scholarly journals Object-Centric Replay-Based Conformance Checking: Unveiling Desire Lines and Local Deviations

2021 ◽  
Vol 28 (2) ◽  
pp. 146-168
Author(s):  
Julio C Carrasquel ◽  
Khalil Mecheraoui
Keyword(s):  
Petri Net ◽  
Process Mining ◽  
Specific Model ◽  
Conformance Checking ◽  
Trading Systems ◽  
Event Logs ◽  
Multiple Process ◽  
Real Behavior ◽  
Model Components

Conformance checking methods diagnose to which extent a real system, whose behavior is recorded in an event log, complies with its specification model, e.g., a Petri net. Nonetheless, the majority of these methods focus on checking isolated process instances, neglecting interaction between instances in a system. Addressing this limitation, a series of object-centric approaches have been proposed in the field of process mining. These approaches are based on the holistic analysis of the multiple process instances interacting in a system, where each instance is centered on the handling of an object. Inspired by the object-centric paradigm, this paper presents a replay-based conformance checking method which uses a class of colored Petri nets (CPNs) -- a Petri net extension where tokens in the model carry values of some types (colors). Particularly, we consider conservative workflow CPNs which allow to describe the expected behavior of a system whose components are centered on the end-to-end processing of distinguishable objects. For describing a system’s real behavior, we consider event logs whose events have sets of objects involved in the execution of activities. For replay, we consider a jump strategy where tokens absent from input places of a transition to fire move from their current place of the model to the requested places. Token jumps allow to identify desire lines, i.e., object paths unforeseen in the specification. Also, we introduce local diagnostics based on the proportion of jumps in specific model components. The metrics allow to inform the severity of deviations in precise system parts. Finally, we report experiments supported by a prototype of our method. To show the practical value of our method, we employ a case study on trading systems, where orders from users are matched to trade.

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 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 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.

scholarly journals Process Modelling Based on Event Logs

2018 ◽  
Vol 1 (1) ◽  
pp. 385-392
Author(s):  
Edyta Brzychczy
Keyword(s):  
Process Management ◽  
Process Mining ◽  
Process Analysis ◽  
Process Modelling ◽  
Process Models ◽  
Sources Of Information ◽  
Event Logs ◽  
It Systems ◽  
Many Sources

Abstract Process modelling is a very important stage in a Business Process Management cycle enabling process analysis and its redesign. Many sources of information for process modelling purposes exist. It may be an analysis of documentation related directly or indirectly to the process being analysed, observations or participation in the process. Nowadays, for this purpose, it is increasingly proposed to use the event logs from organization’s IT systems. Event logs could be analysed with process mining techniques to create process models expressed by various notations (i.e. Petri Nets, BPMN, EPC). Process mining enables also conformance checking and enhancement analysis of the processes. In the paper issues related to process modelling and process mining are briefly discussed. A case study, an example of delivery process modelling with process mining technique is presented.

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.

Auditor Choices during Event Log Building for Process Mining

2019 ◽  
Vol 16 (2) ◽  
pp. 59-67
Author(s):  
Mieke Jans
Keyword(s):  
Analytical Procedure ◽  
Process Mining ◽  
Current Event ◽  
Dual Purpose ◽  
Event Logs ◽  
Event Log ◽  
Audit Evidence ◽  
Mining Projects ◽  
Made In

ABSTRACT Applying process mining as an analytical procedure is a relatively young stream of thought in auditing. This paper examines the first step of such process-mining projects, which involves extracting and structuring the data in the required format for analysis. The article has a dual purpose: (1) to provide an overview of the choices to be made in this phase, and (2) to provide insights into the current event log preferences of auditors. These insights are valuable for a better understanding of how event logs are currently structured, along with the consequences of this structure for the analytical procedure. This matter is important because different preparation steps could lead to varying analytical procedures and consequently, to other audit evidence. This study also aims to reveal what data are perceived as most valuable to the auditor for further analysis. To address this goal, a case study has been conducted.

scholarly journals Simplified Process Model Discovery Based on Role-Oriented Genetic Mining

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Weidong Zhao ◽  
Xi Liu ◽  
Weihui Dai
Keyword(s):  
Process Model ◽  
Process Mining ◽  
Fitness Function ◽  
Control Flow ◽  
Process Models ◽  
Programming Approach ◽  
Event Logs ◽  
Process Complexity

Process mining is automated acquisition of process models from event logs. Although many process mining techniques have been developed, most of them are based on control flow. Meanwhile, the existing role-oriented process mining methods focus on correctness and integrity of roles while ignoring role complexity of the process model, which directly impacts understandability and quality of the model. To address these problems, we propose a genetic programming approach to mine the simplified process model. Using a new metric of process complexity in terms of roles as the fitness function, we can find simpler process models. The new role complexity metric of process models is designed from role cohesion and coupling, and applied to discover roles in process models. Moreover, the higher fitness derived from role complexity metric also provides a guideline for redesigning process models. Finally, we conduct case study and experiments to show that the proposed method is more effective for streamlining the process by comparing with related studies.

Process Mining of Event Logs: A Case Study Evaluating Internal Control Effectiveness

2019 ◽  
Vol 33 (3) ◽  
pp. 141-156 ◽  
Author(s):  
Tiffany Chiu ◽  
Mieke Jans
Keyword(s):  
Internal Control ◽  
Process Mining ◽  
Real Life ◽  
Potential Control ◽  
Control Effectiveness ◽  
Event Logs ◽  
Event Log ◽  
New Type ◽  
Potential Risks

SYNOPSISThis paper aims at adopting process mining to evaluate the effectiveness of internal control using a real-life event log. Specifically, the evaluation is based on the full population of an event log and it contains four analyses: (1) variant analysis that identifies standard and non-standard variants, (2) segregation of duties analysis that examines whether employees violate segregation of duties controls, (3) personnel analysis that investigates whether employees are involved in multiple potential control violations, and (4) timestamp analysis that detects time-related issues including weekend activities and lengthy process duration. Results from the case study indicate that process mining could assist auditors in identifying audit-relevant issues such as non-standard variants, weekend activities, and personnel who are involved in multiple violations. Process mining enables auditors to detect potential risks, ineffective internal controls, and inefficient processes. Therefore, process mining generates a new type of audit evidence and could revolutionize the current audit procedure.

scholarly journals Process Mining and Conformance Checking of Long Running Processes in the Context of Melanoma Surveillance

2018 ◽  
Vol 15 (12) ◽  
pp. 2809 ◽  
Author(s):  
Christoph Rinner ◽  
Emmanuel Helm ◽  
Reinhold Dunkl ◽  
Harald Kittler ◽  
Stefanie Rinderle-Ma
Keyword(s):  
Clinical Data ◽  
Process Mining ◽  
Process Models ◽  
Time Constraints ◽  
Actual Process ◽  
Conformance Checking ◽  
Event Logs ◽  
Melanoma Surveillance ◽  
Medical University ◽  
Medical Guidelines

Background: Process mining is a relatively new discipline that helps to discover and analyze actual process executions based on log data. In this paper we apply conformance checking techniques to the process of surveillance of melanoma patients. This process consists of recurring events with time constraints between the events. Objectives: The goal of this work is to show how existing clinical data collected during melanoma surveillance can be prepared and pre-processed to be reused for process mining. Methods: We describe an approach based on time boxing to create process models from medical guidelines and the corresponding event logs from clinical data of patient visits. Results: Event logs were extracted for 1023 patients starting melanoma surveillance at the Department of Dermatology at the Medical University of Vienna between January 2010 and June 2017. Conformance checking techniques available in the ProM framework and explorative applied process mining techniques were applied. Conclusions: The presented time boxing enables the direct use of existing process mining frameworks like ProM to perform process-oriented analysis also with respect to time constraints between events.

Process discovery and Petri nets

2009 ◽  
Vol 19 (6) ◽  
pp. 1091-1124 ◽  
Author(s):  
NADIA BUSI ◽  
G. MICHELE PINNA
Keyword(s):  
Petri Nets ◽  
Petri Net ◽  
Process Model ◽  
Process Mining ◽  
Model Design ◽  
Abstract Representation ◽  
Process Discovery ◽  
Event Logs ◽  
Mining Methods ◽  
Research Domain

The aim of the research domain known as process mining is to use process discovery to construct a process model as an abstract representation of event logs. The goal is to build a model (in terms of a Petri net) that can reproduce the logs under consideration, and does not allow different behaviours compared with those shown in the logs. In particular, process mining aims to verify the accuracy of the model design (represented as a Petri net), basically checking whether the same net can be rediscovered. However, the main mining methods proposed in the literature have some drawbacks: the classical α-algorithm is unable to rediscover various nets, while the region-based approach, which can mine them correctly, is too complex.In this paper, we compare different approaches and propose some ideas to counter the weaknesses of the region-based approach.

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