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In the last two decades, a proliferation of business process management (BPM) modeling languages, standards and software systems has given rise to much confusion and obstacles to adoption. Since new BPM languages and notation terminologies were not well defined, duplicate features are common. This paper seeks to make sense of the myriad BPM standards, organising them in a classification framework, and to identify key industry trends.

An extensive literature review is conducted and relevant BPM notations, languages and standards are referenced against the proposed BPM Standards Classification Framework, which lists each standard's distinct features, strengths and weaknesses.

The paper is unaware of any classification of BPM languages. An attempt is made to classify BPM languages, standards and notations into four main groups: execution, interchange, graphical, and diagnosis standards. At the present time, there is a lack of established diagnosis standards. It is hoped that such a classification facilitates the meaningful adoption of BPM languages, standards and notations.

The paper differentiates BPM standards, thereby resolving common misconceptions; establishes the need for diagnosis standards; identifies the strengths and limitations of current standards; and highlights current knowledge gaps and future trends. Researchers and practitioners may wish to position their work around this review.

Currently, to the best of one's knowledge, such an overview and such an analysis of BPM standards have not so far been undertaken.

The purpose of this paper is to design a consortium-blockchain based framework for cross-organizational business process mining complying with privacy requirements.

Business process modeling in a cross-organizational setting is complicated due to privacy concerns. The process mining in this situation occurs through trusted third parties (TTPs). It uses a special class of Petri-nets called workflow nets (WF-nets) to represent the formal specifications of event logs in a blockchain-enabled cross-organization.

Using a smart contract algorithm, the proposed framework discovers the organization-specific business process models (BPM) without a TTP. The discovered BPMs are formally represented using WF-nets with a message factor to support the authors’ claim. Finally, the applicability and suitability of the proposed framework is demonstrated using a case study of multimodal transportation.

The proposed framework complies with privacy requirements. It shows how to represent the formal specifications of event logs in a blockchain using a special class of Petri-nets called WF-nets. It also presents a smart contract algorithm to discover organization-specific business process models (BPM) without a TTP.

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.

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.

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.

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.

The purpose of this paper is to review empirical studies on process mining in order to understand its use by organizations. The paper further aims to outline future research opportunities.

The authors propose a classification model that combines core conceptual elements of process mining with prior models from technology classification from the enterprise resource planning and business intelligence field. The model incorporates an organizational usage, a system-orientation and service nature, adding a focus on physical services. The application is based on a systematic literature review of 144 research papers.

The results show that, thus far, the literature has been chiefly concerned with realization of single business process management systems in single organizations. The authors conclude that cross-system or cross-organizational process mining is underrepresented in the ISR, as is the analysis of physical services.

Process mining researchers have paid little attention to utilizing complex use cases and mining mixed physical-digital services. Practitioners should work closely with academics to overcome these knowledge gaps. Only then will process mining be on the cusp of becoming a technology that allows new insights into customer processes by supplying business operations with valuable and detailed information.

Despite the scientific interest in process mining, particularly scant attention has been given by researchers to investigating its use in relatively complex scenarios, e.g., cross-system and cross-organizational process mining. Furthermore, coverage on the use of process mining from a service perspective is limited, which fails to reflect the marketing and business context of most contemporary organizations, wherein the importance of such scenarios is widely acknowledged. The small number of studies encountered may be due to a lack of knowledge about the potential of such scenarios as well as successful examples, a situation the authors seek to remedy with this study.

The purpose of this paper is to consider the question of what is an appropriate enterprise resource planning (ERP) reference model for specifying areas of change in the context of IT-driven ERP implementation and through the model matching. There are other implicit goals to increasing the awareness of the reference models, as this highlights the principles embedded in ERP systems and explains the classification of reference models, which is useful in terms of the reuse of knowledge.

In this paper, a comparison between ERP reference models is conducted using a suitable decision-making technique and the final results are discussed. The comparison depends on nine criteria related to conceptual ERP reference models: scope, abstraction, granularity, views, purpose, simplicity, availability, ease of use for model matching, and target audience.

This study concludes that the business process reference model is best for specifying areas of change in the context of IT-driven ERP implementations. The final ranking of the alternatives based on all criteria places the system organizational model second, followed by the function and data/object reference models, in that order.

This paper is one of very few studies on the selection of appropriate ERP reference models according to the ERP implementation approach and model matching factors. This research also provides an in-depth analysis of various ERP reference model types.

Process mining provides a new means to improve processes in a variety of application domains. The purpose of this paper is to abstract a process model and then use the discovered models from process mining to make useful optimization via predictions.

The paper divides the process model into a combination of “pair-adjacent activities” and “pair-adjacent persons” in the event logs. First, two new handover process models based on adjacency matrix are proposed. Second, by adding the stage, frequency, and time for every activity or person into the matrix, another two new handover prediction process models based on stage adjacency matrix are further proposed. Third, compute the conditional probability from every stage to next stage through the frequency. Finally, use real data to analyze and demonstrate the practicality and effectiveness of the proposed handover optimization process.

The process model can be extended with information to predict what will actually happen, how possible to reach the next activity, who will do this activity, and the corresponding probability if there are several people executing the same activity, etc.

The contribution of this paper is to predict what will actually happen, how possible it is to reach the following activities or persons in the next stage, how soon to reach the following activities or persons by calculating all the possible interval time via different traces, who will do this activity, and the corresponding probability if there are several people executing the same activity, etc.

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.

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.

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.

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.

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.

Predicting the final status of an ongoing process or a subsequent activity in a process is an important aspect of process management. Semi-structured business processes cannot be predicted by precise and mathematical methods. Therefore, artificial intelligence is one of the successful methods. This study aims to propose a method that is a combination of deep learning methods, in particular, the recurrent neural network and Markov chain.

The proposed method applies the BestFirst algorithm for the search section and the Cfssubseteval algorithm for the feature comparison section. This study focuses on the prediction systems of social insurance and tries to present a method that is less costly in providing real-world results based on the past history of an event.

The proposed method is simulated with real data obtained from Iranian Social Security Organization, and the results demonstrate that using the proposed method increases the memory utilization slightly more than the Markov method; however, the CPU usage time has dramatically decreased in comparison with the Markov method and the recurrent neural network and has, therefore, significantly increased the accuracy and efficiency.

This research tries to provide an approach capable of producing the findings closer to the real world with fewer time and processing overheads, given the previous records of an event and the prediction systems of social insurance.

Business process management (BPM) has attracted much focus throughout the years, yet there have been calls questioning the future of BPM. The purpose of this paper is to explore the current state of the field through a dynamic literature review and identify the main challenges for its future development.

A dynamic co-citation network analysis identifies the “evolution” of knowledge of BPM and the most influential works. The results present the developed BPM subthemes in the form of clusters.

The focus within the field has shifted from facilitating wide-ranging business performance improvements to creating introverted optimizations within a particular BPM subgroup. The BPM field has thus experienced strong fragmentation throughout the years and has accrued into self-fueling subareas of BPM research such as business process modeling and workflow management. Those subareas often neglect related disciplines in other management, process modeling and organizational improvement fields.

The study is limited by the initial keyword choice of the authors. The subsequent co-citation analysis ameliorates the subjectivity since it produces a data set and contributions based on references.

A new combination of historical development and the state-of-the-art of the BPM field, by employing a co-citation and cluster analysis. This dynamic literature review presents the current state of the theoretical core and attempts to identify the crossroads that BPM has reached. The study can be replicated in the future to track the changes in the field.

This paper seeks to examine the area of business process mining, providing an overview of state‐of‐the‐art techniques. An outline of the main problems experienced in the practice of process mining is given along with reference to work that addresses the most challenging issues experienced in this field. This paper also aims to examine the application of soft computing techniques to process‐mining problems.

This paper is based on a comprehensive review of literature covering more than 50 research papers. These papers are analysed to identify current trends and future research directions in the field.

Process‐mining techniques are now becoming available as graphical interface‐driven software tools, where flow diagram representations of processes may be manipulated as part of the mining task. A significant number of papers employ mining heuristics to aid in the task of process discovery. Soft computing algorithms are increasingly being investigated to aid the accuracy and speed of mining algorithms. Many papers exist that address common mining problems such as noise and mining loops. However, problems such as duplicate tasks, mining perspectives and delta analysis require further research.

The contribution of this paper is to provide a summary of the current trends in process‐mining practice and point out future research directions. A review of the work in this new and expanding area has been provided in the form of illustrative graphs and tables that identify the trends in this area. This is the most comprehensive and up‐to‐date review of business process‐mining literature.