scholarly journals Event logs gerados da simulação de diferentes cenários e analisados com mineração de processos.

2021 ◽  
Vol 13 (2) ◽  
pp. 73-82
Author(s):  
Felipe Nedopetalski ◽  
Joslaine Cristina Jeske De Freitas
Keyword(s):  
Open Source ◽  
Conformance Checking ◽  
Process Discovery ◽  
Event Logs ◽  
Cpn Tools ◽  
Workflow Net

A cada dia uma quantidade enorme de dados é gerada de sistemas gerenciados por informações. Geralmente esta informação é armazenada em banco de dados ou event logs. Mineração de processos pode utilizar esta informação para prover conhecimento útil para empresas. O objetivo deste trabalho é produzir event logs de diferentes cenários de simulação e analisá-los utilizando mineração de processos. Estes cenários tentam simular atividades contidianas em um ambiente de escritório. Um exemplo é o cenário de recurso fuzzy que tenta simular a incerteza inerente em atividades realizas por humanos. Para alcançar este objetivo algumas ferramentas open-source foram utilizadas. CPN Tools foi utilizada para construir e simular a Workflow net baseada na rede “Handle Complaint Process” e gerar os event logs durante as simulações. ProM foi utilizado para aplicar os algoritmos de process discovery e conformance checking nos event logs gerados. O algoritmo utilizado foi o Inductive Visual Miner. A comparação entre os cenários mostrou uma diferença significativa entre os tempos de execução devido ao propósito de cada cenário. Com este tipo de simulação de cenários, donos de negócios podem realizar simulações de possíveis cenários de sua empresa e estimar melhores deadlines para seus clientes.

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 The RALph miner for automated discovery and verification of resource-aware process models

2020 ◽  
Vol 19 (6) ◽  
pp. 1415-1441
Author(s):  
Cristina Cabanillas ◽  
Lars Ackermann ◽  
Stefan Schönig ◽  
Christian Sturm ◽  
Jan Mendling
Keyword(s):  
Process Model ◽  
Model Verification ◽  
Process Models ◽  
Resource Assignment ◽  
Process Discovery ◽  
Event Logs ◽  
Different Types ◽  
Automated Discovery ◽  
And Performance ◽  
Resource Aware

Abstract Automated process discovery is a technique that extracts models of executed processes from event logs. Logs typically include information about the activities performed, their timestamps and the resources that were involved in their execution. Recent approaches to process discovery put a special emphasis on (human) resources, aiming at constructing resource-aware process models that contain the inferred resource assignment constraints. Such constraints can be complex and process discovery approaches so far have missed the opportunity to represent expressive resource assignments graphically together with process models. A subsequent verification of the extracted resource-aware process models is required in order to check the proper utilisation of resources according to the resource assignments. So far, research on discovering resource-aware process models has assumed that models can be put into operation without modification and checking. Integrating resource mining and resource-aware process model verification faces the challenge that different types of resource assignment languages are used for each task. In this paper, we present an integrated solution that comprises (i) a resource mining technique that builds upon a highly expressive graphical notation for defining resource assignments; and (ii) automated model-checking support to validate the discovered resource-aware process models. All the concepts reported in this paper have been implemented and evaluated in terms of feasibility and performance.

scholarly journals Data-Driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs

2017 ◽  
pp. 545-560 ◽  
Author(s):  
Felix Mannhardt ◽  
Massimiliano de Leoni ◽  
Hajo A. Reijers ◽  
Wil M. P. van der Aalst
Keyword(s):  
Data Driven ◽  
Process Discovery ◽  
Event Logs

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 A task-level parallelism approach for process discovery

2018 ◽  
Vol 7 (4) ◽  
pp. 2446
Author(s):  
Muktikanta Sahu ◽  
Rupjit Chakraborty ◽  
Gopal Krishna Nayak
Keyword(s):  
Process Model ◽  
Process Mining ◽  
Programming Model ◽  
Parallel Implementation ◽  
Primary Objective ◽  
Process Models ◽  
Task Parallelism ◽  
Process Discovery ◽  
Event Logs ◽  
Computationally Intensive

Building process models from the available data in the event logs is the primary objective of Process discovery. Alpha algorithm is one of the popular algorithms accessible for ascertaining a process model from the event logs in process mining. The steps involved in the Alpha algorithm are computationally rigorous and this problem further manifolds with the exponentially increasing event log data. In this work, we have exploited task parallelism in the Alpha algorithm for process discovery by using MPI programming model. The proposed work is based on distributed memory parallelism available in MPI programming for performance improvement. Independent and computationally intensive steps in the Alpha algorithm are identified and task parallelism is exploited. The execution time of serial as well as parallel implementation of Alpha algorithm are measured and used for calculating the extent of speedup achieved. The maximum and minimum speedups obtained are 3.97x and 3.88x respectively with an average speedup of 3.94x.

Quality Dimensions in Process Discovery: The Importance of Fitness, Precision, Generalization and Simplicity

2014 ◽  
Vol 23 (01) ◽  
pp. 1440001 ◽  
Author(s):  
J. C. A. M. Buijs ◽  
B. F. van Dongen ◽  
W. M. P. van der Aalst
Keyword(s):  
Process Models ◽  
Discovery Process ◽  
Process Discovery ◽  
Event Logs ◽  
Quality Dimensions ◽  
Discovery Algorithms

Process discovery algorithms typically aim at discovering process models from event logs that best describe the recorded behavior. Often, the quality of a process discovery algorithm is measured by quantifying to what extent the resulting model can reproduce the behavior in the log, i.e. replay fitness. At the same time, there are other measures that compare a model with recorded behavior in terms of the precision of the model and the extent to which the model generalizes the behavior in the log. Furthermore, many measures exist to express the complexity of a model irrespective of the log.In this paper, we first discuss several quality dimensions related to process discovery. We further show that existing process discovery algorithms typically consider at most two out of the four main quality dimensions: replay fitness, precision, generalization and simplicity. Moreover, existing approaches cannot steer the discovery process based on user-defined weights for the four quality dimensions.This paper presents the ETM algorithm which allows the user to seamlessly steer the discovery process based on preferences with respect to the four quality dimensions. We show that all dimensions are important for process discovery. However, it only makes sense to consider precision, generalization and simplicity if the replay fitness is acceptable.

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