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.

Recovery and Refinement of Business Process Models for Web Applications

2018 ◽  
pp. 248-291
Author(s):  
Alessandro Marchetto ◽  
Chiara Di Francescomarino
Keyword(s):  
Business Process ◽  
Process Model ◽  
Business Processes ◽  
Web Applications ◽  
Process Mining ◽  
Relevant Information ◽  
Process Models ◽  
Model Complexity ◽  
Model Refinement ◽  
Business Process Models

Web Applications (WAs) have been often used to expose business processes to the users. WA modernization and evolution are complex and time-consuming activities that can be supported by software documentation (e.g., process models). When, as often happens, documentation is missing or is incomplete, documentation recovery and mining represent an important opportunity for reconstructing or completing it. Existing process-mining approaches, however, tend to recover models that are quite complex, rich, and intricate, thus difficult to understand and use for analysts and developers. Model refinement approaches have been presented in the literature to reduce the model complexity and intricateness while preserving the capability of representing the relevant information. In this chapter, the authors summarize approaches to mine first and refine later business process models from existing WAs. In particular, they present two process model refinement approaches: (1) re-modularization and (2) reduction. The authors introduce the techniques and show how to apply them to WAs.

Mining business rules from business process model repositories

2015 ◽  
Vol 21 (4) ◽  
pp. 820-836 ◽  
Author(s):  
Jantima Polpinij ◽  
Aditya Ghose ◽  
Hoa Khanh Dam
Keyword(s):  
Business Process ◽  
Process Model ◽  
Process Mining ◽  
Process Models ◽  
Sequential Patterns ◽  
Business Rules ◽  
Data Set ◽  
Content Type ◽  
Market Growth ◽  
Business Process Models

Purpose – Business process has become the core assets of many organizations and it becomes increasing common for most medium to large organizations to have collections of hundreds or even thousands of business process models. The purpose of this paper is to explore an alternative dimension to process mining in which the objective is to extract process constraints (or business rules) as opposed to business process models. It also focusses on an alternative data set – process models as opposed to process instances (i.e. event logs). Design/methodology/approach – The authors present a new method of knowledge discovery to find business activity sequential patterns embedded in process model repositories. The extracted sequential patterns are considered as business rules. Findings – The authors find significant knowledge hidden in business processes model repositories. The hidden knowledge is considered as business rules. The business rules extracted from process models are significant and valid sequential correlations among business activities belonging to a particular organization. Such business rules represent business constraints that have been encoded in business process models. Experimental results have indicated the effectiveness and accuracy of the approach in extracting business rules from repositories of business process models. Social implications – This research will assist organizations to extract business rules from their existing business process models. The discovered business rules are very important for any organization, where rules can be used to help organizations better achieve goals, remove obstacles to market growth, reduce costly mistakes, improve communication, comply with legal requirements, and increase customer loyalty. Originality/value – There has very been little work in mining business process models as opposed to an increasing number of very large collections of business process models. This work has filled this gap with the focus on extracting business rules.

Recovery and Refinement of Business Process Models for Web Applications

2013 ◽  
pp. 134-175
Author(s):  
Alessandro Marchetto ◽  
Chiara Di Francescomarino
Keyword(s):  
Business Process ◽  
Process Model ◽  
Business Processes ◽  
Web Applications ◽  
Process Mining ◽  
Relevant Information ◽  
Process Models ◽  
Model Complexity ◽  
Model Refinement ◽  
Business Process Models

Web Applications (WAs) have been often used to expose business processes to the users. WA modernization and evolution are complex and time-consuming activities that can be supported by software documentation (e.g., process models). When, as often happens, documentation is missing or is incomplete, documentation recovery and mining represent an important opportunity for reconstructing or completing it. Existing process-mining approaches, however, tend to recover models that are quite complex, rich, and intricate, thus difficult to understand and use for analysts and developers. Model refinement approaches have been presented in the literature to reduce the model complexity and intricateness while preserving the capability of representing the relevant information. In this chapter, the authors summarize approaches to mine first and refine later business process models from existing WAs. In particular, they present two process model refinement approaches: (1) re-modularization and (2) reduction. The authors introduce the techniques and show how to apply them to WAs.

scholarly journals Event Log Preprocessing for Process Mining: A Review

2021 ◽  
Vol 11 (22) ◽  
pp. 10556
Author(s):  
Heidy M. Marin-Castro ◽  
Edgar Tello-Leal
Keyword(s):  
Business Process ◽  
Process Model ◽  
Process Mining ◽  
Data Cleaning ◽  
Data Preprocessing ◽  
Process Models ◽  
Event Data ◽  
Event Logs ◽  
Event Log ◽  
Preprocessing Technique

Process Mining allows organizations to obtain actual business process models from event logs (discovery), to compare the event log or the resulting process model in the discovery task with the existing reference model of the same process (conformance), and to detect issues in the executed process to improve (enhancement). An essential element in the three tasks of process mining (discovery, conformance, and enhancement) is data cleaning, used to reduce the complexity inherent to real-world event data, to be easily interpreted, manipulated, and processed in process mining tasks. Thus, new techniques and algorithms for event data preprocessing have been of interest in the research community in business process. In this paper, we conduct a systematic literature review and provide, for the first time, a survey of relevant approaches of event data preprocessing for business process mining tasks. The aim of this work is to construct a categorization of techniques or methods related to event data preprocessing and to identify relevant challenges around these techniques. We present a quantitative and qualitative analysis of the most popular techniques for event log preprocessing. We also study and present findings about how a preprocessing technique can improve a process mining task. We also discuss the emerging future challenges in the domain of data preprocessing, in the context of process mining. The results of this study reveal that the preprocessing techniques in process mining have demonstrated a high impact on the performance of the process mining tasks. The data cleaning requirements are dependent on the characteristics of the event logs (voluminous, a high variability in the set of traces size, changes in the duration of the activities. In this scenario, most of the surveyed works use more than a single preprocessing technique to improve the quality of the event log. Trace-clustering and trace/event level filtering resulted in being the most commonly used preprocessing techniques due to easy of implementation, and they adequately manage noise and incompleteness in the event logs.

scholarly journals Correlation Miner: Mining Business Process Models and Event Correlations Without Case Identifiers

2017 ◽  
Vol 26 (02) ◽  
pp. 1742002 ◽  
Author(s):  
Shaya Pourmirza ◽  
Remco Dijkman ◽  
Paul Grefen
Keyword(s):  
Business Process ◽  
Process Model ◽  
Process Models ◽  
Unique Case ◽  
Capture Process ◽  
Business Process Models ◽  
Event Logs ◽  
Service Oriented ◽  
Discovery Algorithms ◽  
Oriented System

Process discovery algorithms aim to capture process models from event logs. These algorithms have been designed for logs in which the events that belong to the same case are related to each other — and to that case — by means of a unique case identifier. However, in service-oriented systems, these case identifiers are rarely stored beyond request-response pairs, which makes it hard to relate events that belong to the same case. This is known as the correlation challenge. This paper addresses the correlation challenge by introducing a technique, called the correlation miner, that facilitates discovery of business process models when events are not associated with a case identifier. It extends previous work on the correlation miner, by not only enabling the discovery of the process model, but also detecting which events belong to the same case. Experiments performed on both synthetic and real-world event logs show the applicability of the correlation miner. The resulting technique enables us to observe a service-oriented system and determine — with high accuracy — which request-response pairs sent by different communicating parties are related to each other.

scholarly journals Business processes exceptions in relation to operational performance

2019 ◽  
Vol 25 (5) ◽  
pp. 908-922 ◽  
Author(s):  
Remco Dijkman ◽  
Oktay Turetken ◽  
Geoffrey Robert van IJzendoorn ◽  
Meint de Vries
Keyword(s):  
Business Process ◽  
Process Model ◽  
Business Processes ◽  
Process Models ◽  
Operational Performance ◽  
Normal Flow ◽  
Throughput Time ◽  
Content Type ◽  
Business Process Models ◽  
Practical Implications

Purpose Business process models describe the way of working in an organization. Typically, business process models distinguish between the normal flow of work and exceptions to that normal flow. However, they often present an idealized view. This means that unexpected exceptions – exceptions that are not modeled in the business process model – can also occur in practice. This has an effect on the efficiency of the organization, because information systems are not developed to handle unexpected exceptions. The purpose of this paper is to study the relation between the occurrence of exceptions and operational performance. Design/methodology/approach The paper does this by analyzing the execution logs of business processes from five organizations, classifying execution paths as normal or exceptional. Subsequently, it analyzes the differences between normal and exceptional paths. Findings The results show that exceptions are related to worse operational performance in terms of a longer throughput time and that unexpected exceptions relate to a stronger increase in throughput time than expected exceptions. Practical implications These findings lead to practical implications on policies that can be followed with respect to exceptions. Most importantly, unexpected exceptions should be avoided by incorporating them into the process – and thus transforming them into expected exceptions – as much as possible. Also, as not all exceptions lead to longer throughput times, continuous improvement should be employed to continuously monitor the occurrence of exceptions and make decisions on their desirability in the process. Originality/value While work exists on analyzing the occurrence of exceptions in business processes, especially in the context of process conformance analysis, to the best of the authors’ knowledge this is the first work that analyzes the possible consequences of such exceptions.

scholarly journals Data Model and Software Architecture for Business Process Model Generator

2018 ◽  
Vol 2 (4-2) ◽  
pp. 349
Author(s):  
Ivaylo Kamenarov ◽  
Katalina Grigorova
Keyword(s):  
Software Architecture ◽  
Business Process ◽  
Data Model ◽  
Process Model ◽  
Process Models ◽  
Process Chain ◽  
Business Process Models ◽  
Model Generator ◽  
The Individual ◽  
Natural Way

This paper describes the internal data model for a business process generator. Business process models are stored in an Event-driven process chain notation that provides a natural way to link the individual elements of a process. There is a software architecture that makes it easy to communicate with users as well as external systems.

scholarly journals Business Process Models in the Context of Predictive Process Monitoring

2021 ◽  
Vol 28 (1) ◽  
pp. 39-46
Author(s):  
Florian Spree
Keyword(s):  
Process Monitoring ◽  
Business Process ◽  
Process Model ◽  
Academic Research ◽  
Research Area ◽  
Process Models ◽  
Business Process Model ◽  
Business Process Models ◽  
Wide Range ◽  
Predictive Process

Predictive process monitoring is a subject of growing interest in academic research. As a result, an increased number of papers on this topic have been published. Due to the high complexity in this research area a wide range of different experimental setups and methods have been applied which makes it very difficult to reliably compare research results. This paper's objective is to investigate how business process models and their characteristics are used during experimental setups and how they can contribute to academic research. First, a literature review is conducted to analyze and discuss the awareness of business process models in experimental setups. Secondly, the paper discusses identified research problems and proposes the concept of a web-based business process model metric suite and the idea of ranked metrics. Through a metric suite researchers and practitioners can automatically evaluate business process model characteristics in their future work. Further, a contextualization of metrics by introducing a ranking of characteristics can potentially indicate how the outcome of experimental setups will be. Hence, the paper's work demonstrates the importance of business process models and their characteristics in the context of predictive process monitoring and proposes the concept of a tool approach and ranking to reliably evaluate business process models characteristics.

scholarly journals Collaborative method to maintain business process models updated

2014 ◽  
Vol 11 (2) ◽  
pp. 461-480 ◽  
Author(s):  
Nuno Castela ◽  
Paulo Dias ◽  
Marielba Zacarias ◽  
José Tribolet
Keyword(s):  
Case Studies ◽  
Business Process ◽  
Process Model ◽  
Business Processes ◽  
Process Models ◽  
Organizational Environment ◽  
Business Process Model ◽  
Business Process Models ◽  
Definition Of ◽  
Over Time

Business process models are often forgotten after their creation and its representation is not usually updated. This appears to be negative as processes evolve over time. This paper discusses the issue of business process models maintenance through the definition of a collaborative method that creates interaction contexts enabling business actors to discuss about business processes, sharing business knowledge. The collaboration method extends the discussion about existing process representations to all stakeholders promoting their update. This collaborative method contributes to improve business process models, allowing updates based in change proposals and discussions, using a groupware tool that was developed. Four case studies were developed in real organizational environment. We came to the conclusion that the defined method and the developed tool can help organizations to maintain a business process model updated based on the inputs and consequent discussions taken by the organizational actors who participate in the processes.

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