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.

scholarly journals Mining workflow processes from distributed workflow enactment event logs

2012 ◽  
pp. 528-553
Keyword(s):  
Process Model ◽  
Process Mining ◽  
Workflow Management ◽  
Computer Software ◽  
Structural Data ◽  
Control Flow ◽  
Process Models ◽  
Distributed Workflow ◽  
Workflow Mining ◽  
Event Logs

Workflow management systems help to execute, monitor and manage work process flow and execution. These systems, as they are executing, keep a record of who does what and when (e.g. log of events). The activity of using computer software to examine these records, and deriving various structural data results is called workflow mining. The workflow mining activity, in general, needs to encompass behavioral (process/control-flow), social, informational (data-flow), and organizational perspectives; as well as other perspectives, because workflow systems are "people systems" that must be designed, deployed, and understood within their social and organizational contexts. This paper particularly focuses on mining the behavioral aspect of workflows from XML-based workflow enactment event logs, which are vertically (semantic-driven distribution) or horizontally (syntactic-driven distribution) distributed over the networked workflow enactment components. That is, this paper proposes distributed workflow mining approaches that are able to rediscover ICN-based structured workflow process models through incrementally amalgamating a series of vertically or horizontally fragmented temporal workcases. And each of the approaches consists of a temporal fragment discovery algorithm, which is able to discover a set of temporal fragment models from the fragmented workflow enactment event logs, and a workflow process mining algorithm which rediscovers a structured workflow process model from the discovered temporal fragment models. Where, the temporal fragment model represents the concrete model of the XML-based distributed workflow fragment events log.

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 https://ijsea.com/archive/volume10/issue9/IJSEA10091005.pdf

2021 ◽  
Vol 10 (9) ◽  
pp. 144-147
Author(s):  
Huiling LI ◽  
Xuan SU ◽  
Shuaipeng ZHANG
Keyword(s):  
Process Model ◽  
Large Scale ◽  
Process Mining ◽  
Data Set ◽  
Systems Model ◽  
Event Logs ◽  
Event Log ◽  
Low Efficiency ◽  
Sampling Approach

Massive amounts of business process event logs are collected and stored by modern information systems. Model discovery aims to discover a process model from such event logs, however, most of the existing approaches still suffer from low efficiency when facing large-scale event logs. Event log sampling techniques provide an effective scheme to improve the efficiency of process discovery, but the existing techniques still cannot guarantee the quality of model mining. Therefore, a sampling approach based on set coverage algorithm named set coverage sampling approach is proposed. The proposed sampling approach has been implemented in the open-source process mining toolkit ProM. Furthermore, experiments using a real event log data set from conformance checking and time performance analysis show that the proposed event log sampling approach can greatly improve the efficiency of log sampling on the premise of ensuring the quality of model mining.

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 An Optimization Approach for Mining of Process Models with Infrequent Behaviors Integrating Data Flow and Control Flow

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Li-li Wang ◽  
Xian-wen Fang ◽  
Esther Asare ◽  
Fang Huan
Keyword(s):  
Process Model ◽  
Data Flow ◽  
Process Mining ◽  
Control Flow ◽  
Process Models ◽  
Frequent Pattern ◽  
Optimization Approach ◽  
Event Log ◽  
Flow Information ◽  
And Control

Infrequent behaviors of business process refer to behaviors that occur in very exceptional cases, and their occurrence frequency is low as their required conditions are rarely fulfilled. Hence, a strong coupling relationship between infrequent behavior and data flow exists. Furthermore, some infrequent behaviors may reveal very important information about the process. Thus, not all infrequent behaviors should be disregarded as noise, and identifying infrequent but correct behaviors in the event log is vital to process mining from the perspective of data flow. Existing process mining approaches construct a process model from frequent behaviors in the event log, mostly concentrating on control flow only, without considering infrequent behavior and data flow information. In this paper, we focus on data flow to extract infrequent but correct behaviors from logs. For an infrequent trace, frequent patterns and interactive behavior profiles are combined to find out which part of the behavior in the trace occurs in low frequency. And, conditional dependency probability is used to analyze the influence strength of the data flow information on infrequent behavior. An approach for identifying effective infrequent behaviors based on the frequent pattern under data awareness is proposed correspondingly. Subsequently, an optimization approach for mining of process models with infrequent behaviors integrating data flow and control flow is also presented. The experiments on synthetic and real-life event logs show that the proposed approach can distinguish effective infrequent behaviors from noise compared with others. The proposed approaches greatly improve the fitness of the mined process model without significantly decreasing its precision.

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 BPM2Text: A language independent framework for Business Process Models to Natural Language Text

2016 ◽  
Vol 9 (4) ◽  
pp. 38-56 ◽  
Author(s):  
Raphael De Almeida Rodrigues ◽  
Leonardo Guerreiro Azevedo ◽  
Kate Cerqueira Revoredo
Keyword(s):  
Natural Language ◽  
Business Process ◽  
Process Model ◽  
Empirical Support ◽  
Process Models ◽  
Natural Language Text ◽  
Business Process Models ◽  
Output Quality

The proper representation of a Business process is important for its execution and understanding. BPMN has been used as the standard notation for business process models, however domain specialists, which are experts in the business, do not have necessarily the modeling skills to easily read a business process model. It is easier for them to read in natural language. In this work, we propose a language-independent framework, instantiated using Java standard technology, for generating automatically natural language texts from business process models. A case study was conducted to evaluate the quality of the generated text. We found empirical support that the textual work instructions can be considered equivalent, in terms of knowledge representation, to process models represented in BPMN. Regarding the framework output quality (textual descriptions) 86% of the subjects claims that it vary from excellent to good.

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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