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 Database-Less Extraction of Event Logs from Redo Logs

2021 ◽  
pp. 73-82
Author(s):  
Dorina Bano ◽  
Tom Lichtenstein ◽  
Finn Klessascheck ◽  
Mathias Weske
Keyword(s):  
Performance Analysis ◽  
Real World ◽  
Business Processes ◽  
Process Mining ◽  
Detailed Knowledge ◽  
System Failure ◽  
Conformance Checking ◽  
Event Logs ◽  
Event Log ◽  
And Performance

Process mining is widely adopted in organizations to gain deep insights about running business processes. This can be achieved by applying different process mining techniques like discovery, conformance checking, and performance analysis. These techniques are applied on event logs, which need to be extracted from the organization’s databases beforehand. This not only implies access to databases, but also detailed knowledge about the database schema, which is often not available. In many real-world scenarios, however, process execution data is available as redo logs. Such logs are used to bring a database into a consistent state in case of a system failure. This paper proposes a semi-automatic approach to extract an event log from redo logs alone. It does not require access to the database or knowledge of the databaseschema. The feasibility of the proposed approach is evaluated on two synthetic redo logs.

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 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 Conformance Checking of Dwelling Time Using a Token-based Method

2021 ◽  
Vol 7 (2) ◽  
pp. 129
Author(s):  
Bambang Jokonowo ◽  
Nenden Siti Fatonah ◽  
Emelia Akashah Patah Akhir
Keyword(s):  
Business Process ◽  
Process Model ◽  
Process Mining ◽  
Standard Operating Procedure ◽  
Event Data ◽  
Container Port ◽  
Conformance Checking ◽  
Dwelling Time ◽  
Event Log ◽  
Fitness Calculation

Background: Standard operating procedure (SOP) is a series of business activities to achieve organisational goals, with each activity carried to be recorded and stored in the information system together with its location (e.g., SCM, ERP, LMS, CRM). The activity is known as event data and is stored in a database known as an event log.Objective: Based on the event log, we can calculate the fitness to determine whether the business process SOP is following the actual business process.Methods: This study obtains the event log from a terminal operating system (TOS), which records the dwelling time at the container port. The conformance checking using token-based replay method calculates fitness by comparing the event log with the process model.Results: The findings using the Alpha algorithm resulted in the most traversed traces (a, b, n, o, p). The fitness calculation returns 1.0 were produced, missing, and remaining tokens are replied to each of the other traces.Conclusion: Thus, if the process mining produces a fitness of more than 0.80, this shows that the process model is following the actual business process. Keywords: Conformance Checking, Dwelling time, Event log, Fitness, Process Discovery, Process Mining

Business Process Intelligence

2011 ◽  
pp. 456-480 ◽  
Author(s):  
M. Castellanos ◽  
A.K. Alves de Medeiros ◽  
J. Mendling ◽  
B. Weber ◽  
A.J.M.M. Weijters
Keyword(s):  
Business Process ◽  
Process Modeling ◽  
Business Processes ◽  
Process Mining ◽  
Process Efficiency ◽  
Conformance Checking ◽  
Process Redesign ◽  
Process Discovery ◽  
Business Process Intelligence ◽  
Introductory Overview

Business Process Intelligence (BPI) is an emerging area that is getting increasingly popular for enterprises. The need to improve business process efficiency, to react quickly to changes and to meet compliance is among the main drivers for BPI. BPI refers to the application of Business Intelligence techniques to business processes and comprises a large range of application areas spanning from process monitoring and analysis to process discovery, conformance checking, prediction and optimization. This chapter provides an introductory overview of BPI and its application areas and delivers an understanding of how to apply BPI in one’s own setting. In particular, it shows how process mining techniques such as process discovery and conformance checking can be used to support process modeling and process redesign. In addition, it illustrates how processes can be improved and optimized over time using analytics for explanation, prediction, optimization and what-if-analysis. Throughout the chapter, a strong emphasis is given to describe tools that use these techniques to support BPI. Finally, major challenges for applying BPI in practice and future trends are discussed.

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.

scholarly journals Role of Stochastic Petri Net (SPN) in Process Discovery for Modelling and Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Shabnam Shahzadi ◽  
Xianwen Fang ◽  
David Anekeya Alilah
Keyword(s):  
Petri Net ◽  
Business Processes ◽  
Time Perspective ◽  
Process Mining ◽  
Vital Role ◽  
Process Discovery ◽  
Stochastic Petri Net ◽  
Event Log ◽  
The Stability ◽  
Generalized Stochastic Petri Net

For exploitation and extraction of an event’s data that has vital information which is related to the process from the event log, process mining is used. There are three main basic types of process mining as explained in relation to input and output. These are process discovery, conformance checking, and enhancement. Process discovery is one of the most challenging process mining activities based on the event log. Business processes or system performance plays a vital role in modelling, analysis, and prediction. Recently, a memoryless model such as exponential distribution of the stochastic Petri net SPN has gained much attention in research and industry. This paper uses time perspective for modelling and analysis and uses stochastic Petri net to check the performance, evolution, stability, and reliability of the model. To assess the effect of time delay in firing the transition, stochastic reward net SRN model is used. The model can also be used in checking the reliability of the model, whereas the generalized stochastic Petri net GSPN is used for evaluation and checking the performance of the model. SPN is used to analyze the probability of state transition and the stability from one state to another. However, in process mining, logs are used by linking log sequence with the state and, by this, modelling can be done, and its relation with stability of the model can be established.

Statistical Verification of Process Model Conformance to Execution Log Considering Model Abstraction

2018 ◽  
Vol 27 (02) ◽  
pp. 1850002
Author(s):  
Sung-Hyun Sim ◽  
Hyerim Bae ◽  
Yulim Choi ◽  
Ling Liu
Keyword(s):  
Process Model ◽  
Process Mining ◽  
Statistical Significance ◽  
Conformance Checking ◽  
Model Abstraction ◽  
Verification Method ◽  
Statistical Verification ◽  
New Concepts ◽  
Kolmogorov Smirnov ◽  
Process Execution

In Big data and IoT environments, process execution generates huge-sized data some of which is subsequently obtained by sensors. The main issue in such areas has been the necessity of analyzing data in order to suggest enhancements to processes. In this regard, evaluation of process model conformance to the execution log is of great importance. For this purpose, previous reports on process mining approaches have advocated conformance checking by fitness measure, which is a process that uses token replay and node-arc relations based on Petri net. However, fitness measure so far has not considered statistical significance, but just offers a numeric ratio. We herein propose a statistical verification method based on the Kolmogorov–Smirnov (K–S) test to judge whether two different log datasets follow the same process model. Our method can be easily extended to determinations that process execution actually follows a process model, by playing out the model and generating event log data from it. Additionally, in order to solve the problem of the trade-off between model abstraction and process conformance, we also propose the new concepts of Confidence Interval of Abstraction Value (CIAV) and Maximum Confidence Abstraction Value (MCAV). We showed that our method can be applied to any process mining algorithm (e.g. heuristic mining, fuzzy mining) that has parameters related to model abstraction. We expect that our method will come to be widely utilized in many applications dealing with business process enhancement involving process-model and execution-log analyses.

scholarly journals CONDA-PM—A Systematic Review and Framework for Concept Drift Analysis in Process Mining

Algorithms ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 161
Author(s):  
Ghada Elkhawaga ◽  
Mervat Abuelkheir ◽  
Sherif I. Barakat ◽  
Alaa M. Riad ◽  
Manfred Reichert
Keyword(s):  
Systematic Review ◽  
Business Process ◽  
Continuous Monitoring ◽  
Business Processes ◽  
Process Mining ◽  
Concept Drift ◽  
Business Environments ◽  
Drift Analysis ◽  
Comprehensive Framework ◽  
Over Time

Business processes evolve over time to adapt to changing business environments. This requires continuous monitoring of business processes to gain insights into whether they conform to the intended design or deviate from it. The situation when a business process changes while being analysed is denoted as Concept Drift. Its analysis is concerned with studying how a business process changes, in terms of detecting and localising changes and studying the effects of the latter. Concept drift analysis is crucial to enable early detection and management of changes, that is, whether to promote a change to become part of an improved process, or to reject the change and make decisions to mitigate its effects. Despite its importance, there exists no comprehensive framework for analysing concept drift types, affected process perspectives, and granularity levels of a business process. This article proposes the CONcept Drift Analysis in Process Mining (CONDA-PM) framework describing phases and requirements of a concept drift analysis approach. CONDA-PM was derived from a Systematic Literature Review (SLR) of current approaches analysing concept drift. We apply the CONDA-PM framework on current approaches to concept drift analysis and evaluate their maturity. Applying CONDA-PM framework highlights areas where research is needed to complement existing efforts.

scholarly journals Business Process Configuration according to Data Dependency Specification

2018 ◽  
Vol 8 (10) ◽  
pp. 2008 ◽  
Author(s):  
Luisa Parody ◽  
María Gómez-López ◽  
Angel Varela-Vaca ◽  
Rafael Gasca
Keyword(s):  
Business Process ◽  
Business Processes ◽  
Process Models ◽  
Work Flow ◽  
Data Dependency ◽  
Data Dependencies ◽  
Business Process Models ◽  
Programming Paradigm ◽  
Process Configuration ◽  
Correct Execution

Configuration techniques have been used in several fields, such as the design of business process models. Sometimes these models depend on the data dependencies, being easier to describe what has to be done instead of how. Configuration models enable to use a declarative representation of business processes, deciding the most appropriate work-flow in each case. Unfortunately, data dependencies among the activities and how they can affect the correct execution of the process, has been overlooked in the declarative specifications and configurable systems found in the literature. In order to find the best process configuration for optimizing the execution time of processes according to data dependencies, we propose the use of Constraint Programming paradigm with the aim of obtaining an adaptable imperative model in function of the data dependencies of the activities described declarative.

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