Journal articles: '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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HUANG, Zi-Cheng, Jin-Peng HUAI, Xu-Dong LIU, Xiang LI, and Jiang-Jun ZHU. "Automatic Service Discovery Framework Based on Business Process Similarity." Journal of Software 23, no. 3 (April 28, 2012): 489–503. http://dx.doi.org/10.3724/sp.j.1001.2012.04010.

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García-Bañuelos, Luciano, Marlon Dumas, Marcello La Rosa, Jochen De Weerdt, and Chathura C. Ekanayake. "Controlled automated discovery of collections of business process models." Information Systems 46 (December 2014): 85–101. http://dx.doi.org/10.1016/j.is.2014.04.006.

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Carroll, Ryall, and R. Mitch Casselman. "The Lean Discovery Process: the case of raiserve." Journal of Small Business and Enterprise Development 26, no. 6/7 (December 9, 2019): 765–82. http://dx.doi.org/10.1108/jsbed-04-2019-0124.

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Purpose Uncertainty in the early development of digital business startups can benefit from data-driven testing of hypotheses. Startups face uncertainty not only in product development, but also over the structure of the business model and the nature of the customer or market to address. The authors conceptualize a new model, the Lean Discovery Process (LDP), which focuses on market-based testing from the early business idea through to fully realized product stages of an innovation. The purpose of this paper is to highlight a methodology to help digital business reduce uncertainty and apply lean principles as early as possible in the development of a business concept. Design/methodology/approach Examining literature in lean startups, lean user experience and lean software development, the authors highlight underlying assumptions of existing lean models. The authors then examine the LDP using the case of raiserve, a social entrepreneurship startup that focuses on the management of cause-based voluntary service. Findings Existing literature focuses on product development against an assumed customer base. Early hypothesis testing can be applied to business concept development to substantially reduce cost and time to market. Research limitations/implications Further investigation of different forms of uncertainty in digital startups can open up opportunities to further apply lean methodologies. A more extensive empirical study to measure the potential impact is warranted. Originality/value The authors conceptualize the minimum viable customer and support early testing with concepts from market research and collective intelligence. The authors demonstrate early opportunities to apply lean principles and rigorous hypothesis testing in an LDP that results in significant reductions in time and expense of product development.

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Belhajjame, Khalid, and Marco Brambilla. "Ontological Description and Similarity-Based Discovery of Business Process Models." International Journal of Information System Modeling and Design 2, no. 2 (April 2011): 47–66. http://dx.doi.org/10.4018/jismd.2011040103.

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Project repositories are a central asset in software development, as they preserve the knowledge gathered in past development activities. Locating relevant information in a vast project repository is problematic, because it requires manually tagging projects with accurate metadata, an activity which is time consuming and prone to errors and omissions. Just like any other artifact or web service, business processes can be stored in repositories to be shared and used by third parties, e.g., as building blocks for constructing new business processes. The success of such a paradigm depends partly on the availability of effective search tools to locate business processes that are relevant to the user purposes. A handful of researchers have investigated the problem of business process discovery using as input syntactical and structural information that describes business processes. This work explores an additional source of information encoded in the form of annotations that semantically describe business processes. Business processes can be semantically described using the so called abstract business processes. These are designated by concepts from an ontology which additionally captures their relationships. This ontology can be built in an automatic fashion from a collection of (concrete) business processes, and this work illustrates how it can be refined by domain experts and used in the discovery of business processes, with the purpose of reuse and increase in design productivity.

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Sani, Mohammadreza, Sebastiaan van Zelst, and Aalst van der. "Improving the performance of process discovery algorithms by instance selection." Computer Science and Information Systems 17, no. 3 (2020): 927–58. http://dx.doi.org/10.2298/csis200127028s.

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Process discovery algorithms automatically discover process models based on event data that is captured during the execution of business processes. These algorithms tend to use all of the event data to discover a process model. When dealing with large event logs, it is no longer feasible using standard hardware in limited time. A straightforward approach to overcome this problem is to down-size the event data by means of sampling. However, little research has been conducted on selecting the right sample, given the available time and characteristics of event data. This paper evaluates various subset selection methods and evaluates their performance on real event data. The proposed methods have been implemented in both the ProM and the RapidProM platforms. Our experiments show that it is possible to considerably speed up discovery using instance selection strategies. Furthermore, results show that applying biased selection of the process instances compared to random sampling will result in simpler process models with higher quality.

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Shafiq, Omair, Reda Alhajj, and Jon Rokne. "Log based business process engineering using fuzzy web service discovery." Knowledge-Based Systems 60 (April 2014): 1–9. http://dx.doi.org/10.1016/j.knosys.2013.11.021.

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Savickas, Titas, and Olegas Vasilecas. "Belief network discovery from event logs for business process analysis." Computers in Industry 100 (September 2018): 258–66. http://dx.doi.org/10.1016/j.compind.2018.04.020.

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Nguyen, Hoang, Marlon Dumas, Arthur H. M. ter Hofstede, Marcello La Rosa, and Fabrizio Maria Maggi. "Stage-based discovery of business process models from event logs." Information Systems 84 (September 2019): 214–37. http://dx.doi.org/10.1016/j.is.2019.05.002.

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Camargo, Manuel, Marlon Dumas, and Oscar González-Rojas. "Automated discovery of business process simulation models from event logs." Decision Support Systems 134 (July 2020): 113284. http://dx.doi.org/10.1016/j.dss.2020.113284.

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Wu, Budan, Ran Liu, Rongheng Lin, and Junliang Chen. "A distributed business process fragmentation method based on community discovery." Future Generation Computer Systems 108 (July 2020): 372–89. http://dx.doi.org/10.1016/j.future.2020.02.046.

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Remane, Gerrit, Andre Hanelt, Robert C. Nickerson, and Lutz M. Kolbe. "Discovering digital business models in traditional industries." Journal of Business Strategy 38, no. 2 (April 18, 2017): 41–51. http://dx.doi.org/10.1108/jbs-10-2016-0127.

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Purpose The purpose of this paper is to provide managers from traditional industries with a blueprint to systematically analyze and discover digital business models and, thus, better cope with the digital transformation of their industrial businesses. Design/methodology/approach The proposed blueprint is built on state-of-the-art research on digital business model innovation and a rigorous taxonomy-building approach. The process is demonstrated through a simplified case study of a passenger transport company. Findings The process involves three steps: identifying existing products and services, deconstructing business models and discovering new configurations. The managers from the case company very positively evaluated the efficiency and effectiveness of the proposed procedure. Originality/value The proven methodology relates the generic components of digital business models to a specific firm’s context, listing the solution space for each relevant dimension. The resulting framework aids in better understanding the existing business models and serves as a tool for the systematic discovery of new models.

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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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Cuzzocrea, Alfredo, Francesco Folino, Massimo Guarascio, and Luigi Pontieri. "Deviance-Aware Discovery of High-Quality Process Models." International Journal on Artificial Intelligence Tools 27, no. 07 (November 2018): 1860009. http://dx.doi.org/10.1142/s0218213018600096.

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Process Discovery techniques, allowing to extract graph-like models from large process logs, are a valuable mean for grasping a summarized view of real business processes’ behaviors. If augmented with statistics on process performances (e.g., processing times), such models help study the evolution of process performances across different processing steps, and possibly detect bottlenecks and worst practices. However, when the process analyzed exhibits complex and heterogeneous behaviors, these techniques fail to yield good quality models, in terms of readability, accuracy and generality. In particular, the presence of deviant traces may lead to cumbersome models and misleading performance statistics. Current noise/outlier filtering solutions can alleviate this problem and help discover a better model for “normal” process executions, but they do not provide insight on the deviant ones. Then, difficult and expensive analyses are usually performed to extract interpretable and general enough patterns for deviant behaviors. The performance-oriented discovery approach proposed here is addressed to recognize and describe both a normal execution scenario and deviant ones for the process analyzed, by inducing different sub-models: (i) a collection of readable clustering rules (conjunctive patterns over trace attributes) defining the deviance scenarios; (ii) a performance model [Formula: see text] for the “normal” traces that do not fall in any deviant scenario; and (iii) a performance model (and a “difference” model emphasizing the differences in behaviors from the “normal” execution scenario), for each discovered deviance scenario. Technically, these models are discovered by exploiting a conceptual clustering method, embedded in an iterative optimization scheme where the current version of [Formula: see text] is replaced with the model extracted from the newly found normality cluster, in case the latter is more accurate than [Formula: see text]; on the other hand, the clustering procedure is devised to greedily find groups of traces that maximally deviate from [Formula: see text]. Tests on real-life logs confirmed the validity of this approach, and its capability to find good performance models, and to support the analysis of deviant process instances.

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Nikhilsai, Sanjay, and S. Jancy. "Discovering Unpredictable Changes in Business Process." Journal of Computational and Theoretical Nanoscience 16, no. 8 (August 1, 2019): 3228–31. http://dx.doi.org/10.1166/jctn.2019.8167.

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Early recognition of business process changes empowers chiefs to recognize and follow up on changes that may some way or another influence procedure execution. Business process detection refers to a group of methods to detect changes in a business process by analyzing event logs extracted from the systems that support the execution of the process. Existing strategies for business process float discovery depend on an explorative examination of a possibly vast element space and now and again they expect clients to physically recognize explicit highlights that portray the float. Contingent upon the investigated highlight space, these strategies miss different sorts of changes. Additionally, they are either intended to identify sudden floats or continuous floats yet not both. This paper proposes a robotized and factually grounded strategy for recognizing sudden and continuous business process floats under a brought together system.

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Kwon, In Ho. "Book Review: Process Mining: Discovery, Conformance and Enhancement of Business Processes." Healthcare Informatics Research 20, no. 2 (2014): 157. http://dx.doi.org/10.4258/hir.2014.20.2.157.

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LI, Huiling, Shuaipeng ZHANG, and Xuan SU. "Trace Clustering: A Preprocessing Method to Improve the Performance of Process Discovery." International Journal of Science and Engineering Applications 10, no. 09 (September 2021): 116–21. http://dx.doi.org/10.7753/ijsea1009.1001.

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The information system collects a large number of business process event logs, and process discovery aims to discover process models from the event logs. Many process discovery methods have been proposed, but most of them still have problems when processing event logs, such as low mining efficiency and poor process model quality. The trace clustering method allows to decompose original log to effectively solve these problems. There are many existing trace clustering methods, such as clustering based on vector space approaches, context-aware trace clustering, model-based sequence clustering, etc. The clustering effects obtained by different trace clustering methods are often different. Therefore, this paper proposes a preprocessing method to improve the performance of process discovery, called as trace clustering. Firstly, the event log is decomposed into a set of sub-logs by trace clustering method, Secondly, the sub-logs generate process models respectively by the process mining method. The experimental analysis on the datasets shows that the method proposed not only effectively improves the time performance of process discovery, but also improves the quality of the process model.

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Fani Sani, Mohammadreza, Sebastiaan J. van Zelst, and Wil M. P. van der Aalst. "The impact of biased sampling of event logs on the performance of process discovery." Computing 103, no. 6 (February 16, 2021): 1085–104. http://dx.doi.org/10.1007/s00607-021-00910-4.

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AbstractWith Process discovery algorithms, we discover process models based on event data, captured during the execution of business processes. The process discovery algorithms tend to use the whole event data. When dealing with large event data, it is no longer feasible to use standard hardware in a limited time. A straightforward approach to overcome this problem is to down-size the data utilizing a random sampling method. However, little research has been conducted on selecting the right sample, given the available time and characteristics of event data. This paper systematically evaluates various biased sampling methods and evaluates their performance on different datasets using four different discovery techniques. Our experiments show that it is possible to considerably speed up discovery techniques using biased sampling without losing the resulting process model quality. Furthermore, due to the implicit filtering (removing outliers) obtained by applying the sampling technique, the model quality may even be improved.

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Jiang, Bo, Junwu Chen, Ye Wang, Liping Zhao, and Pengxiang Liu. "Deep Learning-Based Service Discovery for Business Process Re-Engineering in the Era of Big Data." International Journal of Big Data Intelligence and Applications 1, no. 1 (January 2020): 1–22. http://dx.doi.org/10.4018/ijbdia.2020010101.

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In recent years, business process re-engineering has played an important role in the development of large-scale web-based applications. To re-engineer business processes, business services are developed and coordinated by reusing a set of open APIs and services on the internet. Yet, the number of services on the internet has grown drastically, making it difficult for them to be discovered to support the changing business goals. One major challenge is therefore to search for a suitable service that matches a specific business goal from a large number of available services in an efficient and effective manner. To address this challenge, this paper proposes a deep learning approach for massive service discovery. The approach, thus called MassRAFF, employs a combination of the recurrent attention and feature fusion methods. This paper first presents the MassRAFF approach and then reports on an experiment for evaluating this approach. The experimental results show that the MassRAFF approach has performed reasonably well and has potential to be improved further in future work.

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Tariq, Zeeshan, Naveed Khan, Darryl Charles, Sally McClean, Ian McChesney, and Paul Taylor. "Understanding Contrail Business Processes through Hierarchical Clustering: A Multi-Stage Framework." Algorithms 13, no. 10 (September 27, 2020): 244. http://dx.doi.org/10.3390/a13100244.

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Real-world business processes are dynamic, with event logs that are generally unstructured and contain heterogeneous business classes. Process mining techniques derive useful knowledge from such logs but translating them into simplified and logical segments is crucial. Complexity is increased when dealing with business processes with a large number of events with no outcome labels. Techniques such as trace clustering and event clustering, tend to simplify the complex business logs but the resulting clusters are generally not understandable to the business users as the business aspects of the process are not considered while clustering the process log. In this paper, we provided a multi-stage hierarchical framework for business-logic driven clustering of highly variable process logs with extensively large number of events. Firstly, we introduced a term contrail processes for describing the characteristics of such complex real-world business processes and their logs presenting contrail-like models. Secondly, we proposed an algorithm Novel Hierarchical Clustering (NoHiC) to discover business-logic driven clusters from these contrail processes. For clustering, the raw event log is initially decomposed into high-level business classes, and later feature engineering is performed exclusively based on the business-context features, to support the discovery of meaningful business clusters. We used a hybrid approach which combines rule-based mining technique with a novel form of agglomerative hierarchical clustering for the experiments. A case-study of a CRM process of the UK’s renowned telecommunication firm is presented and the quality of the proposed framework is verified through several measures, such as cluster segregation, classification accuracy, and fitness of the log. We compared NoHiC technique with two trace clustering techniques using two real world process logs. The discovered clusters through NoHiC are found to have improved fitness as compared to the other techniques, and they also hold valuable information about the business context of the process log.

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Halaška, Michal, and Roman Šperka. "Performance of an automated process model discovery – the logistics process of a manufacturing company." Engineering Management in Production and Services 11, no. 2 (July 30, 2019): 106–18. http://dx.doi.org/10.2478/emj-2019-0014.

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AbstractThe simulation and modelling paradigms have significantly shifted in recent years under the influence of the Industry 4.0 concept. There is a requirement for a much higher level of detail and a lower level of abstraction within the simulation of a modelled system that continuously develops. Consequently, higher demands are placed on the construction of automated process models. Such a possibility is provided by automated process discovery techniques. Thus, the paper aims to investigate the performance of automated process discovery techniques within the controlled environment. The presented paper aims to benchmark the automated discovery techniques regarding realistic simulation models within the controlled environment and, more specifically, the logistics process of a manufacturing company. The study is based on a hybrid simulation of logistics in a manufacturing company that implemented the AnyLogic framework. The hybrid simulation is modelled using the BPMN notation using BIMP, the business process modelling software, to acquire data in the form of event logs. Next, five chosen automated process discovery techniques are applied to the event logs, and the results are evaluated. Based on the evaluation of benchmark results received using the chosen discovery algorithms, it is evident that the discovery algorithms have a better overall performance using more extensive event logs both in terms of fitness and precision. Nevertheless, the discovery techniques perform better in the case of smaller data sets, with less complex process models. Typically, automated discovery techniques have to address scalability issues due to the high amount of data present in the logs. However, as demonstrated, the process discovery techniques can also encounter issues of opposite nature. While discovery techniques typically have to address scalability issues due to large datasets, in the case of companies with long delivery cycles, long processing times and parallel production, which is common for the industrial sector, they have to address issues with incompleteness and lack of information in datasets. The management of business companies is becoming essential for companies to stay competitive through efficiency. The issues encountered within the simulation model will be amplified through both vertical and horizontal integration of the supply chain within the Industry 4.0. The impact of vertical integration in the BPMN model and the chosen case identifier is demonstrated. Without the assumption of smart manufacturing, it would be impossible to use a single case identifier throughout the entire simulation. The entire process would have to be divided into several subprocesses.

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Ordoñez, Hugo, Armando Ordóñez, Victor Buchelli, and Carlos Cobos. "TrazasBP: A Framework for Business Process Models Discovery Based on Execution Cases." Polibits 57 (January 31, 2018): 51–57. http://dx.doi.org/10.17562/pb-57-5.

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Narayanan, V. K. "Idea labs: instituting an innovation discovery process capable of sustaining the business." Strategy & Leadership 45, no. 1 (January 16, 2017): 27–36. http://dx.doi.org/10.1108/sl-12-2016-0089.

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Purpose Sustaining product innovation in an established company – increasingly the key to a company’s economic success, and perhaps its survival – is a challenging task The author describes a and the model often referred to as an “Idea lab” that has emerged as a necessary organizational feature to accomplish this goal. Design/methodology/approach The author explains how to manage Idea labs as deliberately established locations, where individuals and teams with new product ideas can work together for concentrated bursts of time, sharpening and focusing their product concept, embedding the voice of the customer in product design and charting alternative progression paths for their ideas to be developed into potentially profitable offerings by units of the business that will nurture them. Findings In today’s organizations, the managerial prime directive is fundamentally being redefined as one of addressing the challenge of sustained, profitable innovation that opens new markets or reinvigorates existing ones. Idea labs should be considered a vital process in fostering sustaining innovation. Practical implications A critical success factor is the interplay between idea originators, technology specialists and product managers with a keen awareness of customer needs, competitor initiatives and genuine product differentiation. Originality/value A comprehensive guide for top managers and innovators, the article details the four key facets of Idea labs: Positioning in the firm’s innovation value chain. Tasks. Processes. Structure.

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Saragih, Harriman Samuel, Togar Simatupang, and Yos Sunitiyoso. "From co-discovery to co-capture: co-innovation in themusic business." International Journal of Innovation Science 11, no. 4 (November 29, 2019): 600–617. http://dx.doi.org/10.1108/ijis-07-2019-0068.

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Purpose Previous work has asserted that the co-innovation process in the music business is composed of four stages, i.e. co-discovery, co-creation, co-delivery and co-capture. This study aims to re-examine and validate this proposed conceptualisation by gathering and interviewing additional respondents, specifically academics and professional event organisers, who were not formerly involved. By gaining more insight from different stakeholders, this study expects to gain more reliable results regarding the proposed concept derived from the previous study. Design/methodology/approach This study uses the case study method by carrying out qualitative interview data collection from 11 respondents. Narrative analysis is used in examining the findings. Pattern matching is used as the basis of the analysis using the proposed conceptualisation from co-discovery to co-capture of co-innovation as the rival analysis to the empirical findings discovered in this study. This paper also discusses how the validity and reliability of the qualitative analysis carried out are ensured. Findings This study supports the notion that the co-innovation process in the music industry follows the four stages of co-discovery, co-creation, co-delivery and co-capture. The respondents, from different professional backgrounds, interviewed in this study indicated and validated that the proposed framework aligns with their actual practices, expectations and realities, along with their specific roles in the music industry’s ecosystems. Practical implications The results of this study can be used as a reference in developing guidelines or policies for co-innovation practices in the music business, which previous studies have not explored, e.g. focusing only on preconditions for positive collaboration, open license and music for co-creation or discussions that are merely conceptual. Originality/value This study validates the co-innovation process in the music business proposed by the previous works, which integrates the value chain thinking concept within the analysis.

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Choudhary, Kapil, and Sushil Bajaj. "Price Discovery Process in Nifty Spot and Futures Markets." Global Business Review 14, no. 1 (February 2013): 55–88. http://dx.doi.org/10.1177/0972150912466444.

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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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Pourmirza, Shaya, Remco Dijkman, and Paul Grefen. "Correlation Miner: Mining Business Process Models and Event Correlations Without Case Identifiers." International Journal of Cooperative Information Systems 26, no. 02 (May 18, 2017): 1742002. http://dx.doi.org/10.1142/s0218843017420023.

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

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Kim, Aekyung, Josue Obregon, and Jae-Yoon Jung. "PRANAS: A Process Analytics System Based on Process Warehouse and Cube for Supply Chain Management." Applied Sciences 10, no. 10 (May 20, 2020): 3521. http://dx.doi.org/10.3390/app10103521.

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Most organizations need to monitor and assess their business activities. In order to support the performance analysis of the business activities in a more systematic manner, in this research, we introduce a PRocess ANalytics System, called PRANAS. The system adopts process warehouses and process cubes to support process-oriented analysis, as well as data-oriented analysis. In this research, the process warehouse and cube were designed to assess business performances for supply chain management, specifically under the SCOR standard models. Furthermore, the process cube was constructed based on process-related dimensions such as time, case type, and event class to support process mining. Finally, we exemplify how the system can be applied to process analytics with three use cases of process discovery, data analytics, and decision point analysis. It is expected that the proposed system can be a helpful reference model when business process analyst designs process analytics systems in the process-oriented perspective, as well as in the data-oriented perspective.

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Zhou, Huan, Chuang Lin, and Yi Ping Deng. "Process Mining Based on Statistic Ordering Relations of Events." Advanced Materials Research 760-762 (September 2013): 1959–66. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1959.

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An explicit process model is vital in business processes. However, it is complicated and time consuming to create a workflow design. Also discords usually occur between the perceived management processes and the actual workflow processes. Under this condition, the process discovery techniques emerge. The aim is to rebuild a workflow model (e.g. a Petri net) of a business process based on the execution log. The model should give an abstract representation of the system and reproduce the log. This model can be further applied for process redesign/improvement and performance/reliability evaluation. In this paper, we present a new algorithm derived from α-algorithm for process discovery in term of Petri nets, where statistic long distance causal relationship is taken into consideration. Also this algorithm covers some shortages in α-algorithm.

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Datta, Anindya. "Automating the Discovery of AS-IS Business Process Models: Probabilistic and Algorithmic Approaches." Information Systems Research 9, no. 3 (September 1998): 275–301. http://dx.doi.org/10.1287/isre.9.3.275.

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Sarno, Riyanarto, and Kelly Rossa Sungkono. "A survey of graph-based algorithms for discovering business processes." International Journal of Advances in Intelligent Informatics 5, no. 2 (July 30, 2019): 137. http://dx.doi.org/10.26555/ijain.v5i2.296.

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Algorithms of process discovery help analysts to understand business processes and problems in a system by creating a process model based on a log of the system. There are existing algorithms of process discovery, namely graph-based. Of all algorithms, there are algorithms that process graph-database to depict a process model. Those algorithms claimed that those have less time complexity because of the graph-database ability to store relationships. This research analyses graph-based algorithms by measuring the time complexity and performance metrics and comparing them with a widely used algorithm, i.e. Alpha Miner and its expansion. Other than that, this research also gives outline explanations about graph-based algorithms and their focus issues. Based on the evaluations, the graph-based algorithm has high performance and less time complexity than Alpha Miner algorithm.

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Shrestha, Keshab, Ravichandran Subramaniam, and Thangarajah Thiyagarajan. "Price Discovery in Agricultural Markets." American Business Review 23, no. 1 (May 2020): 53–69. http://dx.doi.org/10.37625/abr.23.1.53-69.

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In this study, we empirically analyze the contribution of futures markets to the price discovery process for seven agricultural commodities using the generalized information share proposed by Lien and Shrestha (2014) and component share based on the permanent-temporary decomposition proposed by Gonzalo and Granger (1995). We find that most of the price discovery takes place in the futures markets with the exception of cocoa. Our results show that futures markets play an important role in price discovery process. These results are important to academicians, practitioners, policymakers as well as business leaders.

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Sun, Zhaohao, Paul Pinjik, and Francisca Pambel. "Business Case Mining and E-R Modeling Optimization." Studies in Engineering and Technology 8, no. 1 (July 9, 2021): 53. http://dx.doi.org/10.11114/set.v8i1.5288.

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Business case mining and business rule discovery are at the center for entity relationship (E-R) modeling and database design to obtain E-R models. How to transform business cases through business rules into E-R models is a fundamental issue for database design. This article addresses this issue by exploring business case mining and E-R modeling optimization. Business case mining is business rule discovery from a business case. This article reviews case-based reasoning, explores business case-based reasoning, and presents a unified approach to business case mining for business rule discovery. The approach includes people-centered entity/business rule discovery and function-centered entity/business rule discovery. E-R modeling optimization aims to improve the E-R modeling process to get a better E-R diagram that reflects the business case properly. This article proposes a unified optimal method for E-R modeling. The unified optimal method includes people-centered E-R modeling, function-centered E-R modeling, and hierarchical E-R modeling. The approach proposed in this research will facilitate the research and development of E-R modeling, database design, data science, and big data analytics.

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Liu, Cong, Huiling Li, Qingtian Zeng, Ting Lu, and Caihong Li. "Cross-Organization Emergency Response Process Mining: An Approach Based on Petri Nets." Mathematical Problems in Engineering 2020 (November 17, 2020): 1–12. http://dx.doi.org/10.1155/2020/8836007.

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To support effective emergency disposal, organizations need to collaborate with each other to complete the emergency mission that cannot be handled by a single organization. In general, emergency disposal that involves multiple organizations is typically organized as a group of interactive processes, known as cross-organization emergency response processes (CERPs). The construction of CERPs is a time-consuming and error-prone task that requires practitioners to have extensive experience and business background. Process mining aims to construct process models by analyzing event logs. However, existing process mining techniques cannot be applied directly to discover CERPs since we have to consider the complexity of various collaborations among different organizations, e.g., message exchange and resource sharing patterns. To tackle this challenge, a CERP model mining method is proposed in this paper. More specifically, we first extend classical Petri nets with resource and message attributes, known as resource and message aware Petri nets (RMPNs). Then, intra-organization emergency response process (IERP) models that are represented as RMPNs are discovered from emergency drilling event logs. Next, collaboration patterns among emergency organizations are formally defined and discovered. Finally, CERP models are obtained by merging IERP models and collaboration patterns. Through comparative experimental evaluation using the fire emergency drilling event log, we illustrate that the proposed approach facilitates the discovery of high-quality CERP models than existing state-of-the-art approaches.

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R'bigui, Hind, and Chiwoon Cho. "Heuristic rule-based process discovery approach from events data." International Journal of Technology, Policy and Management 19, no. 4 (2019): 352. http://dx.doi.org/10.1504/ijtpm.2019.10025752.

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R', Hind, N. A. bigui, and Chiwoon Cho. "Heuristic rule-based process discovery approach from events data." International Journal of Technology, Policy and Management 19, no. 4 (2019): 352. http://dx.doi.org/10.1504/ijtpm.2019.104060.

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Zhang, Guan Zhu, and Yu Ye Zhu. "Research of After-Sales Management System of Enterprises Based on J2EE and Data Mining Technology." Applied Mechanics and Materials 608-609 (October 2014): 375–81. http://dx.doi.org/10.4028/www.scientific.net/amm.608-609.375.

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With the globalization of market and economy, more and more enterprises realize the importance of after-sales service system. However, traditonal after-sales service system only focuses on the business process of system, and ignores important information of after-sales service data. It is data mining technique that solves the problem as a knowledge discovery technique. Data mining technique only can discover potential and valuable information and knowledge in lots of data for decision support. The paper analyzes the business process of after-sales service of enterprises, uses the idea of J2EE design mode, and expounds the development design of the system including the design of J2EE frame, functional module, system component and database.

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Augusto, Adriano, Raffaele Conforti, Marlon Dumas, Marcello La Rosa, and Artem Polyvyanyy. "Split miner: automated discovery of accurate and simple business process models from event logs." Knowledge and Information Systems 59, no. 2 (May 15, 2018): 251–84. http://dx.doi.org/10.1007/s10115-018-1214-x.

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LI, Huiling, Xuan SU, and Shuaipeng ZHANG. "https://ijsea.com/archive/volume10/issue9/IJSEA10091005.pdf." International Journal of Science and Engineering Applications 10, no. 9 (September 2021): 144–47. http://dx.doi.org/10.7753/ijsea1009.1006.

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

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Augusto, Adriano, Marlon Dumas, Marcello La Rosa, Sander J. J. Leemans, and Seppe K. L. M. vanden Broucke. "Optimization framework for DFG-based automated process discovery approaches." Software and Systems Modeling 20, no. 4 (February 27, 2021): 1245–70. http://dx.doi.org/10.1007/s10270-020-00846-x.

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AbstractThe problem of automatically discovering business process models from event logs has been intensely investigated in the past two decades, leading to a wide range of approaches that strike various trade-offs between accuracy, model complexity, and execution time. A few studies have suggested that the accuracy of automated process discovery approaches can be enhanced by means of metaheuristic optimization techniques. However, these studies have remained at the level of proposals without validation on real-life datasets or they have only considered one metaheuristic in isolation. This article presents a metaheuristic optimization framework for automated process discovery. The key idea of the framework is to construct a directly-follows graph (DFG) from the event log, to perturb this DFG so as to generate new candidate solutions, and to apply a DFG-based automated process discovery approach in order to derive a process model from each DFG. The framework can be instantiated by linking it to an automated process discovery approach, an optimization metaheuristic, and the quality measure to be optimized (e.g., fitness, precision, F-score). The article considers several instantiations of the framework corresponding to four optimization metaheuristics, three automated process discovery approaches (Inductive Miner—directly-follows, Fodina, and Split Miner), and one accuracy measure (Markovian F-score). These framework instances are compared using a set of 20 real-life event logs. The evaluation shows that metaheuristic optimization consistently yields visible improvements in F-score for all the three automated process discovery approaches, at the cost of execution times in the order of minutes, versus seconds for the baseline approaches.

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Kalenkova, Anna A., and Danil A. Kolesnikov. "Application of Genetic Algorithms for Finding Edit Distance between Process Models." Modeling and Analysis of Information Systems 25, no. 6 (December 19, 2018): 711–25. http://dx.doi.org/10.18255/1818-1015-2018-6-711-725.

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Finding graph-edit distance (graph similarity) is an important task in many computer science areas, such as image analysis, machine learning, chemicalinformatics. Recently, with the development of process mining techniques, it became important to adapt and apply existing graph analysis methods to examine process models (annotated graphs) discovered from event data. In particular, finding graph-edit distance techniques can be used to reveal patterns (subprocesses), compare discovered process models. As it was shown experimentally and theoretically justified, exact methods for finding graph-edit distances between discovered process models (and graphs in general) are computationally expensive and can be applied to small models only. In this paper, we present and assess accuracy and performance characteristics of an inexact genetic algorithm applied to find distances between process models discovered from event logs. In particular, we find distances between BPMN (Business Process Model and Notation) models discovered from event logs by using different process discovery algorithms. We show that the genetic algorithm allows us to dramatically reduce the time of comparison and produces results which are close to the optimal solutions (minimal graph edit distances calculated by the exact search algorithm).

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Journal articles on the topic 'Business process discovery'

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