Workflow mining: discovering process models from event logs

Contemporary workflow management systems are driven by explicit process models, i.e., a completely specified workflow design is required in order to enact a given workflow process. Creating a workflow design is a complicated time-consuming process and, typically, there are discrepancies between the actual workflow processes and the processes as perceived by the management. Therefore, we have developed techniques for discovering workflow models. The starting point for such techniques is a so-called "workflow log" containing information about the workflow process as it is actually being executed. We present a new algorithm to extract a process model from such a log and represent it in terms of a Petri net. However, we also demonstrate that it is not possible to discover arbitrary workflow processes. We explore a class of workflow processes that can be discovered. We show that the /spl alpha/-algorithm can successfully mine any workflow represented by a so-called SWF-net.     

Sequence partitioning for process mining with unlabeled event logs

Finding the case id in unlabeled event logs is arguably one of the hardest challenges in process mining research. While this problem has been addressed with greedy approaches, these usually converge to sub-optimal solutions. In this work, we describe an approach to perform complete search over the search space. We formulate the problem as a matter of finding the minimal set of patterns contained in a sequence, where patterns can be interleaved but do not have repeating symbols. This represents a new problem that has not been previously addressed in the literature, with NP-hard variants and conjectured NP-completeness. We solve it in a stepwise manner, by generating and verifying a list of candidate solutions. The techniques, introduced to address various subtasks, can be applied independently for solving more specific problems. The approach has been implemented and applied in a case study with real data from a business process supported in a software application.     

From event logs to goals: a systematic literature review of goal-oriented process mining

Process mining helps infer valuable insights about business processes using event logs, whereas goal modeling focuses on the representation and analysis of competing goals of stakeholders and systems. Although there are clear benefits in mining the goals of existing processes, goal-oriented approaches that consider logs during model construction are still rare. Process mining techniques, when generalizing large instance-level data into process models, can be considered as a data-driven complement to use case/scenario elicitation. Requirements engineers can exploit process mining techniques to find new system or process requirements in order to align current practices and desired ones. This paper provides a systemic literature review, based on 24 papers rigorously selected from four popular search engines in 2018, to assess the state of goal-oriented process mining. Through two research questions, the review highlights that the use of process mining in association with goals does not yet have a coherent line of research, whereas intention mining (where goal models are mined) shows a meaningful trace of research. Research about performance indicators measuring goals associated with process mining is also sparse. Although the number of publications in process mining and goal modeling is trending up, goal mining and goal-oriented process mining remain modest research areas. Yet, synergetic effects achievable by combining goals and process mining can potentially augment the precision, rationality and interpretability of mined models and eventually improve opportunities to satisfy system stakeholders.

     

Merging event logs for process mining: A rule based merging method and rule suggestion algorithm

In an inter-organizational setting the manual construction of process models is challenging because the different people involved have to put together their partial knowledge about the overall process. Process mining, an automated technique to discover and analyze process models, can facilitate the construction of inter-organizational process models. This paper presents a technique to merge the input data of the different partners of an inter-organizational process in order to serve as input for process mining algorithms. The technique consists of a method for configuring and executing the merge and an algorithm that searches for links between the data of the different partners and that suggests rules to the user on how to merge the data. Tool support is provided in the open source process mining framework ProM. The method and the algorithm are tested using two artificial and three real life datasets that confirm their effectiveness and efficiency.     

基于分布式工作流日志挖掘的流程识别算法

为了识别出分布式环境下工作流的执行流程,对分布式工作流管理系统进行了研究,通过对分布式工作流执行站点中XML格式的系统运行日志进行分析,提出了一种增量式工作流挖掘算法。该算法通过对大量工作流执行站点中的活动执行时间序列进行分析与合并,从而重构出分布式环境下的工作流模型。该算法主要由两个重要部分组成:一个是时间序列挖掘算法,用于从工作流执行日志中挖掘出活动间的执行时间序列;另一个是工作流程识别算法,在时间序列挖掘算法得出的活动执行时间序列基础上,识别出结构化的工作流模型。通过实例结果表明了该算法的有效性。     

A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs

Process mining is the research domain that is dedicated to the a posteriori analysis of business process executions. The techniques developed within this research area are specifically designed to provide profound insight by exploiting the untapped reservoir of knowledge that resides within event logs of information systems. Process discovery is one specific subdomain of process mining that entails the discovery of control-flow models from such event logs. Assessing the quality of discovered process models is an essential element, both for conducting process mining research as well as for the use of process mining in practice. In this paper, a multi-dimensional quality assessment is presented in order to comprehensively evaluate process discovery techniques. In contrast to previous studies, the major contribution of this paper is the use of eight real-life event logs. For instance, we show that evaluation based on real-life event logs significantly differs from the traditional approach to assess process discovery techniques using artificial event logs. In addition, we provide an extensive overview of available process discovery techniques and we describe how discovered process models can be assessed regarding both accuracy and comprehensibility. The results of our study indicate that the HeuristicsMiner algorithm is especially suited in a real-life setting. However, it is also shown that, particularly for highly complex event logs, knowledge discovery from such data sets can become a major problem for traditional process discovery techniques.     

A general process mining framework for correlating,predicting and clustering dynamic behavior based on event logs

Process mining can be viewed as the missing link between model-based process analysis and data-oriented analysis techniques. Lion׳s share of process mining research has been focusing on process discovery (creating process models from raw data) and replay techniques to check conformance and analyze bottlenecks. These techniques have helped organizations to address compliance and performance problems. However, for a more refined analysis, it is essential to correlate different process characteristics. For example, do deviations from the normative process cause additional delays and costs? Are rejected cases handled differently in the initial phases of the process? What is the influence of a doctor׳s experience on treatment process? These and other questions may involve process characteristics related to different perspectives (control-flow, data-flow, time, organization, cost, compliance, etc.). Specific questions (e.g., predicting the remaining processing time) have been investigated before, but a generic approach was missing thus far. The proposed framework unifies a number of approaches for correlation analysis proposed in literature, proposing a general solution that can perform those analyses and many more. The approach has been implemented in ProM and combines process and data mining techniques. In this paper, we also demonstrate the applicability using a case study conducted with the UWV (Employee Insurance Agency), one of the largest “administrative factories” in The Netherlands.     

Turning event logs into process movies: animating what has really happened

Today’s information systems log vast amounts of data. These collections of data (implicitly) describe events (e.g. placing an order or taking a blood test) and, hence, provide information on the actual execution of business processes. The analysis of such data provides an excellent starting point for business process improvement. This is the realm of process mining, an area which has provided a repertoire of many analysis techniques. Despite the impressive capabilities of existing process mining algorithms, dealing with the abundance of data recorded by contemporary systems and devices remains a challenge. Of particular importance is the capability to guide the meaningful interpretation of “oceans of data” by process analysts. To this end, insights from the field of visual analytics can be leveraged. This article proposes an approach where process states are reconstructed from event logs and visualised in succession, leading to an animated history of a process. This approach is customisable in how a process state, partially defined through a collection of activity instances, is visualised: one can select a map and specify a projection of events on this map based on the properties of the events. This paper describes a comprehensive implementation of the proposal. It was realised using the open-source process mining framework ProM. Moreover, this paper also reports on an evaluation of the approach conducted with Suncorp, one of Australia’s largest insurance companies.     

On process model synthesis based on event logs with noise

Process mining is a new emerging discipline related to process management, formal process modelling, and data mining. One of the main tasks of process mining is model synthesis (discovery) based on event logs. A wide range of algorithms for process model discovery, analysis, and enhancement is developed. The real-life event logs often contain noise of different types. In this paper, we describe the main causes of noise in the event logs and study the effect of noise on the performance of process discovery algorithms. The experimental results of application of the main process discovery algorithms to artificial event logs with noise are provided. Specially generated event logs with noise of different types were processed using the four basic discovery techniques. Although modern algorithms can cope with some types of noise, in most cases, their use does not lead to obtaining a satisfactory result. Thus, there is a need for more sophisticated algorithms to deal with noise of different types.     

A new model for discovering process trees from event logs

Process mining techniques aim at extracting knowledge from event logs. One of the most important tasks in process mining is process model discovery. In discovering process models, an algorithm is designed to build a process model from a given event log. In this paper, a new model to discover process models has been proposed. A combination of Genetic Algorithm and Simulated Annealing has been used in this model. Genetic Algorithms has previously been used in this context. Previous approaches had drawbacks in fitness evaluation that misguided the algorithm. Another problem was that the quality of the candidates, in the population, was low such that it reduced the chance of finding a perfect answer. In this paper, a new fitness measure has been proposed to evaluate process models based on event logs. Moreover SA has been used to improve the quality of candidates in the population. It has been demonstrated that the proposed model outperformed in terms of rediscovering process models, compared to other approaches which are proposed in the literature, which was the result of better fitness evaluation and increased quality of individuals,. It came to conclusion that using GA and SA in combination with each other can be effective in this context.     

Workflow simulation for operational decision support using event graph through process mining

It is increasingly common to see computer-based simulation being used as a vehicle to model and analyze business processes in relation to process management and improvement. While there are a number of business process management (BPM) and business process simulation (BPS) methodologies, approaches and tools available, it is more desirable to have a systemic BPS approach for operational decision support, from constructing process models based on historical data to simulating processes for typical and common problems. In this paper, we have proposed a generic approach of BPS for operational decision support which includes business processes modeling and workflow simulation with the models generated. Processes are modeled with event graphs through process mining from workflow logs that have integrated comprehensive information about the control-flow, data and resource aspects of a business process. A case study of a credit card application is presented to illustrate the steps involved in constructing an event graph. The evaluation detail is also given in terms of precision, generalization and robustness. Based on the event graph model constructed, we simulate the process under different scenarios and analyze the simulation logs for three generic problems in the case study: 1) suitable resource allocation plan for different case arrival rates; 2) teamwork performance under different case arrival rates; and 3) evaluation and prediction for personal performances. Our experimental results show that the proposed approach is able to model business processes using event graphs and simulate the processes for common operational decision support which collectively play an important role in process management and improvement.     

Leveraging BPMN particularities to improve traceability links recovery among requirements and BPMN models

Requirements Engineering - Traceability links recovery (TLR) has been a topic of interest for many years. However, TLR approaches are based on the latent semantics of the software artifacts, and...     

Discovering process models from event multiset

The aim of process mining is to discover the process model from the event log which is recorded by the information system. Typical steps of process mining algorithm can be described as: (1) generating event traces from event log, (2) analyzing event traces and obtaining ordering relations of tasks, (3) generating process model with ordering relations of tasks. The first two steps could be very time consuming involving millions of events and thousands of event traces. This paper presents a novel algorithm (λ-algorithm) which almost eliminates these two steps in generating event traces from event log and analyzing event traces so as to reduce the performance of process mining algorithm. Firstly, we retrieve the event multiset (input data of algorithm marked as MS) which records the frequency of each event but ignores their orders when extracted from event logs. The event in event multiset contains the information of post-activities. Secondly, we obtain ordering relations from event multiset. The ordering relations contain causal dependency, potential parallelism and non-potential parallelism. Finally, we discover a process models with ordering relations. The complexity of λ-algorithm is only bound up with the event classes (the set of events in event logs) that has significantly improved the performance of existing process mining algorithms and is expected to be more practical in real-world process mining based on event logs, as well as being able to detect SWF-nets, short-loops and most of implicit dependency (generated by non-free choice constructions).     

A policy-based process mining framework: mining business policy texts for discovering process models

Many organizations use business policies to govern their business processes, often resulting in huge amounts of policy documents. As new regulations arise such as Sarbanes-Oxley, these business policies must be modified to ensure their correctness and consistency. Given the large amounts of business policies, manually analyzing policy documents to discover process information is very time-consuming and imposes excessive workload. In order to provide a solution to this information overload problem, we propose a novel approach named Policy-based Process Mining (PBPM) to automatically extracting process information from policy documents. Several text mining algorithms are applied to business policy texts in order to discover process-related policies and extract such process components as tasks, data items, and resources. Experiments are conducted to validate the extracted components and the results are found to be very promising. To the best of our knowledge, PBPM is the first approach that applies text mining towards discovering business process components from unstructured policy documents. The initial research results presented in this paper will require more research efforts to make PBPM a practical solution.     

How do humans inspect BPMN models: an exploratory study

Even though considerable progress regarding the technical perspective on modeling and supporting business processes has been achieved, it appears that the human perspective is still often left aside. In particular, we do not have an in-depth understanding of how process models are inspected by humans, what strategies are taken, what challenges arise, and what cognitive processes are involved. This paper contributes toward such an understanding and reports an exploratory study investigating how humans identify and classify quality issues in BPMN process models. Providing preliminary answers to initial research questions, we also indicate other research questions that can be investigated using this approach. Our qualitative analysis shows that humans adapt different strategies on how to identify quality issues. In addition, we observed several challenges appearing when humans inspect process models. Finally, we present different manners in which classification of quality issues was addressed.     

Finitely recursive process models for discrete event systems

A class of discrete-event models called finitely recursive processes (FRP) is introduced. These models are motivated by problems in supervisory control, while some of the formal structure is based on C.A.R. Hoare's (1985) communicating sequential processes. Although only preliminary work is presented, it is believed that for many problems it offers a formalism that is superior to that of finite-state machines recently introduced in control theory. Simple examples illustrate the calculus of operators for FRPs. A more complex exercise is worked out to show how the formalism is used to construct a scheme for handling connections in a data network. This preliminary work needs to be extended in several directions, especially in directions that can assist implementation of real controllers based on the FRP formalism. Some specific suggestions towards this end are made     

A novel approach for process mining based on event types

Despite the omnipresence of event logs in transactional information systems (cf. WFM, ERP, CRM, SCM, and B2B systems), historic information is rarely used to analyze the underlying processes. Process mining aims at improving this by providing techniques and tools for discovering process, control, data, organizational, and social structures from event logs, i.e., the basic idea of process mining is to diagnose business processes by mining event logs for knowledge. Given its potential and challenges it is no surprise that recently process mining has become a vivid research area. In this paper, a novel approach for process mining based on two event types, i.e., START and COMPLETE, is proposed. Information about the start and completion of tasks can be used to explicitly detect parallelism. The algorithm presented in this paper overcomes some of the limitations of existing algorithms such as the α-algorithm (e.g., short-loops) and therefore enhances the applicability of process mining.

Web usage mining: extracting unexpected periods from web logs

Existing Web usage mining techniques are currently based on an arbitrary division of the data (e.g. “one log per month”) or guided by presumed results (e.g. “what is the customers’ behaviour for the period of Christmas purchases?”). These approaches have two main drawbacks. First, they depend on the above-mentioned arbitrary organization of data. Second, they cannot automatically extract “seasonal peaks” from among the stored data. In this paper, we propose a specific data mining process (in particular, to extract frequent behaviour patterns) in order to reveal the densest periods automatically. From the whole set of possible combinations, our method extracts the frequent sequential patterns related to the extracted periods. A period is considered to be dense if it contains at least one frequent sequential pattern for the set of users connected to the website in that period. Our experiments show that the extracted periods are relevant and our approach is able to extract both frequent sequential patterns and the associated dense periods.