Process Discovery and Conformance Checking in Modular Construction Using RFID and Process Mining

2020 ◽  
Author(s):  
Khandakar M. Rashid ◽  
Joseph Louis
Keyword(s):  
Process Mining ◽  
Modular Construction ◽  
Conformance Checking ◽  
Process Discovery

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 Business process mining

2017 ◽  
Vol 01 (01) ◽  
pp. 1630004 ◽  
Author(s):  
Asef Pourmasoumi ◽  
Ebrahim Bagheri
Keyword(s):  
Process Mining ◽  
Status Quo ◽  
Process Models ◽  
Added Value ◽  
Conformance Checking ◽  
Event Logs ◽  
Organizational Information ◽  
Organization Process ◽  
The Status ◽  
Business Process Mining

One of the most valuable assets of an organization is its organizational data. The analysis and mining of this potential hidden treasure can lead to much added-value for the organization. Process mining is an emerging area that can be useful in helping organizations understand the status quo, check for compliance and plan for improving their processes. The aim of process mining is to extract knowledge from event logs of today’s organizational information systems. Process mining includes three main types: discovering process models from event logs, conformance checking and organizational mining. In this paper, we briefly introduce process mining and review some of its most important techniques. Also, we investigate some of the applications of process mining in industry and present some of the most important challenges that are faced in this area.

scholarly journals Multilevel Privacy Assurance Evaluation of Healthcare Metadata

2021 ◽  
Vol 11 (22) ◽  
pp. 10686
Author(s):  
Syeda Amna Sohail ◽  
Faiza Allah Bukhsh ◽  
Maurice van Keulen
Keyword(s):  
Privacy Preservation ◽  
Process Mining ◽  
Research Work ◽  
Healthcare Providers ◽  
Conceptual Modeling ◽  
Empirical Evaluation ◽  
Healthcare Services ◽  
Privacy Preserving ◽  
Third Party ◽  
Conformance Checking

Healthcare providers are legally bound to ensure the privacy preservation of healthcare metadata. Usually, privacy concerning research focuses on providing technical and inter-/intra-organizational solutions in a fragmented manner. In this wake, an overarching evaluation of the fundamental (technical, organizational, and third-party) privacy-preserving measures in healthcare metadata handling is missing. Thus, this research work provides a multilevel privacy assurance evaluation of privacy-preserving measures of the Dutch healthcare metadata landscape. The normative and empirical evaluation comprises the content analysis and process mining discovery and conformance checking techniques using real-world healthcare datasets. For clarity, we illustrate our evaluation findings using conceptual modeling frameworks, namely e3-value modeling and REA ontology. The conceptual modeling frameworks highlight the financial aspect of metadata share with a clear description of vital stakeholders, their mutual interactions, and respective exchange of information resources. The frameworks are further verified using experts’ opinions. Based on our empirical and normative evaluations, we provide the multilevel privacy assurance evaluation with a level of privacy increase and decrease. Furthermore, we verify that the privacy utility trade-off is crucial in shaping privacy increase/decrease because data utility in healthcare is vital for efficient, effective healthcare services and the financial facilitation of healthcare enterprises.

scholarly journals Prefix Imputation of Orphan Events in Event Stream Processing

2021 ◽  
Vol 4 ◽  
Author(s):  
Rashid Zaman ◽  
Marwan Hassani ◽  
Boudewijn F. Van Dongen
Keyword(s):  
Process Model ◽  
Process Mining ◽  
State Of The Art ◽  
Processing System ◽  
Conformance Checking ◽  
Event Stream ◽  
Window Width ◽  
Event Logs ◽  
Relevant Case ◽  
Log File

In the context of process mining, event logs consist of process instances called cases. Conformance checking is a process mining task that inspects whether a log file is conformant with an existing process model. This inspection is additionally quantifying the conformance in an explainable manner. Online conformance checking processes streaming event logs by having precise insights into the running cases and timely mitigating non-conformance, if any. State-of-the-art online conformance checking approaches bound the memory by either delimiting storage of the events per case or limiting the number of cases to a specific window width. The former technique still requires unbounded memory as the number of cases to store is unlimited, while the latter technique forgets running, not yet concluded, cases to conform to the limited window width. Consequently, the processing system may later encounter events that represent some intermediate activity as per the process model and for which the relevant case has been forgotten, to be referred to as orphan events. The naïve approach to cope with an orphan event is to either neglect its relevant case for conformance checking or treat it as an altogether new case. However, this might result in misleading process insights, for instance, overestimated non-conformance. In order to bound memory yet effectively incorporate the orphan events into processing, we propose an imputation of missing-prefix approach for such orphan events. Our approach utilizes the existing process model for imputing the missing prefix. Furthermore, we leverage the case storage management to increase the accuracy of the prefix prediction. We propose a systematic forgetting mechanism that distinguishes and forgets the cases that can be reliably regenerated as prefix upon receipt of their future orphan event. We evaluate the efficacy of our proposed approach through multiple experiments with synthetic and three real event logs while simulating a streaming setting. Our approach achieves considerably higher realistic conformance statistics than the state of the art while requiring the same storage.

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.

Discovering Process Horizontal Boundaries to Facilitate Process Comprehension

2018 ◽  
Vol 9 (2) ◽  
pp. 1-31 ◽  
Author(s):  
Pavlos Delias ◽  
Kleanthi Lakiotaki
Keyword(s):  
Process Model ◽  
Process Mining ◽  
A Priori ◽  
Quality Criteria ◽  
Large Set ◽  
Process Discovery ◽  
Event Log ◽  
Human Interpretation ◽  
Automated Discovery ◽  
Priori Information

Automated discovery of a process model is a major task of Process Mining that means to produce a process model from an event log, without any a-priori information. However, when an event log contains a large number of distinct activities, process discovery can be real challenging. The goal of this article is to facilitate process discovery in such cases when a process is expected to contain a large set of unique activities. To this end, this article proposes a clustering approach that recommends horizontal boundaries for the process. The proposed approach ultimately partitions the event log in a way that human interpretation efforts are decomposed. In addition, it makes automated discovery more efficient as well as effective by simultaneously considering two quality criteria: informativeness and robustness of the derived groups of activities. The authors conducted several experiments to test the behavior of the algorithm under different settings, and to compare it against other techniques. Finally, they provide a set of recommendations that may help process analysts during the process discovery endeavor.

scholarly journals Process mining: data analytics voor de accountant die wil weten hoe het nu echt zit

2015 ◽  
Vol 89 (10) ◽  
pp. 359-368
Author(s):  
Wim van der Aalst ◽  
Angelique Koopman
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Process Mining ◽  
Event Data ◽  
Process Discovery ◽  
Compliance Checking

Steeds meer gebeurtenissen (“events”) worden geregistreerd en opgeslagen in IT-systemen. Op dit moment staat “Big Data” volop in de schijnwer- pers en denken we vaak aan bedrijven als Google en Facebook. Event data zijn ech- ter in elke organisatie te vinden en op elk niveau. Process mining is de verbindende schakel tussen data en proces. Dankzij process mining is het mogelijk tegelijkertijd prestatie-georiënteerde en compliance-georiënteerde vragen te stellen. Door pro- cesmodellen te koppelen aan event data kunnen knelpunten opgespoord worden en is precies te zien waar en waarom mensen afwijken van het normatieve proces. Dit artikel beschrijft twee basisvormen van process mining: ‘process discovery’ en ‘con- formance/compliance checking’.

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