Process Mining: A Recent Framework for Extracting a Model from Event Logs (original) (raw)
Related papers
2016
Il presente elaborato esplora l’attitudine delle organizzazioni nei confronti dei processi di business che le sostengono: dalla semi-assenza di struttura, all’organizzazione funzionale, fino all’avvento del Business Process Reengineering e del Business Process Management, nato come superamento dei limiti e delle problematiche del modello precedente. All’interno del ciclo di vita del BPM, trova spazio la metodologia del process mining, che permette un livello di analisi dei processi a partire dagli event data log, ossia dai dati di registrazione degli eventi, che fanno riferimento a tutte quelle attività supportate da un sistema informativo aziendale. Il process mining può essere visto come naturale ponte che collega le discipline del management basate sui processi (ma non data-driven) e i nuovi sviluppi della business intelligence, capaci di gestire e manipolare l’enorme mole di dati a disposizione delle aziende (ma che non sono process-driven). Nella tesi, i requisiti e le tecnolog...
Process Mining – New era in process management - A Survey
Process Mining is extraction of process models from event logs recorded by information systems. It acts as a bridge between data mining and business process. It is possible to use process mining to monitor deviations (e.g., comparing the observed events with predefined models or business rules in the context of SOX). . This technology has become available only recently, but it can be applied to any type of operational processes (organizations and systems). Example applications include: analyzing treatment processes in hospitals, improving customer service processes in a multinational, understanding the browsing behavior of customers using a booking site, analyzing failures of a baggage handling system, and improving the user interface of an X-ray machine The paper does a survey of important process mining aspects- its perspective, types, tools available for it, advantages and the fundamental algorithm used in Process Mining- Alpha Miner Algorithm.
Business Process Mining Approaches: A Relative Comparison
ArXiv, 2015
Recently, information systems like ERP, CRM and WFM record different business events or activities in a log named as event log. Process mining aims at extracting information from event logs to capture business process as it is being executed. Process mining is an important learning task based on captured processes. In order to be competent organizations in the business world; they have to adjust their business process along with the changing environment. Sometimes a change in the business process implies a change into the whole system. Process mining allows for the automated discovery of process models from event logs. Process mining techniques has the ability to support automatically business process (re)design. Typically, these techniques discover a concrete workflow model and all possible processes registered in a given events log. In this paper, detailed comparison among process mining methods used in the business process mining and differences in their approaches have been prov...
Business process mining: from theory to practice
… Process Management Journal, 2012
Purpose -This paper presents a comparison of a number of business process mining tools currently available in the UK market. An outline of the practice of business process mining is given along with an analysis of the main techniques developed by academia and commercial entities. This paper also acts as a primer for the acceptance and further use of process mining in industry suggesting future directions for this practice.
Enhancement in process mining : guideline for process owner and process analyst
2019
Business process in an organization consists of numerous activities performed by different actors. A process model is a representation of process executions. In practices, a process model is typically created through meetings and interviews with various stakeholders in the organization. This traditional approach usually takes up to several years to complete. On the other hand, process mining offers an automatic means to develop a process model. The process model discovered by process mining is based on actual process behavior recorded in the event log. However, process mining is a relatively young field, and there is a lack of attention about how to perform a process mining project. In this thesis, we proposed a three-phase process analysis approach using process mining techniques involving process owner and process analyst. The application of the proposed approach is demonstrated using real-life data sets. The approach elaborations and result of the demonstration is combined into a...
Process Mining: A 360 Degree Overview
Process mining enables organizations to uncover their actual processes, provide insights, diagnose problems, and automatically trigger corrective actions. Process mining is an emerging scientific discipline positioned at the intersection between process science and data science. The combination of process modeling and analysis with the event data present in today's information systems provides new means to tackle compliance and performance problems. This chapter provides an overview of the field of process mining introducing the different types of process mining (e.g., process discovery and conformance checking) and the basic ingredients, i.e., process models and event data. To prepare for later chapters, event logs are introduced in detail (including pointers to standards for event data such as XES and OCEL). Moreover, a brief overview of process mining applications and software is given.
Process mining using BPMN: relating event logs and process models
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A New Approach for Discovering Business Process Models from Event Logs
SSRN Electronic Journal, 2000
Process mining is the automated acquisition of process models from the event logs of information systems. Although process mining has many useful applications, not all inherent difficulties have been sufficiently solved. A first difficulty is that process mining is often limited to a setting of non-supervised learning since negative information is often not available. Moreover, state transitions in processes are often dependent on the traversed path, which limits the appropriateness of search techniques based on local information in the event log. Another difficulty is that case data and resource properties that can also influence state transitions are time-varying properties, such that they cannot be considered as cross-sectional. This article investigates the use of first-order, ILP classification learners for process mining and describes techniques for dealing with each of the above mentioned difficulties. To make process mining a supervised learning task, we propose to include negative events in the event log. When event logs contain no negative information, a technique is described to add artificial negative examples to a process log. To capture history-dependent behavior the article proposes to take advantage of the multi-relational nature of ILP classification learners. Multi-relational process mining allows to search for patterns among multiple event rows in the event log, effectively basing its search on global information. To deal with timevarying case data and resource properties, a closed-world version of the Event Calculus has to be added as background knowledge, transforming the event log effectively in a temporal database. First experiments on synthetic event logs show that first-order classification learners are capable of predicting the behavior with high accuracy, even under conditions of noise.