scholarly journals Logic-Based Regulatory Conformance Checking

2008 ◽  
pp. 147-160 ◽  
Author(s):  
Nikhil Dinesh ◽  
Aravind K. Joshi ◽  
Insup Lee ◽  
Oleg Sokolsky
Keyword(s):  
Conformance Checking

scholarly journals Conformance checking of mixed-paradigm process models

2020 ◽  
pp. 101685
Author(s):  
Boudewijn F. van Dongen ◽  
Johannes De Smedt ◽  
Claudio Di Ciccio ◽  
Jan Mendling
Keyword(s):  
Process Models ◽  
Conformance Checking

Architectural conformance checking for KDM-represented systems

2021 ◽  
pp. 111116
Author(s):  
André de S. Landi ◽  
Daniel San Martín ◽  
Bruno M. Santos ◽  
Warteruzannan S. Cunha ◽  
Rafael S. Durelli ◽  
...  
Keyword(s):  
Conformance Checking

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.

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