scholarly journals Extraction of Missing Tendency Using Decision Tree Learning in Business Process Event Log

Data ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 82
Author(s):  
Hiroki Horita ◽  
Yuta Kurihashi ◽  
Nozomi Miyamori
Keyword(s):  
Decision Tree ◽  
Human Error ◽  
Missing Values ◽  
Business Processes ◽  
Process Mining ◽  
Incident Management ◽  
Decision Tree Learning ◽  
Event Log ◽  
Process Event ◽  
Using Data

In recent years, process mining has been attracting attention as an effective method for improving business operations by analyzing event logs that record what is done in business processes. The event log may contain missing data due to technical or human error, and if the data are missing, the analysis results will be inadequate. Traditional methods mainly use prediction completion when there are missing values, but accurate completion is not always possible. In this paper, we propose a method for understanding the tendency of missing values in the event log using decision tree learning without supplementing the missing values. We conducted experiments using data from the incident management system and confirmed the effectiveness of our method.

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.

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-EVENT APPROACH FOR OPERATIONAL RISK ESTIMATION

2019 ◽  
Vol 2 ◽  
pp. 70
Author(s):  
Vasily Karasev ◽  
Ekaterina Karaseva
Keyword(s):  
Financial Risk ◽  
Business Processes ◽  
Process Mining ◽  
Quantitative Estimation ◽  
Risk Estimation ◽  
Main Idea ◽  
Operational Risk ◽  
Economic Capital ◽  
Advantages And Disadvantages ◽  
Process Event

Authors propose a new process-event approach for quantitative estimation of operational risk in a bank and calculation the amount of economic capital in dynamics. The proposed approach, according to the Basel II Capital Accord, belongs to the category of “advanced methods”. Operational risk is not financial risk and appeared in unfavorable events mainly. A number of business processes are performed in banking activity. Every business process contains a set of routines (operations), which can be interrupted by operational risk events with certain losses. The main idea of the approach is to describe processes as chains of casual events instead of a traditional graphic description as diagrams. Authors introduce new concepts: an elementary process event, a chain of process events, a time diagram of enterprise’s event flow, and build logical and probabilistic risk models. Methods and formulas for calculation the current and integrated operational risk in dynamics, the amount of economic capital, upper and lower limits of reservation, are given. The value of integrated operational risk can be used as a rating of the current reliability of the bank. The paper outlines the advantages and disadvantages of proposed process-event approach. Research results can be implemented as analytical tool in risk management technology, “process mining” technology and in bank intelligent management systems. 

scholarly journals A Novel Impurity Measuring Technique for Decision Tree Learning in Machine Learning

2020 ◽  
Vol 9 (8) ◽  
pp. 506-512
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Big Data Analytics ◽  
Similarity Measures ◽  
Data Classification ◽  
Decision Tree Learning ◽  
Decision Tree Classification ◽  
C4.5 Decision Tree ◽  
Using Data ◽  
The Many

Decision tree classification is one of the most powerful data classification techniques in machine learning, data mining, big data analytics and split functionality is a crucial and inherently associated integral part of the decision tree learning. Many split similarity measures are proposed to determine the best split attribute and then partitioning the node data in decision tree learning accordingly. A new impurity measuring based split technique called (IMDT) for decision tree learning is proposed in this paper and it is used in obtaining experimental results. Many UCI machine learning dataset are employed in experimentation. The algorithm C4.5 is the most using data classification algorithm. The results obtained with the proposed approach are outperformed than the many existing decision tree classification algorithms in particular C4.5 decision tree algorithm.

Metamodel of the Artifact-Centric Approach to Event Log Extraction from ERP Systems

2016 ◽  
Vol 8 (2) ◽  
pp. 18-28 ◽  
Author(s):  
Ana Pajić ◽  
Dragana Bečejski-Vujaklija
Keyword(s):  
Enterprise Resource Planning ◽  
Business Processes ◽  
Process Mining ◽  
Resource Planning ◽  
Erp Systems ◽  
Huge Amount ◽  
Erp System ◽  
Event Logs ◽  
Event Log ◽  
Data Source

Enterprise Resource Planning (ERP) systems handle a huge amount of data related to the actual execution of business processes and the goal is to discover from transaction log a model of how the business processes are actually carried out. The authors' work captures the knowledge of existing approaches and tools in converting the data from transaction logs to event logs for process mining techniques. They conduct a detailed analysis of the artifact-centric approach concepts and describe its constructs by the ontological metamodel. The underlying logical and semantically rich structure of the approach is presented through the model definition. The paper specifies how concepts of the data source are mapped onto the concept of the event log. Dynamics NAV ERP system is used as an example to illustrate the data-oriented structure of ERP system.

A Cluster Analysis Method of Software Development Activities Based on Event Log

2019 ◽  
Vol 13 ◽  
Author(s):  
MingJing Tang ◽  
Tong Li ◽  
Rui Zhu ◽  
ZiFei Ma
Keyword(s):  
Software Development ◽  
Development Process ◽  
Clustering Algorithm ◽  
Process Mining ◽  
Development Project ◽  
Software Development Process ◽  
Log Data ◽  
Event Logs ◽  
Event Log ◽  
Process Event

Background: Event log data generated in the software development process contains historical information and future trends of software development activities. The mining and analysis of event log data contribute to identify and discover software development activities and provide effective support for software development process mining and modeling. Method: Firstly, deep learning model (Word2vec) has used for feature extraction and vectorization of software development process event logs. Then, K-means clustering algorithm and silhouette coefficient measure has used for clustering and clustering effect evaluation of vectorized software development process event logs. Results: This paper obtained the mapping relationship between software development activities and events, and realized the identification and discovery of software development activities. Conclusion: A practical software development project (jEdit) is given to prove the feasibility, rationality and effectiveness of our proposed method. This work provides effective support for software development process mining and software development behavior guidance.

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 Business Process Failure Prediction: a case study

2019 ◽  
Author(s):  
Pedro O. T. Mello ◽  
Kate Revoredo ◽  
Flávia Santoro
Keyword(s):  
Business Process ◽  
Business Processes ◽  
Historical Data ◽  
Process Mining ◽  
Risk Mitigation ◽  
Incident Management ◽  
Dynamic Nature ◽  
Process Failure ◽  
Business Process Monitoring

Business process monitoring aims at maintaining the reliability of process executions. However, the dynamic nature of business processes hinders a proactive scenario in which risk mitigation actions can occur before the facts that put the process at risk. Thus, some premises are necessary such as the identification of situations and patterns in historical data of the processes execution in order to characterize what determined the failures. In this paper, we address the problem of how to identify and detect patterns of behaviors that can lead the processes to a failure situation. As a solution, a combination of well-established techniques from Data and Process Mining fields are applied in a case study of an incident management process. The results obtained open possibilities to a proactive scenario.

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.

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