Extraction of business process models from workflow events logs

Determining right model of business process from event log is the purpose of process discovery. However some problems i.e the inability to discover OR, noise and event log incompleteness are emmerged while determining right model of business process. First, OR relation is often discovered as AND relation. Second, noise problem is occured when there are truncated and low frequency traces in event log. Thus control-flow pattern is used to solve issues of same noise relation frequency hence it discovers relation based on transaction function of activity. Consequently, it can refine non noise relation in business process model. Third, incompleteness leads to incorrect discovery of parallel process model; therefore we used Timed-based Process Mining which utilized non-linear dependence to solve the incompleteness. Finally this paper proposed combination of Timed-based Process Mining and control-flow pattern to discover OR and handle same frequency noise and incompleteness. From the experiment in section 3, this proposed method manages to get right process model from event log.

Business process model discovery (BPMD) is one of the most important research topics in the business process mining area. Many outstanding BPMD algorithms which perform well in most cases have been developed in the last years. As one of the most widely used BPMD algorithms, the Heuristics Miner meets great challenges while dealing with event logs that contain complex behaviours. As a result, process models with low fitness values might be obtained. In this paper, we propose a new technique that is able to locate the process behaviours recorded in an event log which cannot be expressed by the Heuristics Miner and then transform them into expressible behaviours so that a high-fitness model can be built.

Business process modeling aims to construct digital representations of processes being executed in the company. However, models derived from the event logs of their execution tend to overcomplicate the desired representation, making them difficult to apply. The most accurate recovery of the business process model requires a comprehensive study of the various artifacts stored in the company's information system. This paper, however, aims to explore the possibility to automatically obtain the most accurate model of business process, using mutual optimization of models recovered from a set of event logs. Further, the obtained models are executed in multi-agent simulation model of company, and the resulting event logs are examined to determine patterns that are specific to distinct employees and those that generally characterize business process.

Process mining is an emerging discipline incorporating methods and tools for the analysis of system/process executions captured in the form of event logs [1]. Traditionally process mining can be divided into three research areas: discovery (construction of process models from event logs), conformance checking (finding log and model deviations), and enhancement of existing process models with additional event log data.BPMN (Business Process Model and Notation) 2.0 [2] is a widely used process modeling notation, supported by various process modeling and analysis tools, and is a de-facto process modeling standard. Using BPMN within process mining opens perspectives for applicability of the existing process mining techniques: for instance discovered process models can be analyzed or enacted using existing BPMN-based software, and vice versa, manually created models can be imported to a process mining tool, verified against event logs, and enhanced with additional data.In this work we bridge the gap between conventional process modeling formalisms used in the context of process mining (e.g., Petri nets, causal nets, process trees) and BPMN. For that purpose we developed a suite of conversion algorithms and provide formal guarantees relating the behavior of Petri nets (including non-free-choice nets) to the corresponding BPMN models (and vice versa). The derived relations are used to enhance the BPMN models with information learned from the event logs.The developed conversion techniques are described in detail in [3] and have been implemented [4] as a part of ProM (Process Mining Framework) [5] -- an open source tool for process mining and verified on real event log data. Moreover, cases for which conversion algorithms give more compact process models in comparison with the initial models are identified. Although the developed algorithms deal with basic control flow constructs only, they can be applied in the discovery of advanced BPMN modeling elements [2], including subprocesses [6-7], cancellations [8], conditional branching and data objects, swimlanes, message flows, and others.

The aim of organizations is to achieve their business goals with reduced costs. Therefore, it is very important to accurately compute the cost of their business processes and to extend the business process models with cost perspective in order to support decision-makers in reducing these costs. Different approaches proposed an extension of business process models with cost perspective using event logs. However, these approaches do not consider the achievement of the business goals, while it is crucial to provide information about costs incurred by the business process execution that led (or not) to the achievement of the business goals. Such costs, named business costs, depend on the reliability of the business process. Thus, we consider to propose a business cost mining approach allowing the extension of business process models with business cost perspective using cost-annotated event logs. This approach includes business cost description and analysis in order to extract knowledge about factors influencing on business costs. This will further support decision-makers to reduce the costs of their business processes. In this paper, we propose an approach towards business cost mining allowing the extension of business process models with business cost perspective at the activity level.KeywordsBusiness process improvementBusiness cost miningReliabilityBusiness process model cost extension

Business process modelling is an important part of system design. When designing or redesigning a business process, stakeholders specify, negotiate, and agree on business requirements to be satisfied, including non-functional requirements that concern the quality of the business process. This thesis addresses the question of how to specify and compute the quality of a business process, given the model that stakeholders use. The motivation for this thesis is the increasing importance of the quality of business processes. Knowing the quality of specific business processes enables stakeholders to judge if these processes need improvement. Knowing the quality of the constructs of those processes (viz., events, inputs, activities, and outputs) and the way they are structured enables a more detailed analysis of their shortcomings and provides a basis for the design of improvements. The research challenge of this thesis is grounded in the assumption that: “Organisations need an appropriate means to effectively compute achievement of their goals and objectives by their business processes.” Given this challenge, the main research question on which this thesis focuses is: “Can the quality of a business process be computed quantitatively at different levels of granularity?” The research objective is: “To develop frameworks, factors, and metrics for computing non-functional requirements (quality) of business processes quantitatively at different levels of granularity.” The outcomes of this thesis are: 1) BPIMM, a language-independent business process integrating meta-model, based on the concepts of seven mainstream business process modelling languages: BPMN, EPC, RAD, UML AD, SADT, IDEF0, and IDEF3. 2) BPC-QC (Business Process Concept - Quality Computation), an approach to quality computation at the lowest level of granularity of a business process. The approach consists of: i. BPC-QEF (Business Process Concept - Quality Evaluation Framework), a language-independent generic framework and algorithm to compute the quality of the constructs of a business process: event, input, activity, and output. ii. A set of business process quality dimensions and factors. The following quality dimensions are distinguished: performance, efficiency, reliability, recoverability, permissibility, and availability. Each dimension categorises different quality aspects in terms of factors. A non-exhaustive set of sixteen quantitative factors is provided. iii. Quality metrics for each of the quality factors, to facilitate a quantitative computation of the quality of a specific construct of a business process. 3) BP-QC (Business Process - Quality Computation), an approach to compute the quality at the highest level of granularity of a business process. The approach consists of: i. BP-CQCF (Business Process - Compositional Quality Computation Framework), a language-independent generic framework and algorithm to compute the quality of a business process as a whole, given the quality of its constructs. ii. A set of generic business process modelling patterns to decompose a business process into more succinct parts, namely: sequential, parallel with synchronisation, exclusive, inclusive, simple loop, and complex loop. iii. A set of over one hundred computational formulae. For each combination of modelling pattern and a quality factor, there is a formula to compute the quality. 4) AAV (Approach to Application and Validation), an evaluation plan to evaluate BPIMM, BPC-QC and BP-QC in practice, together with expert stakeholders. The plan consists of the units of measure, a measurement model, and a case study procedure. To evaluate the applicability of the contributions of this thesis to real world business needs, four case studies have been conducted in different environments: a Dutch educational institution, a global financial institution, an international financial service provider, and a Dutch research project on crisis management. Each of these case studies concerns a different, single business process. This thesis shows that: 1) A quality computation approach can be adopted independent of a business process modelling language. 2) Quantitative quality factors can be introduced specifically for the constructs of a business process. 3) Quantitative metrics and computational formulae can be developed for specific quality factors, allowing the computation of different aspects of the quality of a business process quantitatively at different levels of granularity. 4) An evaluation plan can be developed to evaluate the applicability of the contributions of this thesis (viz., BPIMM, BPC-QC, and BP-QC). The contributions of this thesis are designed to be beneficial to the areas of business and management, requirements engineering, software engineering, and business process modelling. In the areas of requirements engineering and software engineering, these contributions are intended to help practitioners to consider non-functional requirements at the earliest stage. In the area of business process modelling, information systems, service computing, and cloud computing, the contributions can be used for quality-driven modelling, design, and redesign. To conclude, knowing the quality value of a business process at different levels of granularity provides a basis for its improvement.

Conformance checking is a method to compare the actually executed business process recorded as an event log with the business process described as a business process model and to identify differences. For human or technical reasons, event logs that contain noise and are of low quality may be recorded. Therefore, methods have been proposed to repair low-quality event logs, but they require the setting of a threshold, and it is difficult to set a suitable threshold. In this paper, we investigate the effect of low-quality event log repair methods on conformance checking. Through experiments, it was shown that the appropriate threshold depends on the type of event log and the amount of noise.

Business processes are implemented in an organization. When a business process is run, it generates event log. One type of event log is double timestamp event log. Double timestamp has the start and complete time of each activity executed in the business process and has a close relationship with temporal pattern. In this paper, seven types of temporal pattern between activities were presented as extended version of relations used in the double timestamp event log. Since the event log was not always executed in sequential way, therefore using temporal pattern, event log was divided into several small groups to mine the business process both sequential and parallel. Both temporal pattern and Time-based α+ Miner algorithm were used to mine process model, determined sequential and parallel relations and then evaluated the process model using fitness value. This paper was focused on the advantages of temporal pattern implemented in Time-based α+ Miner algorithm to mine business process. The results also clearly stated that the proposed method could present better result rather than that of original α+ Miner algorithm.

Enterprise business process management is directly affected by how effectively it designs and coordinates decision making. To ensure optimal process executions, decision management should incorporate decision logic documentation and implementation. To achieve the separation of concerns principle, the OMG group proposes to use Decision Model and Notation (DMN) in combination with Business Process Model and Notation (BPMN). However, often in practice, decision logic is either explicitly encoded in process models through control flow structures, or it is implicitly contained in process execution logs. Our work proposes an approach of semi-automatic derivation of DMN decision models from process event logs with the help of decision tree classification. The approach is demonstrated by an example of a loan application in a bank.

Business process modeling (BPM) is one of the key technologies to benefit enterprises with higher efficiencies and greater business values. However, BPM requires intensive business expertise and human resources. Thus, it is valuable and challenging to enable fast delivery of reusable business process designs, regarding the existence of various popular business processes modeling languages and fast changing markets. In this paper, we generate language-specific business process models and process interaction semantics from UML 2.0 process models. A WS-BPEL metadata model and a process interaction semantics metadata model is defined to annotate UML 2.0 business process model as stereotypes; with a set of transformation patterns, we can generate a WS-BPEL business process model and a process interaction semantics file from each annotated UML 2.0 process model. Our approach can also be similarly applied to other process modeling languages to highly reuse the business process model in UML 2.0.

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.

Business processes are essential components of all enterprises and the use of models, languages and execution engines as components of a Business Process Management (BPM) deployment are now commonplace. By definition, these processes describe an enterprise in terms of its organizational knowledge, structure and activities, and are often essential to realizing an organization's competitive advantage. It thus follows that the design, execution and, critically, responsiveness to change in the system or environment they are affecting, is of prime significance toward establishing and maintaining efficient business operation. Deploying a high quality and effective Business Process Management System (BPMS) is thus utterly essential to many modern enterprises. Yet current trends toward flexible methods of working, just-in-time organizational reaction times, distributed intra-organization and interorganization collaboration and constantly changing markets are creating new and complex business landscapes. This brings about increased complexity, further motivating the need for real-time dynamic change throughout an enterprise's business processes; ever more dynamic environments require key business processes to be more flexible and automated in both their design and behaviour. Yet many companies are now discovering that investments in conventional BPMS often suffers from poor return on investment due to a common inability to create business process models that are both meaningful to business people and capable of offering the real-time process flexibility and rapid process adaptation required to cope effectively with the fluid business conditions typifying many modern enterprises. As evidenced by our work with customers in the manufacturing domain, there is very often a need to alter executing process structures, sometimes in real-time, without perturbing the integrity of running process instances. If a BPMS is not built to innately support change in this manner the result can be reductions in both dependability and visibility, especially from a management perspective. Our observation is that many of the current procedural approaches to BPM are too inflexible and unresponsive to change, especially in any automated fashion. In fact many BPMS solutions provide only design-time modelling, with neither support for run-time determination of process structure, nor direct execution of industry standard Business Process Modeling Notation (BPMN) process models without the need for first translating BPMN into intermediary formats, such as the Business Process Execution Language (BPEL), in preparation for execution. In practice, these issues imply that process models can tend to become overly complex and brittle through the necessity of coding-in all O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

To solve the problem of modeling cross-organizational business process, a method of mining the cross-organizational businessprocess model from event logs is proposed. The method describes the different aspects of the cross-organizational business processfrom three perspectives: internal perspective, public perspectiveand collaborative perspective, so as to gain the intra-organization-al process model, the inter-organizational public process model, the collaborative process model in cross-organizational collaborat-ion business. The intra-organizational process model formed bymining, analyzing and sorting the running log of the organization involved in cross-organizational collaboration. The inter-organiza-tional process model is obtained by simplifying the intra-organ-izational process model, and finally the cooperative process modelcan be modelled. Compared with the existing modeling methods, this method can protect the details of the internal business proc-ess, and provides the public process model to public only, which can effectively support the individual characteristics of thecross- organizational business process.

Discrete Modeling and Simulation of Business Processes Using Event Logs

In a real-world environment, business processes are complex and challenging to monitor for potential irregularities. Service-oriented organisations rely primarily on the effectiveness of their processes to maintain the quality of their offered services and prefer to be notified in advance if an ongoing business process is not executing as expected. Process mining techniques analyse business processes, but heterogeneity & variability in the real-world event logs make it challenging and time-consuming to mine using standard methods. Trace clustering divides event logs into sub-logs with similar properties based on the case and event attributes. However, given the size of the event logs collected from heterogeneous information systems, it is difficult to identify meaningful clusters in actual business process logs. This paper proposes a hybrid technique to decompose large event logs into several smaller sub-logs, making it easier and more time efficient to analyse. Each sub-log is independently investigated to gain valuable insights into the process's underlying behaviour. Non-traditional process mining techniques are applied to identify possible behavioural correlations and discrepancies between simulated and observed process execution by extracting features from the log segment. We used a real-world case study to depict our framework's usefulness in prediction accuracy and timeliness. Furthermore, we compared our findings to those of previously proposed methodologies from the literature. We demonstrated that the framework improved the fitness quality of the resultant business process models.