply them to WAs.

scholarly journals METHOD OF CONSTRUCTING AN ATTRIBUTE DESCRIPTION OF THE BUSINESS PROCESS "AS IS" IN THE PROCESS APPROACH TO ENTERPRISE MANAGEMENT

2018 ◽  
Vol 6 ◽  
pp. 35-40
Author(s):  
Serhii Chalyi ◽  
Ievgen Bogatov
Keyword(s):  
Business Process ◽  
Process Management ◽  
Business Processes ◽  
Process Mining ◽  
Ordered Set ◽  
Process Models ◽  
Enterprise Management ◽  
Temporal Aspects ◽  
Subsequent Selection ◽  
Selection Of

The problem of constructing an attribute description of a business process with the automated formation of process models “as is” using logs of information systems in which the tracks of individual processes are not identified is considered. It is shown that to solve this problem, it is advisable to distinguish the distinctive properties of individual business processes represented by the attributes of log events. A method for constructing an attribute description of a business process is proposed. The method is based on the comparison of combinations of attributes for intervals of events of a fixed length and the subsequent selection of subsets of attributes with the same values. The method includes the steps of forming the intervals of events, constructing combinations of attributes for specified intervals, as well as calculating and subsequently averaging the weights of combinations of attributes on these intervals. The result of the method is a weight-ordered set of event attributes and their values, which takes into account the attribute and temporal aspects of the business process. The method creates conditions for a more efficient transition from functional to process management based on splitting the log into processes using the resulting attribute description and subsequent prototyping of business process models “as is” by means of process mining.

scholarly journals Artifact Lifecycle Discovery

2015 ◽  
Vol 24 (01) ◽  
pp. 1550001 ◽  
Author(s):  
Viara Popova ◽  
Dirk Fahland ◽  
Marlon Dumas
Keyword(s):  
Business Process ◽  
Process Model ◽  
Business Processes ◽  
Process Mining ◽  
Real Life ◽  
Process Models ◽  
Reverse Engineer ◽  
Wide Range ◽  
Open Source Framework ◽  
Automated Discovery

Artifact-centric modeling is an approach for capturing business processes in terms of so-called business artifacts — key entities driving a company's operations and whose lifecycles and interactions define an overall business process. This approach has been shown to be especially suitable in the context of processes where one-to-many or many-to-many relations exist between the entities involved in the process. As a contribution towards building up a body of methods to support artifact-centric modeling, this article presents a method for automated discovery of artifact-centric process models starting from logs consisting of flat collections of event records. We decompose the problem in such a way that a wide range of existing (non-artifact-centric) automated process discovery methods can be reused in a flexible manner. The presented methods are implemented as a package for ProM, a generic open-source framework for process mining. The methods have been applied to reverse-engineer an artifact-centric process model starting from logs of a real-life business process.

Recovery and Refinement of Business Process Models for Web Applications

2013 ◽  
pp. 134-175
Author(s):  
Alessandro Marchetto ◽  
Chiara Di Francescomarino
Keyword(s):  
Business Process ◽  
Process Model ◽  
Business Processes ◽  
Web Applications ◽  
Process Mining ◽  
Relevant Information ◽  
Process Models ◽  
Model Complexity ◽  
Model Refinement ◽  
Business Process Models

Web Applications (WAs) have been often used to expose business processes to the users. WA modernization and evolution are complex and time-consuming activities that can be supported by software documentation (e.g., process models). When, as often happens, documentation is missing or is incomplete, documentation recovery and mining represent an important opportunity for reconstructing or completing it. Existing process-mining approaches, however, tend to recover models that are quite complex, rich, and intricate, thus difficult to understand and use for analysts and developers. Model refinement approaches have been presented in the literature to reduce the model complexity and intricateness while preserving the capability of representing the relevant information. In this chapter, the authors summarize approaches to mine first and refine later business process models from existing WAs. In particular, they present two process model refinement approaches: (1) re-modularization and (2) reduction. The authors introduce the techniques and show how to apply them to WAs.

scholarly journals Event Log Preprocessing for Process Mining: A Review

2021 ◽  
Vol 11 (22) ◽  
pp. 10556
Author(s):  
Heidy M. Marin-Castro ◽  
Edgar Tello-Leal
Keyword(s):  
Business Process ◽  
Process Model ◽  
Process Mining ◽  
Data Cleaning ◽  
Data Preprocessing ◽  
Process Models ◽  
Event Data ◽  
Event Logs ◽  
Event Log ◽  
Preprocessing Technique

Process Mining allows organizations to obtain actual business process models from event logs (discovery), to compare the event log or the resulting process model in the discovery task with the existing reference model of the same process (conformance), and to detect issues in the executed process to improve (enhancement). An essential element in the three tasks of process mining (discovery, conformance, and enhancement) is data cleaning, used to reduce the complexity inherent to real-world event data, to be easily interpreted, manipulated, and processed in process mining tasks. Thus, new techniques and algorithms for event data preprocessing have been of interest in the research community in business process. In this paper, we conduct a systematic literature review and provide, for the first time, a survey of relevant approaches of event data preprocessing for business process mining tasks. The aim of this work is to construct a categorization of techniques or methods related to event data preprocessing and to identify relevant challenges around these techniques. We present a quantitative and qualitative analysis of the most popular techniques for event log preprocessing. We also study and present findings about how a preprocessing technique can improve a process mining task. We also discuss the emerging future challenges in the domain of data preprocessing, in the context of process mining. The results of this study reveal that the preprocessing techniques in process mining have demonstrated a high impact on the performance of the process mining tasks. The data cleaning requirements are dependent on the characteristics of the event logs (voluminous, a high variability in the set of traces size, changes in the duration of the activities. In this scenario, most of the surveyed works use more than a single preprocessing technique to improve the quality of the event log. Trace-clustering and trace/event level filtering resulted in being the most commonly used preprocessing techniques due to easy of implementation, and they adequately manage noise and incompleteness in the event logs.

scholarly journals Blockchain support for execution, monitoring and discovery of inter-organizational business processes

2021 ◽  
Vol 7 ◽  
pp. e731
Author(s):  
Miguel Morales-Sandoval ◽  
José A. Molina ◽  
Heidy M. Marin-Castro ◽  
Jose Luis Gonzalez-Compean
Keyword(s):  
Business Process ◽  
Business Processes ◽  
Process Mining ◽  
Software Tool ◽  
Conformance Checking ◽  
Execution Monitoring ◽  
Event Log ◽  
Correct Execution ◽  
Process Execution ◽  
Execution Management

In an Inter-Organizational Business Process (IOBP), independent organizations (collaborators) exchange messages to perform business transactions. With process mining, the collaborators could know what they are actually doing from process execution data and take actions for improving the underlying business process. However, process mining assumes that the knowledge of the entire process is available, something that is difficult to achieve in IOBPs since process execution data generally is not shared among the collaborating entities due to regulations and confidentiality policies (exposure of customers’ data or business secrets). Additionally, there is an inherently lack-of-trust problem in IOBP as the collaborators are mutually untrusted and executed IOBP can be subject to dispute on counterfeiting actions. Recently, Blockchain has been suggested for IOBP execution management to mitigate the lack-of-trust problem. Independently, some works have suggested the use of Blockchain to support process mining tasks. In this paper, we study and address the problem of IOBP mining whose management and execution is supported by Blockchain. As contribution, we present an approach that takes advantage of Blockchain capabilities to tackle, at the same time, the lack-of-trust problem (management and execution) and confident execution data collection for process mining (discovery and conformance) of IOBPs. We present a method that (i) ensures the business rules for the correct execution and monitoring of the IOBP by collaborators, (ii) creates the event log, with data cleaning integrated, at the time the IOBP executes, and (iii) produces useful event log in XES and CSV format for the discovery and conformance checking tasks in process mining. By a set of experiments on real IOBPs, we validate our method and evaluate its impact in the resulting discovered models (fitness and precision metrics). Results revealed the effectiveness of our method to cope with both the lack-of-trust problem in IOBPs at the time that contributes to collect the data for process mining. Our method was implemented as a software tool available to the community as open-source code.

scholarly journals PROSES MINING UNTUK OPTIMASI PROSES BISNIS

2020 ◽  
Vol 6 (2) ◽  
pp. 87-93
Author(s):  
Nur Fitrianti Fahrudin
Keyword(s):  
Business Process ◽  
Business Performance ◽  
Business Processes ◽  
Process Mining ◽  
Business Activity ◽  
Time Process ◽  
Considerable Time ◽  
Event Logs ◽  
Event Log ◽  
A Company

Organizations currently need to conduct an analysis of their business processes in order to improve business performance and productivity. In addition, this analysis can be a way to compete with competitors. However, the analysis of this business process if done manually requires considerable time. Process mining is a technique that helps solve this problem. Information systems that are owned by a company certainly store their every business activity. This data can be processed to find business processes that occur. This data is usually called an event log. Event logs help organizations to find gaps between business processes that occur with those expected. Based on this gap business processes can later be evaluated for later improvement.

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