Relevant bibliographies by topics / Business process discovery

Academic literature on the topic 'Business process discovery'

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Published: 4 June 2021
Last updated: 19 February 2022

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Journal articles on the topic "Business process discovery"

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Dymora, Paweł, Maciej Koryl, and Mirosław Mazurek. "Process Discovery in Business Process Management Optimization." Information 10, no. 9 (August 29, 2019): 270. http://dx.doi.org/10.3390/info10090270.

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Appropriate business processes management (BPM) within an organization can help attain organizational goals. It is particularly important to effectively manage the lifecycle of these processes for organizational effectiveness in improving ever-growing performance and competitivity-building across the company. This paper presents a process discovery and how we can use it in a broader framework supporting self-organization in BPM. Process discovery is intrinsically associated with the process lifecycle. We have made a pre-evaluation of the usefulness of our facts using a generated log file. We also compared visualizations of the outcomes of our approach with different cases and showed performance characteristics of the cash loan sales process.
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Huang, Ying, Liyun Zhong, and Yan Chen. "Filtering Infrequent Behavior in Business Process Discovery by Using the Minimum Expectation." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 2 (April 2020): 1–15. http://dx.doi.org/10.4018/ijcini.2020040101.

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The aim of process discovery is to discover process models from the process execution data stored in event logs. In the era of “Big Data,” one of the key challenges is to analyze the large amounts of collected data in meaningful and scalable ways. Most process discovery algorithms assume that all the data in an event log fully comply with the process execution specification, and the process event logs are no exception. However, real event logs contain large amounts of noise and data from irrelevant infrequent behavior. The infrequent behavior or noise has a negative influence on the process discovery procedure. This article presents a technique to remove infrequent behavior from event logs by calculating the minimum expectation of the process event log. The method was evaluated in detail, and the results showed that its application in existing process discovery algorithms significantly improves the quality of the discovered process models and that it scales well to large datasets.
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Zacarias, Marielba, and Paula Ventura Martins. "Business Alignment Methodology." Information Resources Management Journal 27, no. 1 (January 2014): 1–20. http://dx.doi.org/10.4018/irmj.2014010101.

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Current business process modeling methodologies offer little guidance regarding how to discover and maintain business process models aligned with their actual execution. This paper describes how to achieve this goal by uncovering, supervising and improving business process models based on actual work practices, using the Business Alignment Methodology (BAM). BAM aims at enabling business process modeling, supervision and improvement through the distinction of two dimensions; (1) business processes and (2) work practices. BAM encompasses three phases; (1) Business Process Discovery, (2) Business Process Supervision and (3) Business Process Assessment and Improvement. This paper illustrates the business discovery phase of BAM with a case study in a real organizational setting.
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Vulcu, Gabriela, Sami Bhiri, Wassim Derguech, and María José Ibáñez. "Semantically-enabled business process models discovery." International Journal of Business Process Integration and Management 5, no. 3 (2011): 257. http://dx.doi.org/10.1504/ijbpim.2011.042529.

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Effendi, Yutika Amelia, and Nania Nuzulita. "Process Discovery of Business Processes Using Temporal Causal Relation." Journal of Information Systems Engineering and Business Intelligence 5, no. 2 (October 24, 2019): 183. http://dx.doi.org/10.20473/jisebi.5.2.183-194.

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Background: Nowadays, enterprise computing manages business processes which has grown up rapidly. This situation triggers the production of a massive event log. One type of event log is double timestamp event log. The double timestamp has a start time and complete time of each activity executed in the business process. It also has a close relationship with temporal causal relation. The temporal causal relation is a pattern of event log that occurs from each activity performed in the process.Objective: In this paper, seven types of temporal causal relation between activities were presented as an extended version of relations used in the double timestamp event log. Since the event log was not always executed sequentially, therefore using temporal causal relation, the event log was divided into several small groups to determine the relations of activities and to mine the business process.Methods: In these experiments, the temporal causal relation based on time interval which were presented in Gantt chart also determined whether each case could be classified as sequential or parallel relations. Then to obtain the business process, each temporal causal relation was combined into one business process based on the timestamp of activity in the event log.Results: The experimental results, which were implemented in two real-life event logs, showed that using temporal causal relation and double timestamp event log could discover business process models.Conclusion: Considering the findings, this study concludes that business process models and their sequential and parallel AND, OR, XOR relations can be discovered by using temporal causal relation and double timestamp event log.Keywords:Business Process, Process Discovery, Process Mining, Temporal Causal Relation, Double Timestamp Event Log
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Lamghari, Zineb. "An Integrated Approach for Discovering Process Models According to Business Process Types." ASM Science Journal 16 (July 26, 2021): 1–14. http://dx.doi.org/10.32802/asmscj.2021.767.

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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.
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Rgibi, Ahmed Elajeli, Shu Zhen Yao, and Jia Jun Xu. "Dataflow Errors Detection in Business Process Model." Applied Mechanics and Materials 130-134 (October 2011): 1765–69. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.1765.

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Despite the abundance of analysis techniques to discover control-flow errors in workflow designs, there is hardly any support for data-flow verification. Most techniques simply abstract from data while data dependencies can be the source of all kinds of errors. This paper focuses on the discovery of data-flow errors in workflows. There are many issues in the dataflow, like Redundant Data, Lost Data, Missing Data, Mismatched data, Inconsistent data, and Misdirected data. We present an analysis approach that uses so called “The RWD Boolean Table Technique” expressed in steps, I Split dataflow from control flow; II Create Boolean table for each data element/s; III Make comparative between RWD Boolean table for current task and next task until get the end of workflow; Typical errors include dataflow issue, like redundant Data, lost Data and missing Data.
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Polpinij, Jantima, Aditya Ghose, and Hoa Khanh Dam. "Mining business rules from business process model repositories." Business Process Management Journal 21, no. 4 (July 6, 2015): 820–36. http://dx.doi.org/10.1108/bpmj-01-2014-0004.

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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.
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Popova, Viara, Dirk Fahland, and Marlon Dumas. "Artifact Lifecycle Discovery." International Journal of Cooperative Information Systems 24, no. 01 (March 2015): 1550001. http://dx.doi.org/10.1142/s021884301550001x.

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Artifact-centric modeling is an approach for capturing business processes in terms of so-called business artifacts — key entities driving a company's operations and whose lifecycles and interactions define an overall business process. This approach has been shown to be especially suitable in the context of processes where one-to-many or many-to-many relations exist between the entities involved in the process. As a contribution towards building up a body of methods to support artifact-centric modeling, this article presents a method for automated discovery of artifact-centric process models starting from logs consisting of flat collections of event records. We decompose the problem in such a way that a wide range of existing (non-artifact-centric) automated process discovery methods can be reused in a flexible manner. The presented methods are implemented as a package for ProM, a generic open-source framework for process mining. The methods have been applied to reverse-engineer an artifact-centric process model starting from logs of a real-life business process.
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Kalenkova, Anna, Andrea Burattin, Massimiliano de Leoni, Wil van der Aalst, and Alessandro Sperduti. "Discovering high-level BPMN process models from event data." Business Process Management Journal 25, no. 5 (September 2, 2019): 995–1019. http://dx.doi.org/10.1108/bpmj-02-2018-0051.

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Purpose The purpose of this paper is to demonstrate that process mining techniques can help to discover process models from event logs, using conventional high-level process modeling languages, such as Business Process Model and Notation (BPMN), leveraging their representational bias. Design/methodology/approach The integrated discovery approach presented in this work is aimed to mine: control, data and resource perspectives within one process diagram, and, if possible, construct a hierarchy of subprocesses improving the model readability. The proposed approach is defined as a sequence of steps, performed to discover a model, containing various perspectives and presenting a holistic view of a process. This approach was implemented within an open-source process mining framework called ProM and proved its applicability for the analysis of real-life event logs. Findings This paper shows that the proposed integrated approach can be applied to real-life event logs of information systems from different domains. The multi-perspective process diagrams obtained within the approach are of good quality and better than models discovered using a technique that does not consider hierarchy. Moreover, due to the decomposition methods applied, the proposed approach can deal with large event logs, which cannot be handled by methods that do not use decomposition. Originality/value The paper consolidates various process mining techniques, which were never integrated before and presents a novel approach for the discovery of multi-perspective hierarchical BPMN models. This approach bridges the gap between well-known process mining techniques and a wide range of BPMN-complaint tools.
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Dissertations / Theses on the topic "Business process discovery"

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Motahari, Nezhad Hamid Reza Computer Science &amp Engineering Faculty of Engineering UNSW. "Discovery and adaptation of process views." Publisher:University of New South Wales. Computer Science & Engineering, 2008. http://handle.unsw.edu.au/1959.4/41026.

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Business process analysis and integration are key endeavours for today's enterprises. Recently, Web services have been widely adopted for the implementation and integration of business processes within and across enterprises. In this dissertation, we investigate the problem of enabling the analysis of service interactions, in today's enterprises, in the context of business process executions, and that of service integration. Our study shows that only fraction of interactions in the enterprise are supported by process-aware systems. However, enabling above-mentioned analyses requires: (i) a model of the underlying business process to be used as a reference for the analysis, and (ii) the ability to correlate events generated during service interactions into process instances. We refer to a process model and the corresponding process instances as a "process view". We propose the concept of process space to refer to all process related information sources in the enterprise, over which various process views are defined. We propose the design and development of a system called "process space discovery system" (PSDS) for discovering process views in a process space. We introduce novel approaches for the correlation of events into process instances, focusing on the public processes of Web services (business protocols), and also for the discovery of the business protocol models from the process instances of a process view. Analysis of service integration approaches shows that while standardisation in Web services simplifies the integration in the communication level, at the higher levels of abstractions (e.g., services interfaces and protocol models) services are still open to heterogeneities. We characterise the mismatches between service interfaces and protocol specifications and introduce "mismatch patterns" to represent them. A mismatch pattern also includes an adapter template that aims at the resolution of the captured mismatch. We also propose semi-automated approaches for identifying the mismatches between interface and protocol specifications of two services. The proposed approaches have been implemented in prototype tools, and experimentally validated on synthetic and real-world datasets. The discovered process views, using PSDS, can be used to perform various analyses in an enterprise, and the proposed adaptation approach facilitates the adoption of Web services in business process integration.
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Aldin, Laden. "Semantic discovery and reuse of business process patterns." Thesis, Brunel University, 2010. http://bura.brunel.ac.uk/handle/2438/4635.

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In modern organisations business process modelling has become fundamental due to the increasing rate of organisational change. As a consequence, an organisation needs to continuously redesign its business processes on a regular basis. One major problem associated with the way business process modelling (BPM) is carried out today is the lack of explicit and systematic reuse of previously developed models. Enabling the reuse of previously modelled behaviour can have a beneficial impact on the quality and efficiency of the overall information systems development process and also improve the effectiveness of an organisation’s business processes. In related disciplines, like software engineering, patterns have emerged as a widely accepted architectural mechanism for reusing solutions. In business process modelling the use of patterns is quite limited apart from few sporadic attempts proposed by the literature. Thus, pattern-based BPM is not commonplace. Business process patterns should ideally be discovered from the empirical analysis of organisational processes. Empiricism is currently not the basis for the discovery of patterns for business process modelling and no systematic methodology for collecting and analysing process models of business organisations currently exists. The purpose of the presented research project is to develop a methodological framework for achieving reuse in BPM via the discovery and adoption of patterns. The framework is called Semantic Discovery and Reuse of Business Process Patterns (SDR). SDR provides a systematic method for identifying patterns among organisational data assets representing business behaviour. The framework adopts ontologies (i.e., formalised conceptual models of real-world domains) in order to facilitate such discovery. The research has also produced an ontology of business processes that provides the underlying semantic definitions of processes and their constituent parts. The use of ontologies to model business processes represents a novel approach and combines advances achieved by the Semantic Web and BPM communities. The methodological framework also relates to a new line of research in BPM on declarative business processes in which the models specify what should be done rather than how to ‘prescriptively’ do it. The research follows a design science method for designing and evaluating SDR. Evaluation is carried out using real world sources and reuse scenarios taken from both the financial and educational domains.
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Al, Jlailaty Diana. "Mining Business Process Information from Emails Logs for Process Models Discovery." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLED028.

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Les informations échangées dans les textes des courriels sont généralement concernées par des événements complexes ou des processus métier dans lesquels les entités qui échangent des courriels collaborent pour atteindre les objectifs finaux des processus. Ainsi, le flux d’informations dans les courriels envoyés et reçus constitue une partie essentielle, les activités métier de l’entreprise. L’extraction d’informations sur les processus métier à partir des courriels peut aider à améliorer la gestion des courriels pour les utilisateurs. Il peut également être utilisé pour trouver des réponses riches à plusieurs questions analytiques sur les employés et les organisations. Aucun des travaux précédents n’a résolu le problème de la transformation automatique des journaux de courriels en journaux d’événements pour éventuellement en déduire les processus métier non documentés. Dans ce but, nous travaillons dans cette thèse sur un framework qui induit des informations de processus métier à partir d’emails. Nous introduisons des approches qui contribuent à ce qui suit : (1) découvrir pour chaque courriel le sujet de processus qui le concerne, (2) découvrir l’instance de processus métier à laquelle appartient chaque courriel, (3) extraire les activités de processus métier des courriels et associer ces activités aux métadonnées qui les décrivent, (4) améliorer la performance de la découverte des instances de processus métier et des activités métier en utilisant la relation entre ces deux problèmes, et enfin (5) estimer au préalable la date/heure réelle d’un activité métier. En utilisant les résultats des approches mentionnées, un journal d’événements est généré qui peut être utilisé pour déduire les modèles de processus métier d’un journal de courriels. L’efficacité de toutes les approches ci-dessus est prouvée par l’application de plusieurs expériences sur l’ensemble de données de courriel ouvert d’Enron
Exchanged information in emails’ texts is usually concerned by complex events or business processes in which the entities exchanging emails are collaborating to achieve the processes’ final goals. Thus, the flow of information in the sent and received emails constitutes an essential part of such processes i.e. the tasks or the business activities. Extracting information about business processes from emails can help in enhancing the email management for users. It can be also used in finding rich answers for several analytical queries about the employees and the organizations enacting these business processes. None of the previous works have fully dealt with the problem of automatically transforming email logs into event logs to eventually deduce the undocumented business processes. Towards this aim, we work in this thesis on a framework that induces business process information from emails. We introduce approaches that contribute in the following: (1) discovering for each email the process topic it is concerned by, (2) finding out the business process instance that each email belongs to, (3) extracting business process activities from emails and associating these activities with metadata describing them, (4) improving the performance of business process instances discovery and business activities discovery from emails by making use of the relation between these two problems, and finally (5) preliminary estimating the real timestamp of a business process activity instead of using the email timestamp. Using the results of the mentioned approaches, an event log is generated which can be used for deducing the business process models of an email log. The efficiency of all of the above approaches is proven by applying several experiments on the open Enron email dataset
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Persson, Andreas, and Fredrik Jeppsson. "A Process and Enterprise Maturity Model (PEMM) Analysis of the Hampered Big Pharma Drug Discovery Process." Thesis, Blekinge Tekniska Högskola, Institutionen för industriell ekonomi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15368.

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The big pharma drug discovery process is currently hampered by long lead times, high costs, and fre-quent failures. On the other hand the general view is that many small pharmaceutical and biotech com-panies are more successful in terms of preclinical drug project transitions, and many small companies and/or their projects are bought by big pharma. In this thesis we investigated if the Business Process Reengineering (BPR) and the Process and Enterprise Maturity Model (PEMM) approaches by Hammer are suitable for the big pharma R&D process and if these approaches could shed any light on where potential improvements in the process can be made. A number of people with work experience from both big pharma and biotech preclinical R&D were also interviewed regarding the BPR and PEMM approaches and also regarding general organizational and pro-cess differences between biotech and big pharma. Our findings suggests that where suitable, most of the BPR principles have already been implemented in big pharma R&D, and that the PEMM approach at large, is suitable for the industry. Further, the inter-views revealed that there is a stronger focus on the core science and project work, and much less focus on processes, in biotech compared to big pharma. Despite a higher focus on control in terms of pro-cesses and metrics, the big pharma R&D process suffers, indicating a need to strengthen the process cul-ture if these processes should remain and add value to the organization.
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Mavaddat, Matin. "Business process discovery through conversation log analysis in pluralist and coercive problem contexts." Thesis, University of the West of England, Bristol, 2013. http://eprints.uwe.ac.uk/21925/.

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Business process discovery is one of the most fundamental steps of business process management (BPM) lifecycles. Incorrect, misleading or biased results of this stage can cause the whole BPM project to fail or the information systems that are created based on them to have great alignment problems with the reality of the organisation and how people carry out their work. The main problems of the business process discovery phase stem from two main sources. Firstly, the wrong attachment of BPM definitions and business process discovery techniques to the functionalist social paradigm whose only objective is the survival of the organisation through ensuring its efficiency and adaptability like a machine. This attachment to the functionalist paradigm has made BPM definitions to assume that organisations as social systems are in a unitary problem context, which means its constituents have similar beliefs and interests, they share common goals and objectives and they have all been involved in the decision-making. These assumptions are obviously far from the reality of today’s organisations which are normally either in pluralist or coercive problem contexts. The second source of problems in the business process discovery phase are BPM’s definitions and techniques over-reliance on human memory and cognition that has made them suffer, like any other knowledge acquisition technique, from human memory and cognition limitations. Using Design Science Research methodology, this research develops a conceptual framework in which new definitions for business task, business process and business process model in pluralist and coercive problem contexts will be presented. It will also be shown that conversation logs are a good source of information for business process discovery based on the new definitions and that using conversation logs can reduce the limitations caused by human memory and cognition. To develop the new conceptual framework, organisations as social systems have been analysed using the creative holism systems approach, and sound theories such as viable system model (VSM), i* framework, speech act theory, conversation for action diagrams and episodic memory have been leveraged. Based on the conceptual framework that consumes email messages as the conversation log and as its source of information, a method for business process discovery has been developed. Using two case studies it has been demonstrated that the proposed definitions and the developed methods are applicable in unitary, pluralist and coercive problem contexts; and taking advantage of the conversation logs as their information source, they suffer to a lesser extent from human memory and cognition limitations. As a consequence, the resulting business process models created from applying the proposed definitions and methods are closer to the realities of the organisations and can increase the success rate of the business process management projects and reduce the information system’s alignment problems.
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Sharma, Sumana. "An Integrated Knowledge Discovery and Data Mining Process Model." VCU Scholars Compass, 2008. http://scholarscompass.vcu.edu/etd/1615.

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Enterprise decision making is continuously transforming in the wake of ever increasing amounts of data. Organizations are collecting massive amounts of data in their quest for knowledge nuggets in form of novel, interesting, understandable patterns that underlie these data. The search for knowledge is a multi-step process comprising of various phases including development of domain (business) understanding, data understanding, data preparation, modeling, evaluation and ultimately, the deployment of the discovered knowledge. These phases are represented in form of Knowledge Discovery and Data Mining (KDDM) Process Models that are meant to provide explicit support towards execution of the complex and iterative knowledge discovery process. Review of existing KDDM process models reveals that they have certain limitations (fragmented design, only a checklist-type description of tasks, lack of support towards execution of tasks, especially those of the business understanding phase etc) which are likely to affect the efficiency and effectiveness with which KDDM projects are currently carried out. This dissertation addresses the various identified limitations of existing KDDM process models through an improved model (named the Integrated Knowledge Discovery and Data Mining Process Model) which presents an integrated view of the KDDM process and provides explicit support towards execution of each one of the tasks outlined in the model. We also evaluate the effectiveness and efficiency offered by the IKDDM model against CRISP-DM, a leading KDDM process model, in aiding data mining users to execute various tasks of the KDDM process. Results of statistical tests indicate that the IKDDM model outperforms the CRISP model in terms of efficiency and effectiveness; the IKDDM model also outperforms CRISP in terms of quality of the process model itself.
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Reguieg, Hicham. "Using MapReduce to scale event correlation discovery for process mining." Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2014. http://tel.archives-ouvertes.fr/tel-01002623.

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The volume of data related to business process execution is increasing significantly in the enterprise. Many of data sources include events related to the execution of the same processes in various systems or applications. Event correlation is the task of analyzing a repository of event logs in order to find out the set of events that belong to the same business process execution instance. This is a key step in the discovery of business processes from event execution logs. Event correlation is a computationally-intensive task in the sense that it requires a deep analysis of very large and growing repositories of event logs, and exploration of various possible relationships among the events. In this dissertation, we present a scalable data analysis technique to support efficient event correlation for mining business processes. We propose a two-stages approach to compute correlation conditions and their entailed process instances from event logs using MapReduce framework. The experimental results show that the algorithm scales well to large datasets.
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Gonella, Philippe. "Business Process Management and Process Mining within a Real Business Environment: An Empirical Analysis of Event Logs Data in a Consulting Project." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/11799/.

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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 tecnologie che abilitano l’utilizzo della disciplina sono descritti, cosi come le tre tecniche che questa abilita: process discovery, conformance checking e process enhancement. Il process mining è stato utilizzato come strumento principale in un progetto di consulenza da HSPI S.p.A. per conto di un importante cliente italiano, fornitore di piattaforme e di soluzioni IT. Il progetto a cui ho preso parte, descritto all’interno dell’elaborato, ha come scopo quello di sostenere l’organizzazione nel suo piano di improvement delle prestazioni interne e ha permesso di verificare l’applicabilità e i limiti delle tecniche di process mining. Infine, nell’appendice finale, è presente un paper da me realizzato, che raccoglie tutte le applicazioni della disciplina in un contesto di business reale, traendo dati e informazioni da working papers, casi aziendali e da canali diretti. Per la sua validità e completezza, questo documento è stata pubblicato nel sito dell'IEEE Task Force on Process Mining.
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Namaki, Araghi Sina. "A methodology for business process discovery and diagnosis based on indoor location data : Application to patient pathways improvement." Thesis, Ecole nationale des Mines d'Albi-Carmaux, 2019. http://www.theses.fr/2019EMAC0014.

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Dans chaque organisation, les processus métier sont aujourd’hui incontournables. Cette thèse vise à développer une méthode pour les améliorer. Dans le domaine de la santé, les organisations hospitalières déploient beaucoup d’efforts pour mettre leurs processus sous contrôle, notamment à cause de la très faible marge d’erreur admise. Les parcours des patients au sein des structures de santé constituent l’application qui a été choisie pour démontrer les apports de cette méthode. Elle a pour originalité d’exploiter les données de géolocalisation des patients à l’intérieur de ces structures. Baptisée DIAG, elle améliore les parcours de soins grâce à plusieurs sous-fonctions : (i) interpréter les données de géolocalisation pour la modélisation de processus, (ii) découvrir automatiquement les processus métier, (iii) évaluer la qualité et la performance des parcours et (iv) diagnostiquer automatiquement les problèmes de performance des processus. Cette thèse propose donc les contributions suivantes : la méthode DIAG elle-même qui, grâce à quatre différents états, extrait les informations des données de géolocalisation ; le méta-modèle DIAG qui a deux utilités : d’une part, interpréter les données de géolocalisation et donc passer des données brutes aux informations utilisables, et, d’autre part contribuer à vérifier l’alignement des données avec le domaine grâce à deux méthodes de diagnostic décrites plus bas ; deux algorithmes de découverte de processus qui utilisent la stabilité statistique des logs d’évènements ; une nouvelle approche de process mining utilisant SPC (Statistical Process Control) pour l’amélioration ; l’algorithme proDIST qui mesure les distances entre les modèles de processus ; deux méthodes de diagnostic automatique de processus pour détecter les causes des déviations structurelles dans des cas individuels et pour des processus communs. Le contexte de cette thèse confirme la nécessité de proposer de telles solutions. Une étude de cas dans le cadre de ce travail de recherche illustre l’applicabilité de la méthodologie DIAG et des fonctions et méthodes mentionnées
Business processes are everywhere and, as such, we must acknowledge them. Among all of them, hospital processes are of vital importance. Healthcare organizations invest huge amount of efforts into keeping these processes under control, as the allowed margin of error is so slight. This research work seeks to develop a methodology to endorse improvement of patient pathways inside healthcare organizations. It does so by using the indoor location data of patients. This methodology is called DIAG (Data state, Information state, Awareness, Governance). It is constructed of several different functions. The most important ones are as follows: (i) location data interpreting, (ii) automatic discovery of business process models, (iii) business process analyzing for evaluating the performance and quality of processes, and finally, (iv) automatic diagnosing of business processes. Along the former functions, the contribution of this thesis are: The DIAG methodology which, through four different states, extracts knowledge from location data; the DIAG meta-model which supports both the interpretation of location data (from raw data to usable information) and the alignment of the domain knowledge (which are used for the diagnosing methods); two process discovery algorithms which explore statistical stability in event logs, application of Statistical Process Control (SPC) for the “enhancement notation” of Process Mining; the ProDIST algorithm for measuring the distance between process models; two automatic process diagnosing methods to detect causes of structural deviations in individual cases and common processes. The state of the art in this dissertation endorses the necessity for proposing such solutions. A case study within this research work illustrates the applicability of the DIAG methodology and its mentioned functions and methods
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Rezende, Caio Appelt. "Arcabouço de classificação e escolha de algoritmos de descoberta de processos." Universidade Federal de Goiás, 2017. http://repositorio.bc.ufg.br/tede/handle/tede/7607.

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Process Mining is a recent area of research and is composed of techniques that allow the analysis and extraction of knowledge from the logs of the business processes obtained from Management Information Systems (MIS). The analyzes can be classified into three types: Process Discovery, Conformance Check and Process Improvement. With the current growth not only of quantity, but also of the types of algorithms that seek to fulfill the objectives of Process Mining, a classification that takes into account the performance of the algorithm in the various real situations of its application becomes important. The Evaluation and Comparison of the algorithms from the repository data could be done through the application of Quality Metrics or Machine Learning Techniques. This work presents a proposal of a set of Quality Metrics to allow the classification, evaluation and comparison of Process Discovery algorithms. The proposal is based on the review of algorithms and their families; the possible performance characteristics, that can be applied to any type of algorithm being tested; and in simulations of business process patterns. The results obtained by the work are promising in the sense of creating the conceptual basis and a methodology for future research to allow the construction of a framework for Evaluation and Comparison of new algorithms.
A Mineração de Processos (Process Mining) é uma área de pesquisa recente e é composta por técnicas que permitem a análise e a extração de conhecimento a partir dos registros de eventos (logs) dos processos de negócios obtidos de Sistemas de Informação Gerenciais (SIG). As análises podem ser classificadas em três tipos: Descoberta de Processos, Checagem da Conformidade e Melhoria de Processos. Com o atual crescimento não apenas da quantidade, mas também dos tipos de algoritmos que procuram cumprir os objetivos da Mineração de Processos, uma classificação que leve em consideração a performance do algoritmo nas diversas situações reais de sua aplicação se torna importante. A Avaliação e a Comparação dos algoritmos a partir dos dados do repositório poderiam ser feitas através da aplicação de Métricas de Qualidade ou Técnicas de Aprendizado de Máquina. Este trabalho apresenta uma proposta de um conjunto de Métricas de Qualidade que tem como objetivo permitir a classificação, avaliação e comparação de algoritmos de Descoberta de Processos. A proposta foi construída com base na revisão dos algoritmos e suas famílias; no levantamento das possíveis características de performance, que podem ser aplicadas a qualquer tipo de algoritmo sendo testado; e em simulações de registros de eventos de padrões de processos de negócio. Os resultados obtidos pelo trabalho são promissores no sentido de criar a base conceitual e uma metodologia para que futuras pesquisas permitam a construção de um arcabouço (framework) de Avaliação e Comparação de novos algoritmos.
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Books on the topic "Business process discovery"

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service), SpringerLink (Online, ed. Process Mining: Discovery, Conformance and Enhancement of Business Processes. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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Knowledge discovery process and methods to enhance organizational performance. Boca Raton, FL: CRC Press, Taylor & Francis Group, 2015.

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Ernesto, Damiani, Dillon Tharam S. 1943-, and SpringerLink (Online service), eds. Data-Driven Process Discovery and Analysis: First International Symposium, SIMPDA 2011, Campione d’Italia, Italy, June 29 – July 1, 2011, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Blokdyk, Gerardus. Business process discovery: Second Edition. CreateSpace Independent Publishing Platform, 2018.

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Wil M. P. van der Aalst. Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, 2011.

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Kő, Andrea, and András Gábor. Corporate Knowledge Discovery and Organizational Learning: The Role, Importance, and Application of Semantic Business Process Management. Springer, 2016.

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Kő, Andrea, and András Gábor. Corporate Knowledge Discovery and Organizational Learning: The Role, Importance, and Application of Semantic Business Process Management. Springer, 2018.

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Russo, Barbara, Rafael Accorsi, and Paolo Ceravolo. Data-Driven Process Discovery and Analysis: 4th International Symposium, SIMPDA 2014, Milan, Italy, November 19-21, 2014, Revised Selected Papers. Springer, 2016.

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Ceravolo, Paolo, Maurice van Keulen, and María Teresa Gómez-López. Data-Driven Process Discovery and Analysis: 8th IFIP WG 2.6 International Symposium, SIMPDA 2018, Seville, Spain, December 13–14, 2018, and 9th ... in Business Information Processing ). Springer, 2020.

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Rinderle-Ma, Stefanie, and Paolo Ceravolo. Data-Driven Process Discovery and Analysis: 5th IFIP WG 2.6 International Symposium, SIMPDA 2015, Vienna, Austria, December 9-11, 2015, Revised ... Notes in Business Information Processing). Springer, 2017.

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Book chapters on the topic "Business process discovery"

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Dumas, Marlon, Marcello La Rosa, Jan Mendling, and Hajo A. Reijers. "Process Discovery." In Fundamentals of Business Process Management, 155–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33143-5_5.

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Dumas, Marlon, Marcello La Rosa, Jan Mendling, and Hajo A. Reijers. "Process Discovery." In Fundamentals of Business Process Management, 159–212. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-56509-4_5.

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Leemans, Sander J. J. "Process Discovery and Exploration." In Business Process Management Workshops, 582–85. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15895-2_52.

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Fani Sani, Mohammadreza, Wil van der Aalst, Alfredo Bolt, and Javier García-Algarra. "Subgroup Discovery in Process Mining." In Business Information Systems, 237–52. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59336-4_17.

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Martin, Trevor, and Hongmei He. "Bisociative Discovery in Business Process Models." In Bisociative Knowledge Discovery, 452–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31830-6_32.

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Vulcu, Gabriela, Wassim Derguech, and Sami Bhiri. "Business Process Model Discovery Using Semantics." In Business Process Management Workshops, 326–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20511-8_31.

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Yzquierdo-Herrera, Raykenler, Rogelio Silverio-Castro, and Manuel Lazo-Cortés. "Sub-process Discovery: Opportunities for Process Diagnostics." In Lecture Notes in Business Information Processing, 48–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36611-6_4.

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Leemans, Sander J. J., Dirk Fahland, and Wil M. P. van der Aalst. "Using Life Cycle Information in Process Discovery." In Business Process Management Workshops, 204–17. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42887-1_17.

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De Weerdt, Jochen, Seppe K. L. M. vanden Broucke, and Filip Caron. "Bidimensional Process Discovery for Mining BPMN Models." In Business Process Management Workshops, 529–40. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15895-2_45.

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Ferreira, Diogo R., Susana Alves, and Lucinéia H. Thom. "Ontology-Based Discovery of Workflow Activity Patterns." In Business Process Management Workshops, 314–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28115-0_30.

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Conference papers on the topic "Business process discovery"

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Ibáñez, M. J., G. Vulcu, J. Ezpeleta, and S. Bhiri. "Semantically enabled business process discovery." In the 2010 ACM Symposium. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1774088.1774385.

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Kudo, Michiharu, Ai Ishida, and Naoto Sato. "Business Process Discovery by Using Process Skeletonization." In 2013 International Conference on Signal-Image Technology & Internet-Based Systems (SITIS). IEEE, 2013. http://dx.doi.org/10.1109/sitis.2013.158.

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"PROCESS ORIENTED DISCOVERY OF BUSINESS PARTNERS." In 7th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2005. http://dx.doi.org/10.5220/0002527300570064.

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Zineb, Lamghari, Saidi Rajaa, Radgui Maryam, and Rahmani Moulay Driss. "Guided Process Discovery Approach According to Business Process Types." In International Conference on Advanced Technologies for Humanity. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0010426700190026.

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Sikal, Rabab, Hanae Sbai, and Laila Kjiri. "Promoting resource discovery in business process variability." In the 2nd International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3320326.3320380.

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Jlailaty, Diana, Daniela Grigori, and Khalid Belhajjame. "Business Process Instances Discovery from Email Logs." In 2017 IEEE International Conference on Services Computing (SCC). IEEE, 2017. http://dx.doi.org/10.1109/scc.2017.12.

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Polpinij, Jantima, Aditya K. Ghose, and Hoa Khanh Dam. "Business Rules Discovery from Process Design Repositories." In 2010 IEEE Congress on Services (SERVICES). IEEE, 2010. http://dx.doi.org/10.1109/services.2010.73.

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Yano, Keisuke, Yoshihide Nomura, and Tsuyoshi Kanai. "A Practical Approach to Automated Business Process Discovery." In 2013 17th IEEE International Enterprise Distributed Object Computing Conference Workshops (EDOCW). IEEE, 2013. http://dx.doi.org/10.1109/edocw.2013.13.

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Ghazal, Mohamed, Samy Ghoniemy, and Mostafa Salama. "Multi-Objective Optimization for Automated Business Process Discovery." In 11th International Conference on Knowledge Discovery and Information Retrieval. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0008072400890104.

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Mello, Pedro O. T., Kate Revoredo, and Flávia Santoro. "Business Process Failure Prediction: a case study." In VII Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/kdmile.2019.8793.

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Business process monitoring aims at maintaining the reliability of process executions. However, the dynamic nature of business processes hinders a proactive scenario in which risk mitigation actions can occur before the facts that put the process at risk. Thus, some premises are necessary such as the identification of situations and patterns in historical data of the processes execution in order to characterize what determined the failures. In this paper, we address the problem of how to identify and detect patterns of behaviors that can lead the processes to a failure situation. As a solution, a combination of well-established techniques from Data and Process Mining fields are applied in a case study of an incident management process. The results obtained open possibilities to a proactive scenario.
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Reports on the topic "Business process discovery"

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Marshak, Ronni. Using Business Process Management to Streamline Litigation Discovery. Boston, MA: Patricia Seybold Group, May 2007. http://dx.doi.org/10.1571/i05-24-07cc.

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