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’ 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.

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 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 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 Detecting Controversial Articles on Citizen Journalism

2018 ◽  
Vol 11 (1) ◽  
pp. 34
Author(s):  
Alfan Farizki Wicaksono ◽  
Sharon Raissa Herdiyana ◽  
Mirna Adriani
Keyword(s):  
Machine Learning ◽  
Structural Information ◽  
Structural Features ◽  
The Body ◽  
Supervised Machine Learning ◽  
Citizen Journalism ◽  
Learning Approach ◽  
Daily News ◽  
Machine Learning Approach ◽  
Controversial Topic

Someone's understanding and stance on a particular controversial topic can be influenced by daily news or articles he consume everyday. Unfortunately, readers usually do not realize that they are reading controversial articles. In this paper, we address the problem of automatically detecting controversial article from citizen journalism media. To solve the problem, we employ a supervised machine learning approach with several hand-crafted features that exploits linguistic information, meta-data of an article, structural information in the commentary section, and sentiment expressed inside the body of an article. The experimental results shows that our proposed method manages to perform the addressed task effectively. The best performance so far is achieved when we use all proposed feature with Logistic Regression as our model (82.89\% in terms of accuracy). Moreover, we found that information from commentary section (structural features) contributes most to the classification task.

A Method for Eliciting Goals for Business Process Models based on Non-Functional Requirements Catalogues

2013 ◽  
pp. 226-244 ◽  
Author(s):  
Evellin Cardoso ◽  
João Paulo A. Almeida ◽  
Renata S. S. Guizzardi ◽  
Giancarlo Guizzardi
Keyword(s):  
Business Process ◽  
Process Modeling ◽  
Business Processes ◽  
Business Process Modeling ◽  
Process Models ◽  
Functional Requirements ◽  
Business Process Models ◽  
Traditional Approaches ◽  
Goal Models

While traditional approaches in business process modeling tend to focus on “how” the business processes are performed (adopting a behavioral description in which business processes are described in terms of procedural aspects), in goal-oriented business process modeling, the proposals strive to extend traditional business process methodologies by providing a dimension of intentionality to business processes. One of the key difficulties in enabling one to model goal-oriented processes concerns the identification or elicitation of goals. This paper reports on a case study conducted in a Brazilian hospital, which obtained several goal models represented in i*/Tropos, each of which correspond to a business process also modeled in the scope of the study. NFR catalogues were helpful in goal elicitation, uncovering goals that did not come up during previous interviews prior to these catalogues’ use.

scholarly journals Vision based supervised restricted Boltzmann machine helps to actuate novel shape memory alloy accurately

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ritaban Dutta ◽  
Cherry Chen ◽  
David Renshaw ◽  
Daniel Liang
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Shape Memory ◽  
Video Data ◽  
The Body ◽  
Restricted Boltzmann Machine ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Boltzmann Machine ◽  
High Level

AbstractExtraordinary shape recovery capabilities of shape memory alloys (SMAs) have made them a crucial building block for the development of next-generation soft robotic systems and associated cognitive robotic controllers. In this study we desired to determine whether combining video data analysis techniques with machine learning techniques could develop a computer vision based predictive system to accurately predict force generated by the movement of a SMA body that is capable of a multi-point actuation performance. We identified that rapid video capture of the bending movements of a SMA body while undergoing external electrical excitements and adapting that characterisation using computer vision approach into a machine learning model, can accurately predict the amount of actuation force generated by the body. This is a fundamental area for achieving a superior control of the actuation of SMA bodies. We demonstrate that a supervised machine learning framework trained with Restricted Boltzmann Machine (RBM) inspired features extracted from 45,000 digital thermal infrared video frames captured during excitement of various SMA shapes, is capable to estimate and predict force and stress with 93% global accuracy with very low false negatives and high level of predictive generalisation.

Sign in / Sign up

Export Citation Format

Share Document