Theorizing Process Dynamics with Directed Graphs: A Diachronic Analysis of Digital Trace Data

MIS Quarterly ◽  
2021 ◽  
Vol 45 (2) ◽  
pp. 967-984
Author(s):  
Brian Pentland ◽  
Emmanuelle Vaast ◽  
Julie Ryan Wolf
Keyword(s):  
Business Processes ◽  
Process Mining ◽  
Directed Graphs ◽  
Organization Studies ◽  
Trace Data ◽  
Process Dynamics ◽  
Systems Research ◽  
Theoretical Perspectives ◽  
Diachronic Analysis ◽  
Process Structure

The growing availability of digital trace data has generated unprecedented opportunities for analyzing, explaining, and predicting the dynamics of process change. While research on process organization studies theorizes about process and change, and research on process mining rigorously measures and models business processes, there has so far been limited research that measures and theorizes about process dynamics. This gap represents an opportunity for new information systems research. This research note lays the foundation for such an endeavor by demonstrating the use of process mining for diachronic analysis of process dynamics. We detail the definitions, assumptions, and mechanics of an approach that is based on representing processes as weighted, directed graphs. Using this representation, we offer a precise definition of process dynamics that focuses attention on describing and measuring changes in process structure over time. We analyze process structure over two years at four dermatology clinics. Our analysis reveals process changes that were invisible to the medical staff in the clinics. This approach offers empirical insights that are relevant to many theoretical perspectives on process dynamics.

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 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 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.

Semantic process mining: A conceptual application of main tools, framework and model analysis

2020 ◽  
Vol 16 (3) ◽  
pp. 127-147
Author(s):  
Kingsley Okoye
Keyword(s):  
Business Processes ◽  
Process Mining ◽  
Mining Method ◽  
Conceptual Level ◽  
Semantic Reasoning ◽  
Abstract Knowledge ◽  
Semantic Concepts ◽  
Semantic Process ◽  
High Level ◽  
Information Semantics

Semantics has been a major challenge when applying the process mining (PM) technique to real-time business processes. The several theoretical and practical efforts to bridge the semantic gap has spanned the advanced notion of the semantic-based process mining (SPM). Fundamentally, the SPM devotes its methods to the idea of making use of existing (semantic) technologies to support the analysis of PM techniques. In principle, the semantic-based process mining method is applied through the acquisition and representation of abstract knowledge about the domain processes in question. To this effect, this paper demonstrates how the semantic concepts and process modelling (reasoning) methods are used to improve the outcomes of PM techniques from the syntactic to a more conceptual level. To do this, the study proposes an SPM-based framework that shows to be intelligent with a high level of semantic reasoning aptitudes. Technically, this paper introduces a process mining approach that uses information (semantics) about different activities that can be found in any given process to make inferences and generate rules or patterns through the method for annotation, semantic reasoning, and conceptual assertions. In turn, the method is theoretically applied to enrich the informative values of the resultant models. Also, the study conducts and systematically reviews the current tools and methods that are used to support the outcomes of the process mining as well as evaluates the results of the different methods to determine the levels of impact and its implications for process mining.

Continual Process Improvement Based on ITIL and Process Mining

2011 ◽  
pp. 145-167
Author(s):  
Kerstin Gerke ◽  
Konstantin Petruch ◽  
Gerrit Tamm
Keyword(s):  
Process Improvement ◽  
Service Management ◽  
Business Processes ◽  
Reference Model ◽  
Process Mining ◽  
It Services ◽  
New Approach ◽  
Customer Support ◽  
It Service ◽  
Key Indicators

The inherent quality of business processes and their support through information technology (IT) increasingly plays a significant role in the economic success of an organization. More and more business processes are supported through IT services. In order to provide IT services with the required quality and at minimum costs, the importance of effective and efficient IT service management (ITSM) processes is crucial. In this contribution, the authors present a new approach, which allows the continual process improvement by the interconnection of the ITIL reference model, the 7-step improvement process, and process mining. On the basis of the reference model, to-be processes are set and key indicators are determined. As-is processes and their key indicators derived by process mining are subsequently compared to the to-be processes. This new approach enables the design, control, and improvement of ITIL based customer support processes, which will be trialed in practice.

scholarly journals Towards Improving Business Processes based on preconfigured KPI target values, Process Mining and Redesign Patterns

2019 ◽  
Vol 164 ◽  
pp. 279-284
Author(s):  
Jihen Cherni ◽  
Ricardo Martinho ◽  
Sonia Ayachi Ghannouchi
Keyword(s):  
Business Processes ◽  
Process Mining ◽  
Target Values

scholarly journals Learning that Reflects the Living: Aligning Anticipation and Edusemiotics

2017 ◽  
Vol 8 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Cary Campbell
Keyword(s):  
Theoretical Perspective ◽  
Theoretical Biology ◽  
Final Analysis ◽  
Middle Way ◽  
Systems Research ◽  
Theoretical Perspectives ◽  
Inanimate Matter ◽  
Living Organisms ◽  
Anticipatory Systems ◽  
Emergence Of Life

Joining educational philosophy with theoretical biology has come to form an important part of the growing edusemiotic movement. Edusemiotics has followed the example of biosemiotics (the understanding that the emergence of life is coextensive with the emergence of semiosis) to describe the process of learning itself as being coextensive with semiosis (or, sign-action). Following this recent turn in scholarship this paper argues for a perspective of learning rooted in the dynamics of the living. By ‘living’, I am referring to the integrated dynamics of reaction and anticipation that is definitional of living organisms, as distinguished from (non-living) inanimate matter. This calls for a theoretical perspective that transcends the realist/idealist divides often inherent in educational theory; offering a possible middle way between the constructivist emphasis on mind dependent reality, and the positivist emphasis on mind independent reality. Such a theoretical approach must be able to account for interactions in states of becoming, and thus calls for a broader causality than reductionist methods or computationalist accounts allow. To approach this re-conceptualization, I attempt to explore the combined relevance of two theoretical perspectives --- anticipatory biology, and the edusemiotic understanding of learning-as-semiosis. To address how anticipatory systems research from biology can be applied to learning theory, I first explore Nadin and Rosen’s notion of (Gödelian) G-complexity, and how this contributes to an understanding of the living as complex. Secondly, I address Peirce's notion of semiosis as it is embedded in his categorical system and overarching cosmology. In conclusion, I consider the confluences and differences between the concept of semiosis and the triadic relations that Peirce saw as fundamental to the origins of life, and the anticipatory processes that these theoretical biologists use to define living organisms, and examine how and if these two conceptions (taken in union) can potentially enrich theoretical accounts of learning. In this final analysis, the combined relevance of these two perspectives is applied to understanding the process of improvisation as an anticipatory/semiotic dynamic, to demonstrate the possible pedagogical relevance of this theoretical alignment

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.

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