An Improved Way for Measuring Simplicity During Process Discovery

In this work, we explore an approach to process discovery that is based on combining several existing process discovery algorithms. We focus on algorithms that generate process models in the process tree notation, which are sound by design. The main components of our proposed process discovery approach are the Inductive Miner, the Evolutionary Tree Miner, the Local Process Model Miner and a new bottom-up recursive technique. We conjecture that the combination of these process discovery algorithms can mitigate some of the weaknesses of the individual algorithms. In cases where the Inductive Miner results in overgeneralizing process models, the Evolutionary Tree Miner can often mine much more precise models. At the other hand, while the Evolutionary Tree Miner is computationally expensive, running it only on parts of the log that the Inductive Miner is not able to represent with a precise model fragment can considerably limit the search space size of the Evolutionary Tree Miner. Local Process Models and bottom-up recursion aid the Evolutionary Tree Miner further by instantiating it with frequent process model fragments. We evaluate our approaches on a collection of real-life event logs and find that it does combine the advantages of the miners and in some cases surpasses other discovery techniques.

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.

Business process management (BPM) and accompanying systems allow organizations to react faster both to environmental and market changes. Therefore, BPM is widely applied in industry. Although organizations depend on the secure enactment of electronic business processes, existing BPM languages and techniques provide only little support for security. Several approaches have been proposed to close the gap for security in the domain of BPM. Nevertheless, an approach to develop secure electronic business processes systematically is still missing. In this paper, we provide the design as well as key entities of our Security Engineering Process Model (SecEPM) for electronic business processes. SecEPM guides security, business process, and domain experts through the development of secure business processes from the identification of security goals to the selection and configuration of security controls. It integrates security in the development life cycle of electronic business processes in a flexible way, thus allowing for a secure, adaptable organization.

The automatic detection of divergences from a desired process behavior is a common research topic in the business process management community. An established technique to analyze processes is called conformance checking. Given a definition of a process in form of a process model, conformance checking can be used to test whether the executions of a process contained in a so-called event log data structure are conforming with the process as it was defined. The result is a comparison of the execution traces and their respective correct execution, according to the process model. This technique provides insights into where the divergence has occurred and how the execution must be altered to conform to the process model. However, a problem is that it requires a process model to be available. Process models in the correct format are not always available. Contrary to conformance checking, process anomaly detection aims to find anomalous executions without relying on a predefined process model. A process anomaly detection algorithm derives the process logic from the event log itself and exploits the patterns found within the event log to distinguish normal from anomalous process executions. Though process anomaly detection provides the benefit of not relying on a process model, it typically does not provide the level of detail that conformance checking does. A process execution can either be normal or it can be anomalous. This dissertation proposes process anomaly correction, a novel approach that combines the benefits of conformance checking and process anomaly detection. Given only an event log, process anomaly correction detects anomalous executions, clearly indicates where the anomaly has occurred during the execution and suggests possible corrective measures. The solution presented in this work is based on a new concept to the field of process anomaly detection: Process learning. In process learning, the task of understanding the process based on the example data is transformed into a learning problem in which a neural network is trained to predict the very next activity in a running process execution. The resulting machine learning model thus represents an approximation of the real process that created the data. This cumulative dissertation consists of five contributions to the field of business process management that demonstrate how, starting from a process anomaly detection, process anomaly correction is achieved in a series of four steps. (1) Process learning is employed to generate an approximated model of the process logic. (2) The limitation of only distinguishing between normal and anomalous process executions is overcome by employing the process learning model which processes the process executions on a finer level of detail than existing approaches. (3) The necessity of providing manual threshold settings, as it is typical for process anomaly detection algorithms, is replaced by an automatic parameterization utilizing the process learning model. (4) The predictive capabilities of the process learning model are exploited to generate possible corrections of detected anomalies. The resulting process anomaly correction approach can be employed in scenarios where classic conformance checking would be infeasible, due to the restriction of relying on a process model. Furthermore, it can be employed alongside classical conformance checking, for it incorporates more information coming from the event log than classical conformance checking (such as employees executing a process step, in which country the process is executed, etc.), and thus provides a new perspective for the process analyst.

Healthcare processes are inherently complex as each patient is unique and medical staff deviate from protocols, often for valid reasons. Event logs collected by modern process-aware (healthcare) information systems provide a wealth of data and can be used to analyze the adherence to these protocols. Process mining is a young research area combining data science (machine learning, data mining, etc.) and business process management. Its main contributions have been techniques for process discovery (the automatic learning of process models from event data) and conformance checking (aligning observed and modeled behavior). However, existing techniques face challenging issues discovering high-quality process models in a healthcare setting. In this chapter we introduce the key concepts of process mining such as event logs, process models, and process discovery. We then show the application of two recent process mining techniques on a public event data set from the healthcare domain to demonstrate how some of the common pitfalls can be overcome. A first presented technique projects data statistics on a process model, allowing the analysis of correlations between patient characteristics and the executed activities (e.g., type of treatment). A second technique analyzes process performance without a specific need for a process model by considering contextual information. It correlates process characteristics with process performance.

Information System is one of the key domain in which lot of research has been done in the past few years. Applying Information systems to heterogeneous and distributed environments is one of the current research areas. Business Process Management Systems is the information systems which deal with the administration of tasks in the business processes, organizational structures, or in the related context. The Workflow Management system, the early idea of BPM, controls the workflow in an organization, data transfer, and integration of legacy information systems with existing programs and program modules, delegation of business tasks. Offering task management services especially modeling of business processes and underlying organizations, BPM serves it meaning and incorporates the WFM system. BPM supporting the use of knowledge concerning the awareness/unawareness of integrated software, and analysis of processes and organizational structure in terms of verification, modification, evaluation are key management in BPM system I.INTRODUCTION TO BPM: Combination of corporate restructuring and mergers urged the department and processes to be automated and linked together. Unfortunately it created largely disconnected spaghetti of systems and applications. Business Process Management (BPM) was born from a strong need to streamline internal processes and connections between both internal and external functions. It provides ability to model, monitor, manage and manipulate processes quickly in response to changes in strategy and market forces. BPM is an extension of classical Work Flow Management (WFM) systems and approaches. BPM delivers the maximum utility of legacy system. It is a method for revolutionizing the way the work moves throughout an organization. II.COMPONENTS OF BPM: The four key elements in Business Process Management are: Process modeling, Process rule, Process management, and Process performance analytics. Process modeling: Process modeling describes the flow of information (business process).It helps in building a model of process and mapping into an implementation frame work. Modeling requires a consistent analysis and representation of the processes. It forms the basis for the userspecific process environment. Process Rule: Process Rule is a heart of the BPM. It involves execution of discrete steps & integration of information. A process rules engine is used for this purpose. The engine is to monitor and manage the given steps by providing a flow control over the business process. If a process fails or delayed, a compensated action can be done to rectify. Process Management: Process Management is to correlate business/application events to fundamental business metrics .It enables the organization to monitor and optimize the business process. Once the process has been analyzed, modeled and the associated rules are formed. The next stage involves the visualization and management. Visualization means transaction data ranging from holistic to process-state or client state specific rules. Process Performance Analytics: Process Performance Analytics is a transition data source available for sophisticated management analytics .This data should be available on a retrospective or real-time basis. It should answer to one of the most difficult question like “how much does it cost to process a specific transaction?” By this way BPM mainly addresses four components. They are Work Flow Management, Content and records management Enterprise Application Integration (EAI) and Business Activity monitoring (BAM). III.RELEVENCE TO SYSTEM INTEGRATION: BPM can be fitted easily on the top of existing IT system. It is the Process rules that governs all the happenings and optimizes the process. BPM will not be able to work without the connectivity provided by the EAI layer. EAI layer provides BPM to gain and measure the value from the investment. On the other hand, BPM provides value to the EAI technology layer of Information technology. BPM layer offers business management the view of the message processing layer. It is used to unlock the value of the information held in the integration layer. It is also used to gather data of every transaction in the lifecycle. IV.APPLICATION OF BPM: BPM can be applied to any application which needs these advantages. BPM reduces unnecessary high losses in time critical transactions, helps to provide any critical information, practical application yield fast ROI ,better control over the information, identifying and maximizing the benefits faster, better analysis and tracking, provides enterprise-wide visibility of business processes, improve the effectiveness of the core operations, provides ability to coordinate interactions between information systems, business process and the people, flexible and easily changed by non-technical business people.

A well-known information reuse approach is to exploit event logs for process discovery and optimization. However, process discovery is rarely used for product development projects. This is because information systems in product development, e. g., Product-Lifecycle-Management (PLM) systems, do not provide the event logs required by process discovery algorithms. Additionally, existing algorithms struggle with development projects, as these are unstructured and rich in variety. In this paper, we propose a novel approach to process discovery in order to make it applicable and tailored to product development projects. Instead of using flat event logs, we provide a graph-based data structure that is able to represent both activities and data of product development projects with the dataflow between activities. Based on this structure, we can leverage provenance available in PLM systems. Furthermore, we may use frequent subgraph mining to discover process patterns. Such patterns are well suited to describe different variants and common sub-processes of unstructured processes. Using a prototype, we evaluate this approach and successfully discover prevailing patterns. These patterns may be used by engineers to support their decision-making or help improve the execution of development projects.

Many real world business processes are executed without explicit orchestration and hence do not generate structured execution logs. This is particularly true for the class of business processes which are executed in service delivery centers in emerging markets where rapid changes in processes and in the people executing the processes are common. In such environments, the process execution logs are usually a mix of human entered activity log of actions performed and the auto-generated logs by various tools used during the process execution. Process discovery from unstructured execution logs has been a relatively unexplored research area. In this paper, we propose an approach for process discovery from unstructured logs using gaussian mixture models and hidden markov models. We apply this approach to the logs generated by a real-world business process used in a service delivery center and demonstrate that the results obtained are comparable to an approach of manually labeling the logs followed by a best known process discovery algorithm in literature. The approach proposed is generic and applicable to a wide range of business process execution settings.

PurposeThe purpose of this paper is to address a theoretical gap in the business process management (BPM) literature on factors that influence the acceptance and use of business process modelling (PM) in organisations. The paper seeks to contribute to theory building and practice in BPM through better understanding of important determinants of PM adoption and use.Design/methodology/approachA combination of literature review and 34 interviews in context was used to develop a PM acceptance model that was subsequently empirically tested using survey data from 74 companies.FindingsThe paper provides empirical insights about how business PM can be influenced by many factors in the organisational context. It suggests that PM is a complex activity mandated by management, but influenced by individual and socio‐political factors.Research limitations/implicationsThere is a need for future research to focus on the many opposing forces that influence business PM in organisations. Future studies should analyse influence from different stakeholder groups separately to reveal their relative influence on PM activity and its outcomes.Practical implicationsThe paper identifies important forces in the organisational context that managers should focus on in their efforts to successfully implement business PM in their organisations.Originality/valueThis paper uses a triangulation of sources of information to better understand the less‐studied process of PM adoption and use in an organisational context. It contributes to theory building within BPM and to more successful BPM in organisations.

Event data are collected everywhere: in logistics, manufacturing, finance, healthcare, e-learning, e-government, and many other domains. The events found in these domains typically refer to activities executed by resources at particular times and for particular cases. Process mining provides the means to discover the real processes, to detect deviations from normative processes, and to analyze bottlenecks and waste from such events. However, process mining tends to be backward-looking. Fortunately, simulation can be used to explore different design alternatives and to anticipate future performance problems. This keynote paper discusses the link between both types of analysis and elaborates on the challenges process discovery techniques are facing. Quality notions such as recall, precision, and generalization are discussed. Rather than introducing a specific process discovery or conformance checking algorithm, the paper provides a comprehensive set of conformance propositions. These conformance propositions serve two purposes: (1) introducing the essence of process mining by discussing the relation between event logs and process models, and (2) discussing possible requirements for the quantification of quality notions related to recall, precision, and generalization.

Process discovery aims to derive a process model that accurately represents the observed behavior in an event log. As a state-of-the-art process discovery technique, Inductive Miner (IM) generates sound process models (i.e., free of deadlocks) while ensuring optimal replay fitness. However, IM may sometimes produce over-generalized process models with locally imprecise structures, often resulting in the creation of so-called flower structures. To address this limitation, this paper presents a novel technique that refines the process model generated by IM by optimizing its imprecise sub-processes. Specifically, the technique begins by identifying and extracting sub-logs corresponding to imprecise sub-processes in the initial IM-generated process model. Then, these imprecise sub-processes are iteratively optimized using a frequency-based filtering mechanism applied to the sub-logs. Once optimized, the imprecise sub-processes in the initial process model are replaced by the optimized ones, generating a set of candidates process models. Finally, the candidate with the best quality, in terms of fitness and precision, is selected as the final optimized process models. The proposed technique has been implemented as a plugin for the open-source process mining platform ProM. Through comparisons with state-of-the-art process discovery techniques using 10 publicly available real-life event logs, the experimental results demonstrate that the proposed method achieves an average absolute improvement of 0.173 in F-measure over its IMi variant, while also exhibiting competitive performance relative to other state-of-the-art approaches.

Process mining techniques aim to analyze and improve conformance and performance of processes using event data. Process discovery is the most prominent process-mining task: A process model is derived based on an event log. The process model should be able to capture causalities, choices, concurrency, and loops. Process discovery is very challenging because of trade-offs between fitness, simplicity, precision, and generalization. Note that event logs typically only hold example behavior and cannot be assumed to be complete (to avoid overfitting). Dozens of process discovery techniques have been proposed. These use a wide range of approaches, e.g., language- or state-based regions, genetic mining, heuristics, expectation maximization, iterative log-splitting, etc. When models or logs become too large for analysis, the event log may be automatically decomposed or traces may be clustered before discovery. Clustering and decomposition are done automatically, i.e., no additional information is used. This paper proposes a different approach where a localized event log is assumed. Events are localized by assigning a non-empty set of regions to each event. It is assumed that regions can only interact through shared events. Consider for example the mining of software systems. The events recorded typically explicitly refer to parts of the system (components, services, etc.). Currently, such information is ignored during discovery. However, references to system parts may be used to localize events. Also in other application domains, it is possible to localize events, e.g., communication events in an organization may refer to multiple departments (that may be seen as regions). This paper proposes a generic process discovery approach based on localized event logs. The approach has been implemented in ProM and experimental results show that location information indeed helps to improve the quality of the discovered models.KeywordsVisible TraceConformance CheckVertical DecompositionInductive MinerUnique TraceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Constructing process models from scratch is a complicated time-consuming task that often requires high expertise. And there are discrepancies between the actual workflow processes and the processes as perceived by the management. Therefore, techniques for discovering process models have been developed. Process mining just attempts to improve this by automatically generating a process model from sets of systems’ executions. In this paper, a block structured process mining approach from audit logs to support process discovery is designed. Compare with other algorithms, the result of this approach is more visible and understanding of process model. This approach is used to a widely commercial tool for the visualization and analysis of process model.

Discovering the business process model from an organisation’s records of its operational processes is an active area of research in process mining. The discovered model may be used either during a new system rollout or to improve an existing system. In this paper, we present a process model discovery approach based on the recently proposed bio-inspired Manta Ray Foraging Optimization algorithm (MRFO). Since MRFO is designed to solve real-valued optimization problems, we adapted a binary version of MRFO to suit the domain of process mining. The proposed approach is compared with state-of-the-art process discovery algorithms on several synthetic and real-life event logs. The results show that compared to other algorithms, the proposed approach exhibits faster convergence and yields superior quality process models.

The importance of data is steadily increasing in the domain of business process management due to recent advances in data science, IoT, and Big Data. To reflect this paradigm shift towards data-awareness in service choreographies, we introduced the notion of data-aware choreographies based on concepts for Transparent Data Exchange (TraDE) in our previous works. The goal is to simplify the modeling of business-relevant data and its exchange in choreography models while increasing their run time flexibility. To further improve and simplify the modeling of data-related aspects in service choreographies, in this paper, we focus on the extension of our TraDE concepts to support the modeling of data transformations in service choreographies. Such data transformation capabilities are of dire need to mediate between different data formats, structures and representations of the collaborating participants within service choreographies. Therefore, the paper presents a modeling extension as means for specifying and executing heterogeneous data transformations in service choreographies based on our TraDE concepts.