scholarly journals Process Discovery Enhancement with Trace Clustering and Profiling

2021 ◽  
Vol 5 (4) ◽  
pp. 1-13
Author(s):  
Muhammad Faizan ◽  
Megat F. Zuhairi ◽  
Shahrinaz Ismail
Keyword(s):  
Process Model ◽  
Process Mining ◽  
Process Models ◽  
Patient Treatment ◽  
Process Discovery ◽  
Processing Times ◽  
Data Process ◽  
Event Logs ◽  
Homogeneous Subset ◽  
Process Event

The potential in process mining is progressively growing due to the increasing amount of event-data. Process mining strategies use event-logs to automatically classify process models, recommend improvements, predict processing times, check conformance, and recognize anomalies/deviations and bottlenecks. However, proper handling of event-logs while evaluating and using them as input is crucial to any process mining technique. When process mining techniques are applied to flexible systems with a large number of decisions to take at runtime, the outcome is often unstructured or semi-structured process models that are hard to comprehend. Existing approaches are good at discovering and visualizing structured processes but often struggle with less structured ones. Surprisingly, process mining is most useful in domains where flexibility is desired. A good illustration is the "patient treatment" process in a hospital, where the ability to deviate from dealing with changing conditions is crucial. It is useful to have insights into actual operations. However, there is a significant amount of diversity, which contributes to complicated, difficult-to-understand models. Trace clustering is a method for decreasing the complexity of process models in this context while also increasing their comprehensibility and accuracy. This paper discusses process mining, event-logs, and presenting a clustering approach to pre-process event-logs, i.e., a homogeneous subset of the event-log is created. A process model is generated for each subset. These homogeneous subsets are then evaluated independently from each other, which significantly improving the quality of mining results in flexible environments. The presented approach improves the fitness and precision of a discovered model while reducing its complexity, resulting in well-structured and easily understandable process discovery results.

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.

scholarly journals Trace Clustering: A Preprocessing Method to Improve the Performance of Process Discovery

2021 ◽  
Vol 10 (09) ◽  
pp. 116-121
Author(s):  
Huiling LI ◽  
Shuaipeng ZHANG ◽  
Xuan SU
Keyword(s):  
Process Model ◽  
Process Mining ◽  
Process Models ◽  
Clustering Methods ◽  
Clustering Method ◽  
Model Quality ◽  
Process Discovery ◽  
Event Logs ◽  
Clustering Model ◽  
Preprocessing Method

The information system collects a large number of business process event logs, and process discovery aims to discover process models from the event logs. Many process discovery methods have been proposed, but most of them still have problems when processing event logs, such as low mining efficiency and poor process model quality. The trace clustering method allows to decompose original log to effectively solve these problems. There are many existing trace clustering methods, such as clustering based on vector space approaches, context-aware trace clustering, model-based sequence clustering, etc. The clustering effects obtained by different trace clustering methods are often different. Therefore, this paper proposes a preprocessing method to improve the performance of process discovery, called as trace clustering. Firstly, the event log is decomposed into a set of sub-logs by trace clustering method, Secondly, the sub-logs generate process models respectively by the process mining method. The experimental analysis on the datasets shows that the method proposed not only effectively improves the time performance of process discovery, but also improves the quality of the process model.

scholarly journals Towards Aspects Identification in Business Process Through Process Mining

2015 ◽  
Author(s):  
Bruna Brandão ◽  
Flávia Santoro ◽  
Leonardo Azevedo
Keyword(s):  
Programming Languages ◽  
Business Process ◽  
Process Model ◽  
Process Mining ◽  
Process Models ◽  
Preliminary Evaluation ◽  
Crosscutting Concerns ◽  
Business Process Models ◽  
Event Logs ◽  
Initial Results

In business process models, elements can be scattered (repeated) within different processes, making it difficult to handle changes, analyze process for improvements, or check crosscutting impacts. These scattered elements are named as Aspects. Similar to the aspect-oriented paradigm in programming languages, in BPM, aspect handling has the goal to modularize the crosscutting concerns spread across the models. This process modularization facilitates the management of the process (reuse, maintenance and understanding). The current approaches for aspect identification are made manually; thus, resulting in the problem of subjectivity and lack of systematization. This paper proposes a method to automatically identify aspects in business process from its event logs. The method is based on mining techniques and it aims to solve the problem of the subjectivity identification made by specialists. The initial results from a preliminary evaluation showed evidences that the method identified correctly the aspects present in the process model.

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 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.

An Integrated Approach for Discovering Process Models According to Business Process Types

2021 ◽  
Vol 16 ◽  
pp. 1-14
Author(s):  
Zineb Lamghari
Keyword(s):  
Business Process ◽  
Domain Knowledge ◽  
Process Model ◽  
Process Mining ◽  
Integrated Approach ◽  
Process Models ◽  
Management Support ◽  
Event Data ◽  
Process Discovery ◽  
Discovery Algorithms

Process discovery technique aims at automatically generating a process model that accurately describes a Business Process (BP) based on event data. Related discovery algorithms consider recorded events are only resulting from an operational BP type. While the management community defines three BP types, which are: Management, Support and Operational. They distinguish each BP type by different proprieties like the main business process objective as domain knowledge. This puts forward the lack of process discovery technique in obtaining process models according to business process types (Management and Support). In this paper, we demonstrate that business process types can guide the process discovery technique in generating process models. A special interest is given to the use of process mining to deal with this challenge.

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.

scholarly journals Process Mining Based Software for Automated Business Process Discovery

2020 ◽  
Vol 9 (5) ◽  
pp. 2413-2416
Keyword(s):  
Business Process ◽  
Process Model ◽  
Process Mining ◽  
Computer Software ◽  
Process Discovery ◽  
Event Logs ◽  
Operational Characteristics ◽  
Common Framework ◽  
The Common

Automated Business Process Discovery is a rising field that depends vigorously on computer software. Software do the automatic analysis of the several documents such as audits and event logs and generate useful,novel,hidden and fascinating information from that. The information produce from the software recognize the process model as well as investigates varieties and gives clients a vastly improved picture of what a particular business process resembles, and how changes would influence the business in general. This paper presents the common framework activities of process mining in the context to all well known software. The paper also describes the open source process mining software with their operational characteristics. Finally, paper represents the role of process mining software for various famous industries.

scholarly journals Mining Users' Intents from Logs

2015 ◽  
Vol 6 (2) ◽  
pp. 43-71 ◽  
Author(s):  
Ghazaleh Khodabandelou ◽  
Charlotte Hug ◽  
Camille Salinesi
Keyword(s):  
Systems Engineering ◽  
Process Modeling ◽  
Process Model ◽  
Process Mining ◽  
Process Models ◽  
Levels Of Abstraction ◽  
Engineering Research ◽  
Event Logs ◽  
Novel Approach ◽  
Different Levels

Intentions play a key role in information systems engineering. Research on process modeling has highlighted that specifying intentions can expressly mitigate many problems encountered in process modeling as lack of flexibility or adaptation. Process mining approaches mine processes in terms of tasks and branching. To identify and formalize intentions from event logs, this work presents a novel approach of process mining, called Map Miner Method (MMM). This method automates the construction of intentional process models from event logs. First, MMM estimates users' strategies (i.e., the different ways to fulfill the intentions) in terms of their activities. These estimated strategies are then used to infer users' intentions at different levels of abstraction using two tailored algorithms. MMM constructs intentional process models with respect to the Map metamodel formalism. MMM is applied on a real-world dataset, i.e. event logs of developers of Eclipse UDC (Usage Data Collector). The resulting Map process model provides a precious understanding of the processes followed by the developers, and also provide feedback on the effectiveness and demonstrate scalability of MMM.

scholarly journals Application of Genetic Algorithms for Finding Edit Distance between Process Models

2018 ◽  
Vol 25 (6) ◽  
pp. 711-725
Author(s):  
Anna A. Kalenkova ◽  
Danil A. Kolesnikov
Keyword(s):  
Genetic Algorithm ◽  
Process Model ◽  
Edit Distance ◽  
Process Mining ◽  
Search Algorithm ◽  
Process Models ◽  
Graph Edit Distance ◽  
Minimal Graph ◽  
Event Logs ◽  
Small Models

Finding graph-edit distance (graph similarity) is an important task in many computer science areas, such as image analysis, machine learning, chemicalinformatics. Recently, with the development of process mining techniques, it became important to adapt and apply existing graph analysis methods to examine process models (annotated graphs) discovered from event data. In particular, finding graph-edit distance techniques can be used to reveal patterns (subprocesses), compare discovered process models. As it was shown experimentally and theoretically justified, exact methods for finding graph-edit distances between discovered process models (and graphs in general) are computationally expensive and can be applied to small models only. In this paper, we present and assess accuracy and performance characteristics of an inexact genetic algorithm applied to find distances between process models discovered from event logs. In particular, we find distances between BPMN (Business Process Model and Notation) models discovered from event logs by using different process discovery algorithms. We show that the genetic algorithm allows us to dramatically reduce the time of comparison and produces results which are close to the optimal solutions (minimal graph edit distances calculated by the exact search algorithm).

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