Journal articles: 'Business Process Mining'

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Pourmasoumi, Asef, and Ebrahim Bagheri. "Business process mining." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (March 2017): 1630004. http://dx.doi.org/10.1142/s2425038416300044.

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One of the most valuable assets of an organization is its organizational data. The analysis and mining of this potential hidden treasure can lead to much added-value for the organization. Process mining is an emerging area that can be useful in helping organizations understand the status quo, check for compliance and plan for improving their processes. The aim of process mining is to extract knowledge from event logs of today’s organizational information systems. Process mining includes three main types: discovering process models from event logs, conformance checking and organizational mining. In this paper, we briefly introduce process mining and review some of its most important techniques. Also, we investigate some of the applications of process mining in industry and present some of the most important challenges that are faced in this area.

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van der Aalst, Wil. "Spreadsheets for business process management." Business Process Management Journal 24, no. 1 (February 2, 2018): 105–27. http://dx.doi.org/10.1108/bpmj-10-2016-0190.

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Purpose Process mining provides a generic collection of techniques to turn event data into valuable insights, improvement ideas, predictions, and recommendations. This paper uses spreadsheets as a metaphor to introduce process mining as an essential tool for data scientists and business analysts. The purpose of this paper is to illustrate that process mining can do with events what spreadsheets can do with numbers. Design/methodology/approach The paper discusses the main concepts in both spreadsheets and process mining. Using a concrete data set as a running example, the different types of process mining are explained. Where spreadsheets work with numbers, process mining starts from event data with the aim to analyze processes. Findings Differences and commonalities between spreadsheets and process mining are described. Unlike process mining tools like ProM, spreadsheets programs cannot be used to discover processes, check compliance, analyze bottlenecks, animate event data, and provide operational process support. Pointers to existing process mining tools and their functionality are given. Practical implications Event logs and operational processes can be found everywhere and process mining techniques are not limited to specific application domains. Comparable to spreadsheet software widely used in finance, production, sales, education, and sports, process mining software can be used in a broad range of organizations. Originality/value The paper provides an original view on process mining by relating it to the spreadsheets. The value of spreadsheet-like technology tailored toward the analysis of behavior rather than numbers is illustrated by the over 20 commercial process mining tools available today and the growing adoption in a variety of application domains.

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Ingvaldsen, Jon Espen, and Jon Atle Gulla. "Model-Based Business Process Mining." Information Systems Management 23, no. 1 (December 2006): 19–31. http://dx.doi.org/10.1201/1078.10580530/45769.23.1.20061201/91769.3.

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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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Vasiliev, A. A., and A. V. Goryachev. "Applying Process Mining to Process Management." LETI Transactions on Electrical Engineering & Computer Science 16, no. 3 (2023): 52–59. http://dx.doi.org/10.32603/2071-8985-2023-16-3-52-59.

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Deals with the intellectual analysis of processes (Process Mining), which has recently gained popularity in various organizations. It is based on the construction of business process models in a specific area (for example, in the field of project management) based on event logs, providing a more accurate understanding of the actions occurring in business processes for the purpose of their subsequent analysis and improvement. The article defines process mining, event logs, lists the main tasks, algorithms and view models. The authors propose a methodology that can be used in the application of process analysis in the field of project management. The authors also highlight the main business processes in project management, for which it is advisable to build models and analyze them.

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van der Aalst, W. M. P., H. A. Reijers, A. J. M. M. Weijters, B. F. van Dongen, A. K. Alves de Medeiros, M. Song, and H. M. W. Verbeek. "Business process mining: An industrial application." Information Systems 32, no. 5 (July 2007): 713–32. http://dx.doi.org/10.1016/j.is.2006.05.003.

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Badakhshan, Peyman, Bastian Wurm, Thomas Grisold, Jerome Geyer-Klingeberg, Jan Mendling, and Jan vom Brocke. "Creating business value with process mining." Journal of Strategic Information Systems 31, no. 4 (December 2022): 101745. http://dx.doi.org/10.1016/j.jsis.2022.101745.

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Er, Mahendrawathi, Hanim Maria Astuti, and Dita Pramitasari. "Modeling and Analysis of Incoming Raw Materials Business Process: A Process Mining Approach." International Journal of Computer and Communication Engineering 4, no. 3 (2015): 196–203. http://dx.doi.org/10.17706/ijcce.2015.4.3.196-203.

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Park, Sungbum, and Young Sik Kang. "A Study of Process Mining-based Business Process Innovation." Procedia Computer Science 91 (2016): 734–43. http://dx.doi.org/10.1016/j.procs.2016.07.066.

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Chubukova, Ponomarenko, and Nedbailo. "Using data mining to process business data." Problems of Innovation and Investment Development, no. 23 (April 10, 2020): 71–77. http://dx.doi.org/10.33813/2224-1213.23.2020.8.

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The subject of the research is the approach to the possibility of applying data mining methods in the framework of business analytics in order to optimize the adoption of management decisions by the company.The purpose of writing this article is to study of data mining methods features use of primary data, which act as a valuable resource of the company, which can be used to ensure competitive- ness in a particular market. Methodology. The research methodology is system- structural and comparative analyzes (to study the approaches of data mining data for the complex analysis of first data); monograph (studying the preconditions for the growth of data mining companies’ use in the process of data research); eco- nomic analysis (when assessing the feasibility of using machine learning methods to ensure the goals of business intelligence). The scientific novelty consists the peculiarities of data mining application as one of the directions of business analyt- ics are determined, which makes it possible to determine implicit relationships between known factor and result characteristics on the basis of primary data. The main directions of data manipulation are revealed: classification and forecasting, as well as correlation-regression analysis. The importance of using the basic meth- ods of statistical analysis in the process of studying primary data is proved. The specifics of the use of neural networks as one of the most important methods of machine learning are given. Conclusions. The use of data mining allows companies to increase the efficiency of the use of available data and optimize development strategies accordingly. The presence of a large number of machine learning meth- ods and statistical approaches expands the possibilities of comprehensive data analysis. Innovative technologies and specialized programming languages play an important role in this case.

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Li, Chen, Manfred Reichert, and Andreas Wombacher. "Mining business process variants: Challenges, scenarios, algorithms." Data & Knowledge Engineering 70, no. 5 (May 2011): 409–34. http://dx.doi.org/10.1016/j.datak.2011.01.005.

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Stürmer, Christian. "Mit Business Process Mining die Prozessqualität optimieren." maschinenbau 3, no. 3 (June 2023): 44–47. http://dx.doi.org/10.1007/s44029-023-0788-6.

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Li, Jiexun, Harry Jiannan Wang, Zhu Zhang, and J. Leon Zhao. "A policy-based process mining framework: mining business policy texts for discovering process models." Information Systems and e-Business Management 8, no. 2 (April 11, 2009): 169–88. http://dx.doi.org/10.1007/s10257-009-0112-x.

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Sucharittham, Nanthawadee, Choochart Haruechaiyasak, Hieu Chi Dam, and Thanaruk Theeramunkong. "Multidimensional Sentiment Cube Mining for Process Monitoring." Trends in Sciences 19, no. 9 (April 9, 2022): 3682. http://dx.doi.org/10.48048/tis.2022.3682.

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Process monitoring is essential for quality improvement because it is necessary to find the answers to which business issues need to be understood. In the era of social media, many critiques concern the business domain, including life insurance, which is one of the significant business sectors in Thailand. To utilize this useful cloud corpus for the business improvement process, we propose a novel methodology for process monitoring using the concept of multidimensional sentiment cube (MDSC) mining to raise usefulness with the business process model notation (BPMN). As the ability of MDC raise unlimited analysis perspectives merge with sentiment analysis (MDSC), this method can provide more sets of data for association rules mining and meet the needs to be analyzed. The cube analysis scenario, which uses association rules mining results, can reveal a significant hidden issue among aspects and sub-aspects associated under our design with their measurements. The results can be used for monitoring, which presents the customer's sentiment from social media in the real business case and identifying in the real process model. HIGHLIGHTS A methodology for process monitoring using the concept of multidimensional sentiment cube (MDSC) mining raise the usefulness of the business process model notation (BPMN) by utilizing the corpus for the business improvement process This method can provide more sets of data for association rules mining as the ability of MDC raise unlimited analysis perspectives to merge with sentiment analysis (MDSC) The results can monitor the customer’s sentiment from social media in the real business case and identify in the real process model GRAPHICAL ABSTRACT

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Zakurdaeva, Zh E., and M. V. Bikeeva. "PROCESS MINING: PRINCIPLES, CHARACTERISTICS AND IMPLEMENTATION POTENTIAL." Social’no-ekonomiceskoe upravlenie: teoria i praktika 17, no. 3 (October 5, 2021): 34–40. http://dx.doi.org/10.22213/2618-9763-2021-3-34-40.

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Digital transformation is forcing companies to rethink their processes to meet current customer needs. Business Process Management (BPM) can provide a means to structure and address this change. However, most BPM approaches face limitations on the number of processes they can optimize at the same time, due to complexity and resource constraints. The article is devoted to data mining as a tool for modeling and improving the company's business processes. Process Mining is a collection of data-driven diagnostic and business process improvement methods that combine machine learning and BPM. Among the advantages of Process Mining is more efficient management decision making. The possibility of introducing Process Mining methods into the work of a telecommunications company for the automatic collection of information about business processes and building a map of business processes is analyzed. The use of Process Mining methods will allow a telecommunications company to optimize the work of its departments and increase customer satisfaction. In addition, the implementation of this system contributes to a better analysis of the results of the execution of business processes for providing access to the Internet. This will improve the regulations for these processes, control of their compliance and the procedure for making managerial decisions at a higher quality level.

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Demushkina, K. M., and A. V. Kuzmin. "ANALYSIS OF THE CAPABILITIES OF TOOLS FOR IMPLEMENTING PROCESS MINING TECHNOLOGY." Izvestiya of Samara Scientific Center of the Russian Academy of Sciences 25, no. 4 (2023): 114–20. http://dx.doi.org/10.37313/1990-5378-2023-25-4-114-120.

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Modern realities dictate the requirements for effective optimization of production. Modernization of business, economics, production, healthcare and medicine in general is an urgent task of our time. One of the technologies for effective process analysis is process mining. Process mining technology allows you to extract knowledge from an event log, forming a process model based on it. Such tools make it possible to identify ineffective management structures and business processes, malfunctions in software, and incompetence of employees, solving many problems of organizing effective management in various industries. Today there are many software tools that implement process analysis methods: Disco, Apromore, ProM. Celonis Process Mining, UiPath Process Mining, etc. Selecting software for certain tasks is a complex process, which subsequently affects the implementation of the project as a whole. Therefore, it is extremely important to comprehensively analyze all the capabilities and limitations of existing process mining tools. The following criteria were selected: cost of the framework; availability of source code; programming language; the ability to build a decision tree based on the process; technical support; technical documentation; developed community of developers; the ability to create and add plugins; experience with the framework. Healthcare was chosen as the target application area. According to the results of the analysis, it was concluded that the use of the ProM framework is effective for solving problems in the field of healthcare.

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Abdelaal, Samah Ibrahim. "Business Process Management and Process Mining Technologies: The progress of a discipline." American Journal of Business and Operations Research 10, no. 1 (2023): 53–65. http://dx.doi.org/10.54216/ajbor.100105.

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A wide variety of approaches, strategies, and tools for designing, implementing, managing, and analyzing functional business processes have emerged from studies in business process management (BPM). It is the goal of the emerging topic of research known as process mining (PM) to improve the analysis of business process models by gleaning actionable insights from massive quantities of event logs. The purpose of this study is to research business process management and process mining by surveying the state-of-the-art methods and tools in each area and highlighting the most recent developments. This study concludes with a discussion of BPM and PM, in which PM acts as a bridge between BPM and data science to enhance business processes (BPs).

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Pooja , Dr. Manish Varshney. "A Study of Algorithms, Systems, and Applications of Multi-Agent Systems for Distributed Data Mining." Tuijin Jishu/Journal of Propulsion Technology 44, no. 4 (November 4, 2023): 3963–65. http://dx.doi.org/10.52783/tjjpt.v44.i4.1591.

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Data mining is a powerful technology that converts data into competitive intelligence which businesses can use to proactively predict future trends, uncover meanings to historical happenings and discover business imperatives which was hitherto unknown to business. Data mining is a “process of discovering and interpreting previously unknown patterns in data to aid business in better decision-making”. Data mining is a set of automated techniques used to extract buried or previously unknown pieces of information from large databases. Data mining by nature is an iterative process and gets refined for further probing into data in a continuous manner. From a data management point of view, the data mining process requires exploration of data, creating the analytic data sets for evaluation, generating patterns and forecasting models.

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Arias, Michael, Rodrigo Saavedra, Maira R. Marques, Jorge Munoz-Gama, and Marcos Sepúlveda. "Human resource allocation in business process management and process mining." Management Decision 56, no. 2 (February 12, 2018): 376–405. http://dx.doi.org/10.1108/md-05-2017-0476.

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Purpose Human resource allocation is considered a relevant problem in business process management (BPM). The successful allocation of available resources for the execution of process activities can impact on process performance, reduce costs and obtain a better productivity of the resources. In particular, process mining is an emerging discipline that allows improvement of the resource allocation based on the analysis of historical data. The purpose of this paper is to provide a broad review of primary studies published in the research area of human resource allocation in BPM and process mining. Design/methodology/approach A systematic mapping study (SMS) was conducted in order to classify the proposed approaches to allocate human resources. A total of 2,370 studies published between January 2005 and July 2016 were identified. Through a selection protocol, a group of 95 studies were selected. Findings Human resource allocation is an emerging research area that has been evolving over time, generating new proposals that are increasingly applied to real case studies. The majority of proposed approaches relate to the period 2011-2016. Journals and conference proceedings are the most common venues. Validation research and evaluation research are the most common research types. There are two main evaluation methods: simulation and case studies. Originality/value This study aims to provide an initial assessment of the state of the art in the research area of human resource allocation in BPM and process mining. To the best of the authors’ knowledge, this is the first research that has been conducted to date that generates a SMS in this research area.

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Outmazgin, Nesi, and Pnina Soffer. "A process mining-based analysis of business process work-arounds." Software & Systems Modeling 15, no. 2 (June 18, 2014): 309–23. http://dx.doi.org/10.1007/s10270-014-0420-6.

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Al-Absi, Mohammed Abdulhakim, and Hind R’bigui. "Process Discovery Techniques Recommendation Framework." Electronics 12, no. 14 (July 17, 2023): 3108. http://dx.doi.org/10.3390/electronics12143108.

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In a competitive environment, organizations need to continuously understand, analyze and improve the behavior of processes to maintain their position in the market. Process mining is a set of techniques that allows organizations to have an X-ray view of their processes by extracting process related knowledge from the information recorded in today’s process aware information systems such as ‘Enterprise Resource Planning’ systems, ‘Business Process Management’ systems, ‘Supply Chain Management’ systems, etc. One of the major categories of process mining techniques is the process of discovery. This later allows for automatically constructing process models just from the information stored in the system representing the real behavior of the process discovered. Many process discovery algorithms have been proposed today which made users and businesses, in front of many techniques, unable to choose or decide the appropriate mining algorithm for their business processes. Moreover, existing evaluation and recommendation frameworks have several important drawbacks. This paper proposes a new framework for recommending the most suitable process discovery technique to a given process taking into consideration the limitations of existing frameworks.

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Spruit, Marco, Marcin Kais, and Vincent Menger. "Automated Business Goal Extraction from E-mail Repositories to Bootstrap Business Understanding." Future Internet 13, no. 10 (September 23, 2021): 243. http://dx.doi.org/10.3390/fi13100243.

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The Cross-Industry Standard Process for Data Mining (CRISP-DM), despite being the most popular data mining process for more than two decades, is known to leave those organizations lacking operational data mining experience puzzled and unable to start their data mining projects. This is especially apparent in the first phase of Business Understanding, at the conclusion of which, the data mining goals of the project at hand should be specified, which arguably requires at least a conceptual understanding of the knowledge discovery process. We propose to bridge this knowledge gap from a Data Science perspective by applying Natural Language Processing techniques (NLP) to the organizations’ e-mail exchange repositories to extract explicitly stated business goals from the conversations, thus bootstrapping the Business Understanding phase of CRISP-DM. Our NLP-Automated Method for Business Understanding (NAMBU) generates a list of business goals which can subsequently be used for further specification of data mining goals. The validation of the results on the basis of comparison to the results of manual business goal extraction from the Enron corpus demonstrates the usefulness of our NAMBU method when applied to large datasets.

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Kopčeková, Alena, Michal Kopček, and Pavol Tanuška. "BUSINESS INTELLIGENCE IN PROCESS CONTROL." Research Papers Faculty of Materials Science and Technology Slovak University of Technology 21, no. 33 (December 1, 2013): 43–53. http://dx.doi.org/10.2478/rput-2013-0039.

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Abstract The Business Intelligence technology, which represents a strong tool not only for decision making support, but also has a big potential in other fields of application, is discussed in this paper. Necessary fundamental definitions are offered and explained to better understand the basic principles and the role of this technology for company management. Article is logically divided into five main parts. In the first part, there is the definition of the technology and the list of main advantages. In the second part, an overview of the system architecture with the brief description of separate building blocks is presented. Also, the hierarchical nature of the system architecture is shown. The technology life cycle consisting of four steps, which are mutually interconnected into a ring, is described in the third part. In the fourth part, analytical methods incorporated in the online analytical processing and data mining used within the business intelligence as well as the related data mining methodologies are summarised. Also, some typical applications of the above-mentioned particular methods are introduced. In the final part, a proposal of the knowledge discovery system for hierarchical process control is outlined. The focus of this paper is to provide a comprehensive view and to familiarize the reader with the Business Intelligence technology and its utilisation.

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Meddah, Ishak H. A., Khaled Belkadi, and Mohamed Amine Boudia. "Parallel Mining Small Patterns from Business Process Traces." International Journal of Software Science and Computational Intelligence 8, no. 1 (January 2016): 32–45. http://dx.doi.org/10.4018/ijssci.2016010103.

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Hadoop MapReduce has arrived to solve the problem of treatment of big data, also the parallel treatment, with this framework the authors analyze, process a large size of data. It based for distributing the work in two big steps, the map and the reduce steps in a cluster or big set of machines. They apply the MapReduce framework to solve some problems in the domain of process mining how provides a bridge between data mining and business process analysis, this technique consists to mine lot of information from the process traces; In process mining, there are two steps, correlation definition and the process inference. The work consists in first time of mining patterns whom are the work flow of the process from execution traces, those patterns present the work or the history of each party of the process, the authors' small patterns are represented in this work by finite state automaton or their regular expression, the authors have only two patterns to facilitate the process, the general presentation of the process is the combination of the small mining patterns. The patterns are represented by the regular expressions (ab)* and (ab*c)*. Secondly, they compute the patterns, and combine them using the Hadoop MapReduce framework, in this work they have two general steps, first the Map step, they mine small patterns or small models from business process, and the second is the combination of models as reduce step. The authors use the business process of two web applications, the SKYPE, and VIBER applications. The general result shown that the parallel distributed process by using the Hadoop MapReduce framework is scalable, and minimizes the execution time.

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Wu, He, Wang, Wen, and Yu. "A Business Process Analysis Methodology Based on Process Mining for Complaint Handling Service Processes." Applied Sciences 9, no. 16 (August 12, 2019): 3313. http://dx.doi.org/10.3390/app9163313.

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To improve the service quality of complaint handling service in a manufacturing company, it is key to analyze the business processes. Process mining is quite a useful approach to diagnose complaint handling service process problems, such as bottlenecks and deviations. However, the current business process analysis methodologies based on process mining mainly focus on operational process analysis and neglect other system level analysis. In this study, we introduce the method of Accimap from the discipline of accident analysis to analyze the diagnosis results of process mining. By creating a complaint handling service process management Accimap model, the process mining results analysis can be carried out across different system levels. A case study in a big manufacturing company in China is implemented to verify our approach. In the case study, 42 complaint handling process management factors are identified and the complaint handling process management Accimap model is created. The testing results by Rasmussen’s seven predictions in his risk management framework show that Accimap method presents a systematic approach to analyze the process diagnosis results based on process mining.

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S., Omer. "Performance Analysis of Business Processes using Process Mining." International Journal of Computer Applications 180, no. 37 (April 28, 2018): 27–30. http://dx.doi.org/10.5120/ijca2018916651.

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Anisimova, Svetlana. "Process mining is an effective business optimization tool." Upravlenie kachestvom (Quality management), no. 1 (January 1, 2021): 26–31. http://dx.doi.org/10.33920/pro-1-2101-05.

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The effectiveness of any business depends primarily on how effectively its internal processes are arranged and work. Hundreds of books have been written about how to achieve this. Whole theories have been developed, the most popular among which is the management concept of BPM. This is a great concept that helps to clearly answer all the important questions: where, when, why, how and what work is being done and who is responsible for its implementation. But it is only possible to apply this theory correctly only if the business has complete information about what is happening inside it.

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Bistarelli, Stefano, Tommaso Di Noia, Marina Mongiello, and Francesco Nocera. "PrOnto: an Ontology Driven Business Process Mining Tool." Procedia Computer Science 112 (2017): 306–15. http://dx.doi.org/10.1016/j.procs.2017.08.002.

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Lamghari, Zineb. "Unstructured Business Processes Improvement using Process Mining Techniques." ASM Science Journal 17 (March 18, 2022): 1–13. http://dx.doi.org/10.32802/asmscj.2022.965.

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Executing loosely structured processes generate unstructured behaviours. Thus, an Unstructured Business Process (UBP) still has more issues that are difficult to be analysed and to be understood due to its complexity and variability. Moreover, the need of an instantiate response is clearly appeared in operational systems. Therefore, it is required to study related challenges that can be acquired during the transition from the structured BP to the unstructured one. In this context, process mining plays a dominant role to understand business process complexity using event data resulted from business process execution. Mainly, this paper treats three challenges related to unstructured BPs. The first challenge is how to support UBPs at runtime using process mining techniques. The second challenge is how to manage UBP variability taking into consideration variant conditions. The third challenge is how to adapt dynamically UBPs according to the company business rules and conditions.

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Ito, Sohei, Dominik Vymětal, Roman Šperka, and Michal Halaška. "Process mining of a multi-agent business simulator." Computational and Mathematical Organization Theory 24, no. 4 (April 4, 2018): 500–531. http://dx.doi.org/10.1007/s10588-018-9268-6.

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Kozyrieva, Olena, Veronika Khudolei, Valentina Vyhovska, Maksym Zabashtanskyi, and Andrii Rogovyi. "Mining Business Risk Management." E3S Web of Conferences 174 (2020): 04043. http://dx.doi.org/10.1051/e3sconf/202017404043.

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In the mining industry, as a dangerous industry related to the specifics of its production, in particular, the process of risk management and analysis should be taken into account. One of the main reasons of occupational accidents, in addition to human error and technical failures, is the lack of foresight of possible accidental events, and the lack of assessment by a company of the risks associated with occupational safety. The article considers the main risks in the mining industry, analyses the problems of modern systems of risk assessment and management of mining investment projects, methods and sequence of qualitative and quantitative risk assessment, provides recommendations for their improvement in order to bring them in line with international risk management standards.

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Rustan, Ahmad, Ju Lan Hsieh, and Wahyudi Umar. "Maladministration on Mining Business Licenses: Case Study “Mining Business License Production Operation PT. Aneka Tambang, Tbk.”." Varia Justicia 17, no. 3 (December 31, 2021): 246–57. http://dx.doi.org/10.31603/variajusticia.v17i3.6265.

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As a potential natural wealth, mining sectors should be managed with proper rules. Therefore, control instruments are needed in the form of mining business licenses to prevent the negative impact of mining management. At the stage of production operations, the requirements that must be met by businesses are administrative, technical, environmental, and financial. This paper aims to describe maladministration in the issuance of mining business license process especially on upgrading process of exploration permits to production operating permits. This research is a normative juridical research with statute approach and case approach. The results showed that the issuance of mining business license production operation (IUP OP) of PT. Aneka Tambang, Tbk based on the Decree of the Regent of North Konawe No. 158 of 2010 does not meet the technical requirements, especially regionally because the IUP is overlapping 11 other IUP with the same commodity, and there is an IUP OP area which not a part of the exploration area.

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Lamghari, Zineb. "Process Mining: Auditing Approach Based on Process Discovery Using Frequency Paths Concept." ASM Science Journal 17 (November 2, 2022): 1–11. http://dx.doi.org/10.32802/asmscj.2022.1225.

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In the company environment, the management team is responsible for producing normative models. The normative model is considered a standard model that aims at auditing all business processes in the same context. In this regard, the audit operation encompasses four process mining activities, in a hybrid evaluation (offline and online), which are the detect, the check, the compare, and the promote activities. This is still well performed for structured business processes. Otherwise, complex processes may deviate from the initial defined normative model context. Indeed, the latter must be refined for more precise results. Therefore, the combination of human knowledge, control-flow discovery algorithms, and process mining activities is required. To this end, we present a technique for reducing the complexity of unstructured process models (Spaghetti process models) into structured ones (Lasagna process models). This framework outputs a refined normative model for improving the future Business Process (BP) auditing operations. Moreover, this work introduces the sustainability advantage that can occur using process mining techniques.

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Bernardi, Mario Luca, Marta Cimitile, and Francesco Mercaldo. "Cross-Organisational Process Mining in Cloud Environments." Journal of Information & Knowledge Management 17, no. 02 (June 2018): 1850014. http://dx.doi.org/10.1142/s0219649218500144.

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Cloud computing market is continually growing in the last years and becoming a new opportunity for business for private and public organisations. The diffusion of multi-tenants distributed systems accessible by clouds leads to the birth of some cross-organisational environments, increasing the organisation efficiency, promoting the business dynamism and reducing the costs. In spite of these advantages, this new business model drives the interest of researchers and practitioners through new critical issues. First of all, the multi-tenant distributed systems need new techniques to improve the traditional resource management distribution along the different tenants. Secondly, new approaches to the process analysis and monitoring analysed since cross-organisational environments allow various organisations to execute the same process in different variants. Hence, information about how each process variant characterised can be collected by the system and stored as process logs. The usefulness of such logs is twofold: these logs can be analysed using some process mining techniques to understand and improve the business processes and can be used to find better resource management and scalability. This paper proposes a cloud computing multi-tenancy architecture to support cross-organisational process executions and improve resource management distribution. Moreover, the approach supports the systematic extraction/composition of distributed data from the system event logs that are assumed to carry information of each process variant. To this aim, the approach also integrates an online process mining technique for the runtime extraction of business rules from event logs. Declarative processes are used to represent process variants running on the analysed infrastructure as they are particularly suited to represent the business process in a context characterised by low predictability and high variability. In this work, we also present a case study where the proposed architecture is implemented and applied to the execution of a real-life process of online products selling.

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Meddah, Ishak, and Belkadi Khaled. "Discovering Patterns using Process Mining." International Journal of Rough Sets and Data Analysis 3, no. 4 (October 2016): 21–31. http://dx.doi.org/10.4018/ijrsda.2016100102.

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Process mining provides an important bridge between data mining and business process analysis, his techniques allow for extracting information from event logs. In general, there are two steps in process mining, correlation definition or discovery and then process inference or composition. Firstly, the authors' work consists to mine small patterns from a log traces of two applications; SKYPE, and VIBER, those patterns are the representation of the execution traces of a business process. In this step, the authors use existing techniques; The patterns are represented by finite state automaton or their regular expression; The final model is the combination of only two types of small patterns whom are represented by the regular expressions (ab)* and (ab*c)*. Secondly, the authors compute these patterns in parallel, and then combine those small patterns using the composition rules, they have two parties the first is the mine, they discover patterns from execution traces and the second is the combination of these small patterns. The patterns mining and the composition is illustrated by the automaton existing techniques. The Execution traces are the different actions effected by users in the SKYPE and VIBER. The results are general and precise. It minimizes the execution time and the loss of information.

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Chinnaiah, Veluru, Vadlamani Veerabhadram, Ravi Aavula, and Srinivas Aluvala. "PMiner: Process Mining using Deep Autoencoder for Anomaly Detection and Reconstruction of Business Processes." International journal of electrical and computer engineering systems 15, no. 6 (June 7, 2024): 531–42. http://dx.doi.org/10.32985/ijeces.15.6.7.

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We proposed a deep learning-based process mining framework known as PMiner for automatic detection of anomalies in business processes. Since there are thousands of business processes in real-time applications such as e-commerce, in the presence of concurrency, they are prone to exhibit anomalies. Such anomalies if not detected and rectified, cause severe damage to businesses in the long run. Our Artificial Intelligence (AI) enabled framework PMiner takes business process event longs as input and detects anomalies using a deep autoencoder. The framework exploits a deep autoencoder technique which is well-known for Its ability to discriminate anomalies. We proposed an algorithm known as Intelligent Business Process Anomaly Detector (IBPAD) to realize the framework. This algorithm learns from historical data and performs encoding and decoding procedures to detect business process anomalies automatically. Our empirical results using the BPI Challenge dataset, released by the IEEE Task Force on Process Mining, revealed that PMiner outperforms state-of-the-art methods in detecting business process anomalies. This framework helps businesses to identify process anomalies and rectify them in time to leverage business continuity prospects.

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Li, Hong, Yu Wei, Lin Liu, Shao Wen Yao, and Jun Yang. "Process Mining: Overview and Comparative Analysis of the Mining Algorithms." Advanced Materials Research 989-994 (July 2014): 1924–29. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1924.

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Process mining is helpful for deploying new business processes as well as auditing, analyzing and improving the already enacted ones. This paper summarizes the scholars’ main studies in workflow mining, introduces the modeling process of two different kinds of mining algorithms in detail, compares and analyzes their performances, and explains the modeling process with an actual example.

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ÇELİK, Ufuk, and Eyüp AKÇETİN. "Process Mining Tools Comparison." AJIT-e Online Academic Journal of Information Technology 9, no. 34 (November 1, 2018): 97–104. http://dx.doi.org/10.5824/1309-1581.2018.4.007.x.

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Process mining is a new era in the science of data mining and is a subset of business intelligence. Process mining analysis provides an idea about a general process by comparing each process with others in the terms of time and responsible people who deal with the process. For this reason, event logs are checked. Event logs consist of large data. Because the event logs keep all the records that occur during short time intervals. Special programs are needed to examine such data. These programs generate a process map using information such as event ID, activity, time and responsible person. Through the analysis, processes are discovered, monitored and improved. In this study, the tools named ProM, Disco, Celonis and My-Invenio used in process mining were examined and their performance according to usage features compared. According to the obtained results, the usefulness, performance and reporting features of the software used in a process analysis are revealed.

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Kubrak, Kateryna, Fredrik Milani, and Alexander Nolte. "A visual approach to support process analysts in working with process improvement opportunities." Business Process Management Journal 29, no. 8 (April 3, 2023): 101–32. http://dx.doi.org/10.1108/bpmj-10-2021-0631.

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PurposeWhen improving business processes, process analysts can use data-driven methods, such as process mining, to identify improvement opportunities. However, despite being supported by data, process analysts decide which changes to implement. Analysts often use process visualisations to assess and determine which changes to pursue. This paper helps explore how process mining visualisations can aid process analysts in their work to identify, prioritise and communicate business process improvement opportunities.Design/methodology/approachThe study follows the design science methodology to create and evaluate an artefact for visualising identified improvement opportunities (IRVIN).FindingsA set of principles to facilitate the visualisation of process mining outputs for analysts to work with improvement opportunities was suggested. Particularly, insights into identifying, prioritising and communicating process improvement opportunities from visual representation are outlined.Originality/valuePrior work focuses on visualisation from the perspectives – among others – of process exploration, process comparison and performance analysis. This study, however, considers process mining visualisation that aids in analysing process improvement opportunities.

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Et.al, Ang Jin Sheng. "A Framework to Analyze Business Process Log in XML Format." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 11, 2021): 2623–30. http://dx.doi.org/10.17762/turcomat.v12i3.1264.

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XML has numerous uses in a wide variety of web pages and applications. Some common uses of XML include tasks for web publishing, web searching and automation, and general application such as for utilize, store, transfer and display business process log data. The amount of information expressed in XML has gone up rapidly. Many works have been done on sensible approaches to address issues related to the handling and review of XML documents. Mining XML documents offera way to understand both the structure and the content of XML documents. A common approach capable of analysing XML documents is frequent subtree mining.Frequent subtree mining is one of the data mining techniques that finds the relationship between transactions in a tree structured database. Due to the structure and the content of XML format, traditional data mining and statistical analysis hardly applied to get accurate result. This paper proposes a framework that can flatten a tree structured data into a flat and structured data, while preserving their structure and content.Enabling these XML documents into relational structured data allows a range of data mining techniques and statistical test can be applied and conducted to extract more information from the business process log.

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Levykin, V. М., and O. V. Chalaya. "The Model of Knowledge-Intensive Business Process for the Process Mining." Upravlâûŝie sistemy i mašiny, no. 6 (266) (December 2016): 59–66. http://dx.doi.org/10.15407/usim.2016.06.059.

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Martin, Niels, Benoît Depaire, and An Caris. "The Use of Process Mining in Business Process Simulation Model Construction." Business & Information Systems Engineering 58, no. 1 (November 3, 2015): 73–87. http://dx.doi.org/10.1007/s12599-015-0410-4.

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EL KODSSI, Iman, Hanae Sbai, and mustapha Kabil. "Applying Process Mining to Generate Business Process Models from Smart Environments." International Journal of Computing and Digital Systems 15, no. 1 (August 1, 2024): 705–17. http://dx.doi.org/10.12785/ijcds/160152.

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Gazzawe, Foziah, and Ryan Alturki. "Data Mining and Soft Computing in Business Model for Decision Support System." Scientific Programming 2022 (April 12, 2022): 1–6. http://dx.doi.org/10.1155/2022/9147444.

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Several studies were taken to effectively determine the importance of data mining in business model development for decision support system. It was later discovered that most businesses have invested heavily in the data mining process which enables them to easily study and analyse the market environment and improve their domination in the market. Data mining is a process used by most firms to effectively collect information about certain topic. One factor that business model development usually focuses more on is the value of measurable performance and increased innovation in the market. The aim of the current research is to investigate the values and key roles played by data mining prospects in the business model development. It also revolves around current techniques used within business model development that are crucial in enhancing the position and competitiveness of a firm in the market. Although the data mining prowess varies in regards to the firm and effort used, the main concept remains on how to fully have the firm understand the effects and role played by data mining techniques in their overall business flow. In methodology, secondary sources are categorically used to enhance the overall flow of the study question by comparing them and analyzing them using the techniques provided. The results and analysis reveal that the effects of using data mining techniques are huge towards achieving success for various businesses. Moreover, the practical implications have categorically increased the notion of how influential the prospects of data mining can be when applied in businesses.

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Fang, Xianwen, Changjun Jiang, Zhixiang Yin, and Xiaoqin Fan. "The trustworthiness analyzing of interacting business process based on the induction information." Computer Science and Information Systems 8, no. 3 (2011): 843–67. http://dx.doi.org/10.2298/csis100411031f.

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Under the open environments, it is very difficult to guarantee the trustworthiness of interacting business process using traditional software engineering methods, at the same time, for dealing with the influence of external factors, some proposed business process mining methods are only effective 1-bounded business process, and some behavior dependent relationships are ignore. A behavior trustworthiness analysis method of business process based on induction information is presented in the paper. Firstly, aimed to the internal factors, we analyze the consistent behavior relativity to guarantee the predictable function. Then, for the external factors, in order to analyze the behavior change of business process, we propose a process mining methods based on induction information. Finally, experiment simulation is given out, and compares our method with genetic process mining methods. Theoretical analysis and experimental results indicate that our method is better than the genetic process mining method.

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Wei, Yong He, and Jun Zhong Wang. "An Artificial Immune System Approach to Business Process Mining." Advanced Materials Research 472-475 (February 2012): 35–38. http://dx.doi.org/10.4028/www.scientific.net/amr.472-475.35.

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The aim of process mining is to identify and extract process patterns from data logs to reconstruct an overall process model. And the model’s structural complexity directly impacts readability and quality of the model. Immune systems have many characteristics such as uniqueness, autonomous, recognition of foreigners, distributed detection, and noise tolerance. This paper outlines an alternative approach to business process mining utilizing an artificial immune systems (AIS) technique, and some main steps and operators were depicted.

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Kumar, Brajesh. "GENERAL SCHEME OF AN INNOVATIVE BUSINESS PROCESS IN A GOLD MINING INDUSTRY." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 12/12, no. 141 (2023): 111–18. http://dx.doi.org/10.36871/ek.up.p.r.2023.12.12.015.

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the article discusses the advantages and disadvantages of various approaches to organizing the modeling of the business process of innovative activity of a gold mining enterprise. The main features of business processes of innovative activities of a gold mining company are considered. The most important stages of business planning for innovation activities have been established. It is justified to use two approaches to structuring business processes: top-down structuring; structuring from bottom to top. A basic algorithm for structuring a business process from top to bottom has been formed. Tools for structuring business processes from top to bottom are considered. The basic steps of the bottom-up approach are described. The advantages and disadvantages of a bottom-up approach to structuring the business process of innovative activities of a gold mining company are identified.

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Pick, Aleksander, Olegas Vasilecas, Diana Kalibatienė, and Rok Rupnik. "ON APPROACH FOR THE IMPLEMENTATION OF DATA MINING TO BUSINESS PROCESS OPTIMISATION IN COMMERCIAL COMPANIES." Technological and Economic Development of Economy 19, no. 2 (June 17, 2013): 237–56. http://dx.doi.org/10.3846/20294913.2013.796501.

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Nowadays, organisations aim to automate their business processes to improve operational efficiency, reduce costs, improve the quality of customer service and reduce the probability of human error. Business process intelligence aims to apply data warehousing, data analysis and data mining techniques to process execution data, thus enabling the analysis, interpretation, and optimisation of business processes. Data mining approaches are especially effective in helping us to extract insights into customer behaviour, habits, potential needs and desires, credit associated risks, fraudulent transactions and etc. However, the integration of data mining into business processes still requires a lot of coordination and manual adjustment. This paper aims at reducing this effort by reusing successful data mining solutions. We propose an approach for implementation of data mining into a business process. The confirmation of the suggested approach is based on the results achieved in eight commercial companies, covering different industries, such as telecommunications, banking and retail.

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Nkurunziza, Gideon, John Munene, Joseph Ntayi, and Will Kaberuka. "Business process reengineering in developing economies." Innovation & Management Review 16, no. 2 (May 15, 2019): 118–42. http://dx.doi.org/10.1108/inmr-03-2018-0010.

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Purpose The purpose of this paper is to study the relationship between organizational adaptability, institutional leadership and business process reengineering performance using the tested complexity theory in a developing economy setting. Design/methodology/approach This study is correlation and cross-sectional and adopts institutional-level data collected via questionnaires from reengineered microfinance institutions in Uganda. Cluster analysis as data mining technique was used to classify cases based on respondents’ opinions into homogeneous clusters. Nvivo was used to understand the perceptions of business process reengineering performance based on qualitative data. The authors used structural equation modeling to derive the predictive model of business process reengineering performance in a developing world setting. Findings The authors find that organizational adaptability and institutional leadership are key predictors of business process reengineering performance. Results reveal a predictive model of 61 per cent based on structural equation modeling for the study variables. Cluster analysis as data mining approach explored complex patterns of reengineered business processes. Research limitations/implications The use of cluster analysis is susceptible to problems associated with sampling error and absence of fit indices. However, the likelihood of these problems is reduced by the interaction with the data, practical implications and use of smart partial least square to generate structural equations based on derived measurement models of each study variable. Practical implications Policymakers of Bank of Uganda, Ministry of Finance and Economic Planning, should develop sound policies in relation to knowledge management, institutional leadership and adaptive mechanisms to enhance business process reengineering performance to take advantage of new knowledge opportunities for the improvement of their businesses. Social implications Given the results from structural equations generated, managers need to consider institutional leadership and organizational adaptability as key drivers of business process reengineering performance in microfinance institutions. The results confirm the significant role of institutional leadership, organizational adaptability in determining business process reengineering performance outcomes. Originality/value Unlike most of the business process reengineering literature, this study contributes to literature by domesticating and testing complexity theory to explain business process reengineering performance in developing economies.

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Wang, Lili, Xianwen Fang, and Chifeng Shao. "Discovery of Business Process Models from Incomplete Logs." Electronics 11, no. 19 (October 3, 2022): 3179. http://dx.doi.org/10.3390/electronics11193179.

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The completeness of event logs and long-distance dependencies are two major challenges for process mining. Until now, most process mining methods have not been able to discover long-distance dependency and assume that the directly-follows relationship in the log is complete. However, due to the existence of high concurrency and the cycle, it is difficult to guarantee that the real-life log is complete regarding the directly-follows relationship. Therefore, process mining needs to be able to deal with incompleteness. In this paper, we propose a method for discovering process models including sequential, exclusive, concurrent, and cyclic structures from incomplete event logs. The method analyzes the co-occurrence class of the log and the model and then uses the technology of combining the behavior profile and co-occurrence class to obtain the communication behavior profile of the co-occurrence class. Furthermore, a method of constructing a substructure from the event log using the co-occurrence class is presented. Finally, the whole process model is built by combining those substructures. The experimental results show that the proposed method can discover process models with complex structures involving cycles from incomplete event logs and also can deal with long-distance dependency in the event log. Meanwhile, the discovered process model has a good degree of consistency with the original model.

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Journal articles on the topic 'Business Process Mining'

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