scholarly journals Discovery and Analysis of E-Government Business Processes with Process Mining: a case study

2022 ◽  
Author(s):  
Andrea Delgado ◽  
Daniel Calegari
Keyword(s):  
Business Processes ◽  
Process Mining

scholarly journals Discovering Business Processes from Email Logs using fastText and Process Mining

2020 ◽  
Author(s):  
Yaghoub rashnavadi ◽  
Sina Behzadifard ◽  
Reza Farzadnia ◽  
sina zamani
Keyword(s):  
Machine Learning ◽  
Business Processes ◽  
Oil And Gas ◽  
Process Mining ◽  
The Body ◽  
Process Models ◽  
Supervised Machine Learning ◽  
Implicit Information ◽  
Oil And Gas Sector

<p>Communication has never been more accessible than today. With the help of Instant messengers and Email Services, millions of people can transfer information with ease, and this trend has affected organizations as well. There are billions of organizational emails sent or received daily, and their main goal is to facilitate the daily operation of organizations. Behind this vast corpus of human-generated content, there is much implicit information that can be mined and used to improve or optimize the organizations’ operations. Business processes are one of those implicit knowledge areas that can be discovered from Email logs of an Organization, as most of the communications are followed inside Emails. The purpose of this research is to propose an approach to discover the process models in the Email log. In this approach, we combine two tools, supervised machine learning and process mining. With the help of supervised machine learning, fastText classifier, we classify the body text of emails to the activity-related. Then the generated log will be mined with process mining techniques to find process models. We illustrate the approach with a case study company from the oil and gas sector.</p>

scholarly journals Applied Sequence Clustering Techniques for Process Mining

2011 ◽  
pp. 481-502 ◽  
Author(s):  
Diogo R. Ferreira
Keyword(s):  
Business Processes ◽  
Process Mining ◽  
Behavioral Patterns ◽  
Daily Work ◽  
Sequence Clustering ◽  
Event Logs ◽  
Common Trend ◽  
First Case ◽  
Insight Into

This chapter introduces the principles of sequence clustering and presents two case studies where the technique is used to discover behavioral patterns in event logs. In the first case study, the goal is to understand the way members of a software team perform their daily work, and the application of sequence clustering reveals a set of behavioral patterns that are related to some of the main processes being carried out by that team. In the second case study, the goal is to analyze the event history recorded in a technical support database in order to determine whether the recorded behavior complies with a predefined issue handling process. In this case, the application of sequence clustering confirms that all behavioral patterns share a common trend that resembles the original process. Throughout the chapter, special attention is given to the need for data preprocessing in order to obtain results that provide insight into the typical behavior of business processes.

scholarly journals Discovering Business Processes from Email Logs Using fastText and Process Mining

2021 ◽  
Author(s):  
Yaghoub Rashnavadi ◽  
Sina Behzadifard ◽  
Reza Farzadnia ◽  
Sina Zamani
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Business Processes ◽  
Process Mining ◽  
Real Life ◽  
Process Models ◽  
Supervised Machine Learning ◽  
Implicit Information

Communication is indispensable for today's lifestyle, and thanks to technology, millions of people can communicate as quickly as possible. The effect of this breakthrough has transformed organizations to the degree that they generate billions of emails daily to facilitate their operations. There is implicit information behind this vast corpus of human-generated content that can be mined and used for their benefit. This paper tries to address the opportunity that email logs can bring to organizations and propose an approach to discover process models by combining supervised text classification and process mining. This framework consists of two main steps, text classification, and process mining. First, Emails will be classified with supervised machine learning, and to mine, the processes fuzzy Miner is used. To further investigate the application of this framework, we also applied this framework over a real-life dataset from a case study organization.

scholarly journals Discovering Business Processes from Email Logs using fastText and Process Mining

2020 ◽  
Author(s):  
Yaghoub rashnavadi ◽  
Sina Behzadifard ◽  
Reza Farzadnia ◽  
sina zamani
Keyword(s):  
Machine Learning ◽  
Business Processes ◽  
Oil And Gas ◽  
Process Mining ◽  
The Body ◽  
Process Models ◽  
Supervised Machine Learning ◽  
Implicit Information ◽  
Oil And Gas Sector

<p>Communication has never been more accessible than today. With the help of Instant messengers and Email Services, millions of people can transfer information with ease, and this trend has affected organizations as well. There are billions of organizational emails sent or received daily, and their main goal is to facilitate the daily operation of organizations. Behind this vast corpus of human-generated content, there is much implicit information that can be mined and used to improve or optimize the organizations’ operations. Business processes are one of those implicit knowledge areas that can be discovered from Email logs of an Organization, as most of the communications are followed inside Emails. The purpose of this research is to propose an approach to discover the process models in the Email log. In this approach, we combine two tools, supervised machine learning and process mining. With the help of supervised machine learning, fastText classifier, we classify the body text of emails to the activity-related. Then the generated log will be mined with process mining techniques to find process models. We illustrate the approach with a case study company from the oil and gas sector.</p>

Process mining approach to formal business process modelling and verification: a case study

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sohei Ito ◽  
Dominik Vymětal ◽  
Roman Šperka
Keyword(s):  
Formal Verification ◽  
Business Process ◽  
Business Processes ◽  
Formal Model ◽  
Process Mining ◽  
Content Type ◽  
Process Verification ◽  
Domain Independent ◽  
Formal Business

Purpose The need for assuring correctness of business processes in enterprises is widely recognised in terms of business process re-engineering and improvement. Formal methods are a promising approach to this issue. The challenge in business process verification is to create a formal model that is well-aligned to the reality. Process mining is a well-known technique to discover a model of a process based on facts. However, no studies exist that apply it to formal verification. This study aims to propose a methodology for formal business process verification by means of process mining, and attempts to clarify the challenges and necessary technologies in this approach using a case study. Design/methodology/approach A trading company simulation model is used as a case study. A workflow model is discovered from an event log produced by a simulation tool and manually complemented to a formal model. Correctness requirements of both domain-dependent and domain-independent types of the model are checked by means of model-checking. Findings For business process verification with both domain-dependent and domain-independent correctness requirements, more advanced process mining techniques that discover data-related aspects of processes are desirable. The choice of a formal modelling language is also crucial. It depends on the correctness requirements and the characteristics of the business process. Originality/value Formal verification of business processes starting with creating its formal model is quite new. Furthermore, domain-dependent and domain-independent correctness properties are considered in the same framework, which is also new. This study revealed necessary technologies for this approach with process mining.

Getting Insights to Improve Business Processes with Agility: A Case Study Using Process Mining

2019 ◽  
Author(s):  
Cleiton dos Santos Garcia ◽  
Alex Meincheim ◽  
Fernando C. Garcia Filho ◽  
Eduardo Alves Portela Santos ◽  
Edson Emilio Scalabrin
Keyword(s):  
Business Processes ◽  
Process Mining

Business Autopoiesis Through Process Referencing

2021 ◽  
pp. 233-253
Author(s):  
Małgorzata B. Pańkowska
Keyword(s):  
Business Process ◽  
Communication Systems ◽  
Process Management ◽  
Business Processes ◽  
Process Development ◽  
Process Mining ◽  
The Social ◽  
Business Process Mining ◽  
Development And Management

In this chapter, the concept of autopoietic system is assumed to stem from the theory of social communication systems, which reproduce all their specific structures and self-referential processes. This chapter aims at the analysis of business process development and management. The main goal is to present an original framework of business process management. Through this framework, business processes can be interpreted as autonomic artifacts which are created, discovered, explored, and disseminated within social communities of practice. This constant reproduction of processes and their dissemination allows the social organization to exist, cope with internal complexity, and achieve its operational goals. The chapter consists of three main parts. The first part covers the systematic literature review on business process mining and referencing. The second part includes the discussion on presented business process framework. The last part comprises a case study to present and discuss the application of the framework for the development of academic virtual education processes.

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 Discovering Business Processes from Email Logs Using fastText and Process Mining

2020 ◽  
Author(s):  
Yaghoub Rashnavadi ◽  
Sina Behzadifard ◽  
Reza Farzadnia ◽  
Sina Zamani
Keyword(s):  
Machine Learning ◽  
Business Processes ◽  
Oil And Gas ◽  
Process Mining ◽  
The Body ◽  
Process Models ◽  
Supervised Machine Learning ◽  
Implicit Information ◽  
Oil And Gas Sector

Communication has never been more accessible than today. With the help of Instant messengers and Email Services, millions of people can transfer information with ease, and this trend has affected organizations as well. There are billions of organizational emails sent or received daily, and their main goal is to facilitate the daily operation of organizations. Behind this vast corpus of human-generated content, there is much implicit information that can be mined and used to improve or optimize the organizations&rsquo; operations. Business processes are one of those implicit knowledge areas that can be discovered from Email logs of an Organization, as most of the communications are followed inside Emails. The purpose of this research is to propose an approach to discover the process models in the Email log. In this approach, we combine two tools, supervised machine learning and process mining. With the help of supervised machine learning, fastText classifier, we classify the body text of emails to the activity-related. Then the generated log will be mined with process mining techniques to find process models. We illustrate the approach with a case study company from the oil and gas sector.

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