Time-based α+ miner for modelling business processes using temporal pattern

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.

The current business environment has no room for inefficiency as it can cause companies to lose out to their competitors, to lose customer trust, and to experience cost overruns. Business processes within the company continue to grow and cause them to run more complex. The large scale and complexity of business processes pose a challenge in improving the quality of process model because the effectiveness of time and the efficiency of existing resources are the biggest challenges. In the context of optimizing business processes with a process mining approach, most current process models are optimized with a trace clustering approach to explore the model and to perform analysis on the resulting process model. Meanwhile, in the event log data, not only the activities but also the other resources, such as records of employee or staff working time, process service time, and processing costs, are recorded. This article proposes a mechanism alternative to optimize business processes by exploring the resources that occur in the process. The mechanism is carried out in three stages. The first stage is optimizing the job shop scheduling method from the generated event log. Scheduling the time becomes a problem in the job shop. Utilizing the right time can increase the effectiveness of performance in order to reduce costs. Scheduling can be defined as the allocation of multiple jobs in a series of machines, in which each machine only does one job at a time. In general, scheduling becomes a problem when sequencing the operations and allocating them into specific time slots without prolonging the technical and capacity constraints. The second stage is generating the resource value that is recorded in the event log from the results of analysis of the previous stage, namely, job shop scheduling. The resource values are multivariate and then clustered to determine homogeneous clusters. The last stage is optimizing the nonlinear multipolynomials in the homogeneous cluster formed by using the Hessian solution. The results obtained are analyzed to get recommendations on business processes that are appropriate for the company’s needs. The impact of long waiting times will increase service costs, but by improving workload, costs can be reduced. The process model and the value of service costs resulting from the mechanism in the research can be a reference for process owners in evaluating and improving ongoing processes.

Most actual information systems save transactions in event logs. These logs are not often used to improve the quality of the process model. The area of Process Mining provides techniques and tools which allow the extraction of persistent information about the business process model from event logs in order to incorporate them in a decisional system, and optimise the future executions of the business processes. In this paper, we present an approach using (in addition of event logs) some properties of the tasks composing the business processes like minimum and maximum durations. These properties are used to improve the quality of the underlying processes by a best detection of parallelism, loops and duplicated tasks.

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.

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.

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.

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.

비즈니스 프로세스 및 워크플로우 기술의 국제표준화기구인 WfMCl)에서는 최근 비즈니스 프로세스 인텔리전스 마이닝 분야에 대한 산업체의 관심이 증가함에 따라 프로세스 실행이벤트로그 표준포맷인 비즈니스 프로세스 분석로그 포맷, BPAF2) 1.0을 공식적으로 발표한 바 있다. 즉, 비즈니스 프로세스 인텔리전스 마이닝 기술은 비즈니스 프로세스 모델의 실행이벤트로그로부터 제어흐름, 데이터흐름, 역할흐름, 수행자흐름 등의 흐름중심의 인텔리전스와 최근에 관심이 집중되는 프로세스기반 소셜네트워크, 소속성네트워크 등의 관계중심의 인텔리전스를 마이닝하는 일련의 알고리즘들과 분석기법들로 구성되는데 현재의 표준포맷인 BPAF 1.0은 비즈니스 프로세스의 제어흐름 인텔리전스 마이닝에 초점 맞추고 있어 최근에 관심이 집중되는 관계중심의 인텔리전스 마이닝을 지원할 수가 없다. 따라서, 본 표준화 논문에서는 제어흐름 인텔리전스 이외에 데이터흐름, 역할흐름, 수행자흐름의 흐름 중심 인텔리전스 뿐만 아니라 프로세스기반 소셜네트워크, 소속성 네트워크의 관계중심 인텔리전스의 마이닝을 지원할 수 있도록 기존의 BPAF 1.0 표준포맷을 확장한 BPAF 2.0 표준포맷을 제안한다. 특히, 본 논문에서 제안하는 BPAF 2.0은 한국정보통신기술협회 표준총회의 e 비즈니스 프로젝트 그룹을 통한 국내 표준안의 기반기술이 될 뿐 만 아니라 BPAF 1.0을 제정한 WfMC 국제표준화기구의 국제 표준안의 확장에 기여할 것이라고 판단한다. WfMC, which is one of the international standardization organizations leading the business process and workflow technologies, has been officially released the BPAF1.0 that is a standard format to record process instances' event logs according as the business process intelligence mining technologies have recently issued in the business process and workflow literature. The business process mining technologies consist of two groups of algorithms and their analysis techniques; one is to rediscover flow-oriented process-intelligence, such as control-flow, data-flow, role-flow, and actor-flow intelligence, from process instances' event logs, and the other has something to do with rediscovering relation-oriented process-intelligence like process-driven social networks and process-driven affiliation networks from the event logs. The current standardized format of BPAF1.0 aims at only supporting the control-flow oriented process-intelligence mining techniques, and so it is unable to properly support the relation-oriented process-intelligence mining techniques. Therefore, this paper tries to extend the BPAF1.0 so as to reasonably support the relation-oriented process-intelligence mining techniques, and the extended BPAF is termed BPAF2.0. Particularly, we have a plan to standardize the extended BPAF2.0 as not only the national standard specifications through the e-Business project group of TTA, but also the international standard specifications of WfMC.

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

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.

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.

Process mining is a field of computer science that deals with the discovery and analysis of process models based on automatically generated event logs. Currently, many companies are using this technology to optimize and improve their business processes. However, a discovered process model may be too detailed, sophisticated, and difficult for experts to understand. In this paper, we consider a problem of discovering the hierarchical business process model from a low-level event log, i. e., the problem of the automatic synthesis of more readable and understandable process models based on the data stored in the event logs of information systems. The discovery of better-structured and more readable process models is extensively studied in the framework of process mining research from different perspectives. In this paper, we present an algorithm for discovering hierarchical process models represented as two-level workflow Petri nets. The algorithm is based on predefined event partitioning so that this partitioning defines a sub-process corresponding to a high-level transition at the top level of a two-level net. In contrast to existing solutions, our algorithm does not impose restrictions on the process control flow and allows for concurrency and iterations.

Many present-day companies carry out a huge amount of daily operations through the use of their information systems without ever having done their own enterprise modeling. Business process mining is a well-proven solution which is used to discover the underlying business process models that are supported by existing information systems. Business process discovery techniques employ event logs as input, which are recorded by process-aware information systems. However, a wide variety of traditional information systems do not have any in-built mechanisms with which to collect events (representing the execution of business activities). Various mechanisms with which to collect events from non-process-aware information systems have been proposed in order to enable the application of process mining techniques to traditional information systems. Unfortunately, since business processes supported by traditional information systems are implicitly defined, correlating events into the appropriate process instance is not trivial. This challenge is known as the event correlation problem. This paper presents an adaptation of an existing event correlation algorithm and incorporates it into a technique in order to collect event logs from the execution of traditional information systems. The technique first instruments the source code to collect events together with some candidate correlation attributes. Based on several well-known design patterns, the technique provides a set of guidelines to support experts when instrumenting the source code. The event correlation algorithm is subsequently applied to the data set of events to discover the best correlation conditions, which are then used to create event logs. The technique has been semi-automated to facilitate its validation through an industrial case study involving a writer management system and a healthcare evaluation system. The study demonstrates that the technique is able to discover an appropriate correlation set and obtain well-formed event logs, thus enabling business process mining techniques to be applied to traditional information systems.

Evaluating and predicting overall process risk using event logs

Process discovery aims to discover process models from event logs to describe actual business processes. The quality of event logs has an impact on the quality of process models, so preprocessing methods can be used to improve the quality of event logs. Chaotic activities may exist in real business scenarios, and the occurrence of chaotic activities is independent of other activities in the process and can occur at any location in the event log at any frequency. Therefore, chaotic activities seriously affect the model quality of process discovery. Filtering chaotic activities in event logs can effectively improve the quality of event logs and thus improve the quality of process models. The traditional chaotic activity filtering algorithm makes it difficult to balance accuracy and time performance. Therefore, a direct method for filtering chaotic activities is proposed in this paper. By analyzing the relationship between activities, chaotic activities are identified in the log according to the characteristics of chaotic activities and the direct following relationship of activities as the judgment condition, and the filtering of chaotic activities in the event log is realized. In addition, this paper proposes an indirect chaotic activity filtering method, which identifies and filters chaotic activities in the log by analyzing the influence of the existence of different activities on the overall chaos degree of the log. The proposed method is compared with the traditional chaotic activity filtering method on several simulation/real data sets, and the accuracy and running time between the multi-group event logs and the process models generated before and after chaotic activity filtering are analyzed, further verifying the effectiveness and feasibility of the proposed method. By summarizing the experimental results, it is found that the accuracy of the proposed chaotic activity filtering methods is greater than that of the frequency-based filtering method and is close to that of the entropy-based chaotic activity filtering methods. Moreover, compared with other filtering methods used in the experiment, the chaotic activity filtering method proposed in this paper can improve the efficiency by 23.4% on average for simulation logs, and by 84.25% on average for real event logs. It is concluded that compared with other filtering methods, the proposed chaotic activity filtering methods have higher accuracy and can effectively improve the time performance of chaotic activity filtering. Therefore, the chaotic activity filtering method proposed in this paper can balance the accuracy and time performance, and can ensure the integrity of the filtered event log to a certain extent.