Merging Event Logs for Process Mining: A Rule Based Merging Method and Rule Suggestion Algorithm (original) (raw)

Event Log Preprocessing for Process Mining: A Review

Applied Sciences

Process Mining allows organizations to obtain actual business process models from event logs (discovery), to compare the event log or the resulting process model in the discovery task with the existing reference model of the same process (conformance), and to detect issues in the executed process to improve (enhancement). An essential element in the three tasks of process mining (discovery, conformance, and enhancement) is data cleaning, used to reduce the complexity inherent to real-world event data, to be easily interpreted, manipulated, and processed in process mining tasks. Thus, new techniques and algorithms for event data preprocessing have been of interest in the research community in business process. In this paper, we conduct a systematic literature review and provide, for the first time, a survey of relevant approaches of event data preprocessing for business process mining tasks. The aim of this work is to construct a categorization of techniques or methods related to eve...

Discovering Process Model from Event Logs by Considering Overlapping Rules

Proceeding of the Electrical Engineering Computer Science and Informatics

Process Mining is a technique to automatically discover and analyze business processes from event logs. Discovering concurrent activities often uses process mining since there are many of them contained in business processes. Since researchers and practitioners are giving attention to the process discovery (one of process mining techniques), then the best result of the discovered process models is a must. Nowadays, using process execution data in the past, process models with rules underlying decisions in processes can be enriched, called decision mining. Rules defined over process data specify choices between multiple activities. One out of multiple activities is allowed to be executed in existing decision mining methods or it is known as mutually-exclusive rules. Not only mutually-exclusive rules, but also fully deterministic because all factors which influence decisions are recorded. However, because of non-determinism or incomplete information, there are some cases that are overlapping in process model. Moreover, the rules which are generated from existing method are not suitable with the recorded data. In this paper, a discovery technique for process model with data by considering the overlapping rules from event logs is presented. Discovering overlapping rules uses decision tree learning techniques, which fit the recorded data better than the existing method. Process model discovery from event logs is generated using Modified Time-Based Heuristics Miner Algorithm. Last, online book store management process model is presented in High-level BPMN Process Model.

Process Mining: A Recent Framework for Extracting a Model from Event Logs

Atas da 17ª Conferência da Associação Portuguesa de Sistemas de Informação, 2017

Business Process Management (BPM) is a well-known discipline, with roots in previous theories related with optimizing management and improving businesses results. One can trace BPM back to the beginning of this century, although it was in more recent years when it gained a special focus of attention. Usually, traditional BPM approaches start from top and analyse the organization according some known rules from its structure or from the type of business. Process Mining (PM) is a completely different approach, since it aims to extract knowledge from event logs, which are widely present in many of today's organizations. PM uses specialized data-mining algorithms, trying to uncover patterns and trends in these logs, and it is an alternative approach where formal process specification is not easily obtainable or is not cost-effective. This paper makes a literature review of major works issued about this theme.

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.

Improving Process Discovery Algorithms Using Event Concatenation

IEEE Access

Process mining is the discipline of analyzing and improving processes which are known as an event log. The real-life event log contains noise, infrequent behaviors, and numerous concurrency, in effect the generated process model through process discovery algorithms will be inefficient and complex. Shortcomings in an event log result in current process discovery algorithms failing to pre-process data and describe real-life phenomena. Existing process mining algorithms are limited based on the algorithm's filtering, parameters, and pre-defined features. It is critical to use a high-quality event log to generate a robust process model. However, pre-processing of the event log is mostly cumbersome and is a challenging procedure. In this paper, we propose a novel pre-processing step aimed to obtain superior quality event log from a set of raw data, consequently a better performing process model. The proposed approach concatenates events which hold concurrent relations based on a probability algorithm, producing simpler and accurate process models. This proposed pre-processing step is based on the probability of the frequency of concurrent events. The performance of the pre-processing approach is evaluated on 18 real-life benchmark datasets that are publicly available. We show that the proposed pre-processing framework significantly reduces the complexity of the process model and improves the model's F-Measure.

Semi-Automated Approach for Building Event Logs for Process Mining from Relational Database

Applied Sciences

Process mining is a novel alternative that uses event logs to discover, monitor, and improve real business processes through knowledge extraction. Event logs are a prerequisite for any process mining technique. The extraction of event data and event log building is a complex and time-intensive process, with human participation at several stages of the procedure. In this paper, we propose a framework to semi-automatically build an event log based on the XES standard from relational databases. The framework comprises the stages of requirements identification, event log construction, and event log evaluation. In the first stage, the data is interpreted to identify the relationship between the columns and business process activities, then the business process entities are defined. In the second stage, the hierarchical structure of the event log is specified. Likewise, a formal rule set is defined to allow mapping the database columns with the attributes specified in the event log struct...

Towards Cross-Organizational Process Mining in Collections of Process Models and their Executions

Variants of the same process may be encountered in different organizations, e.g., any municipality will have a process to handle building permits. New paradigms such as Software-as-a-Service (SaaS) and Cloud Computing stimulate organizations to share a BPM infrastructure. The shared infrastructure has to support many processes and their variants. Dealing with such large collections of similar process models for multiple organizations is challenging. However, a shared BPM infrastructure also enables cross-organizational process mining. Since events are recorded in a unified way, it is possible to cross-correlate process models and the actual observed behavior in different organizations. This paper presents a novel approach to compare collections of process models and their events logs. The approach is used to compare processes in different Dutch municipalities.

Process Mining: A 360 Degree Overview

Process mining enables organizations to uncover their actual processes, provide insights, diagnose problems, and automatically trigger corrective actions. Process mining is an emerging scientific discipline positioned at the intersection between process science and data science. The combination of process modeling and analysis with the event data present in today's information systems provides new means to tackle compliance and performance problems. This chapter provides an overview of the field of process mining introducing the different types of process mining (e.g., process discovery and conformance checking) and the basic ingredients, i.e., process models and event data. To prepare for later chapters, event logs are introduced in detail (including pointers to standards for event data such as XES and OCEL). Moreover, a brief overview of process mining applications and software is given.

Cross-organizational collaborative workflow mining from a multi-source log

Decision Support Systems, 2013

Today's enterprise business processes become increasingly complex given that they are often executed by geographically dispersed partners or different organizations. Designing and modeling such a cross-organizational workflow is a complicated, time-consuming process and requires that a designer has extensive experience. Workflow logs captured by different cross-organizational systems provide a very valuable source of information on how business processes are executed in reality and thus can be used to derive workflow models through process mining. In this paper, we investigate the application of process mining for workflow integration based on the concept of RM_WF_Net, a type of Petri net extended with resource and message factors. Four coordination patterns are defined for workflow integration. A process mining approach is presented to discover the coordination patterns between different organizations and the workflow models in different organizations from the running logs containing the information about resource allocation. A process integration approach is then presented to obtain the model for a cross-organizational workflow based on the model mined for each organization and the coordination patterns between different organizations.