Automatic Conversion of Event Data to Event Logs Using CNN and Event Density Embedding

Enterprise resource planning (ERP) systems are often seen as viable sources of data for process mining analysis. To perform most of the existing process mining techniques, it is necessary to obtain a valid event log that is fully compliant with the eXtensible Event Stream (XES) standard. In ERP systems, such event logs are not available as the concept of business activity is missing. Extracting event data from an ERP database is not a trivial task and requires in-depth knowledge of the business processes and underlying data structure. Therefore, domain experts require proper techniques and tools for extracting event data from ERP databases. In this paper, we present the full specification of a domain-specific modeling language for facilitating the extraction of appropriate event data from transactional databases by domain experts. The modeling language has been developed to support complex ambiguous cases when using ERP systems. We demonstrate its applicability using a case study with real data and show that the language includes constructs that enable a domain expert to easily model data of interest in the log extraction step. The language provides sufficient information to extract and transform data from transactional ERP databases to the XES format.

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Spreadsheets for business process management

Business Process Management Journal ◽

10.1108/bpmj-10-2016-0190 ◽

2018 ◽

Vol 24 (1) ◽

pp. 105-127 ◽

Cited By ~ 8

Author(s):

Wil van der Aalst

Keyword(s):

Process Management ◽

Process Mining ◽

Event Data ◽

Data Set ◽

Content Type ◽

Event Logs ◽

Process Support ◽

Commercial Process ◽

Operational Processes ◽

Mining Tools

Purpose Process mining provides a generic collection of techniques to turn event data into valuable insights, improvement ideas, predictions, and recommendations. This paper uses spreadsheets as a metaphor to introduce process mining as an essential tool for data scientists and business analysts. The purpose of this paper is to illustrate that process mining can do with events what spreadsheets can do with numbers. Design/methodology/approach The paper discusses the main concepts in both spreadsheets and process mining. Using a concrete data set as a running example, the different types of process mining are explained. Where spreadsheets work with numbers, process mining starts from event data with the aim to analyze processes. Findings Differences and commonalities between spreadsheets and process mining are described. Unlike process mining tools like ProM, spreadsheets programs cannot be used to discover processes, check compliance, analyze bottlenecks, animate event data, and provide operational process support. Pointers to existing process mining tools and their functionality are given. Practical implications Event logs and operational processes can be found everywhere and process mining techniques are not limited to specific application domains. Comparable to spreadsheet software widely used in finance, production, sales, education, and sports, process mining software can be used in a broad range of organizations. Originality/value The paper provides an original view on process mining by relating it to the spreadsheets. The value of spreadsheet-like technology tailored toward the analysis of behavior rather than numbers is illustrated by the over 20 commercial process mining tools available today and the growing adoption in a variety of application domains.

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Detecting Complex Control-Flow Constructs for Choosing Process Discovery Techniques

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.l3914.119119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 1389-1393

Keyword(s):

Business Process ◽

Free Choice ◽

Process Mining ◽

Control Flow ◽

Process Models ◽

Event Data ◽

Process Discovery ◽

Event Logs ◽

Complex Process ◽

Mining Algorithms

Process models are the analytical illustration of an organization’s activity. They are very primordial to map out the current business process of an organization, build a baseline of process enhancement and construct future processes where the enhancements are incorporated. To achieve this, in the field of process mining, algorithms have been proposed to build process models using the information recorded in the event logs. However, for complex process configurations, these algorithms cannot correctly build complex process structures. These structures are invisible tasks, non-free choice constructs, and short loops. The ability of each discovery algorithm in discovering the process constructs is different. In this work, we propose a framework responsible of detecting from event logs the complex constructs existing in the data. By identifying the existing constructs, one can choose the process discovery techniques suitable for the event data in question. The proposed framework has been implemented in ProM as a plugin. The evaluation results demonstrate that the constructs can correctly be identified.

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Discovering Object-centric Petri Nets

Fundamenta Informaticae ◽

10.3233/fi-2020-1946 ◽

2020 ◽

Vol 175 (1-4) ◽

pp. 1-40

Author(s):

Wil M.P. van der Aalst ◽

Alessandro Berti

Keyword(s):

Petri Nets ◽

Petri Net ◽

Single Case ◽

Real Life ◽

Process Models ◽

Event Data ◽

Multiple Products ◽

Event Logs ◽

Different Types ◽

Complex Relationships

Techniques to discover Petri nets from event data assume precisely one case identifier per event. These case identifiers are used to correlate events, and the resulting discovered Petri net aims to describe the life-cycle of individual cases. In reality, there is not one possible case notion, but multiple intertwined case notions. For example, events may refer to mixtures of orders, items, packages, customers, and products. A package may refer to multiple items, multiple products, one order, and one customer. Therefore, we need to assume that each event refers to a collection of objects, each having a type (instead of a single case identifier). Such object-centric event logs are closer to data in real-life information systems. From an object-centric event log, we want to discover an object-centric Petri net with places that correspond to object types and transitions that may consume and produce collections of objects of different types. Object-centric Petri nets visualize the complex relationships among objects from different types. This paper discusses a novel process discovery approach implemented in PM4Py. As will be demonstrated, it is indeed feasible to discover holistic process models that can be used to drill-down into specific viewpoints if needed.

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Event Log Preprocessing for Process Mining: A Review

Applied Sciences ◽

10.3390/app112210556 ◽

2021 ◽

Vol 11 (22) ◽

pp. 10556

Author(s):

Heidy M. Marin-Castro ◽

Edgar Tello-Leal

Keyword(s):

Business Process ◽

Process Model ◽

Process Mining ◽

Data Cleaning ◽

Data Preprocessing ◽

Process Models ◽

Event Data ◽

Event Logs ◽

Event Log ◽

Preprocessing Technique

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 event data preprocessing and to identify relevant challenges around these techniques. We present a quantitative and qualitative analysis of the most popular techniques for event log preprocessing. We also study and present findings about how a preprocessing technique can improve a process mining task. We also discuss the emerging future challenges in the domain of data preprocessing, in the context of process mining. The results of this study reveal that the preprocessing techniques in process mining have demonstrated a high impact on the performance of the process mining tasks. The data cleaning requirements are dependent on the characteristics of the event logs (voluminous, a high variability in the set of traces size, changes in the duration of the activities. In this scenario, most of the surveyed works use more than a single preprocessing technique to improve the quality of the event log. Trace-clustering and trace/event level filtering resulted in being the most commonly used preprocessing techniques due to easy of implementation, and they adequately manage noise and incompleteness in the event logs.

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A self-portrayal of GI Junior Fellow Matthias Weidlich: Event-driven analysis of service processes

it - Information Technology ◽

10.1515/itit-2017-0035 ◽

2018 ◽

Vol 60 (1) ◽

pp. 51-54

Author(s):

Matthias Weidlich

Keyword(s):

Information Systems ◽

Focal Point ◽

Online Assessment ◽

Event Data ◽

Qualitative And Quantitative Analysis ◽

Qualitative And Quantitative ◽

Event Logs ◽

Event Driven ◽

Research Questions ◽

Source Of Information

Abstract In domains such as e-commerce, logistics, or healthcare, the conduct of service processes is widely supported by information systems and event data is generated continuously during process execution. Such event data constitutes a valuable source of information to monitor and improve the respective service processes. My research focuses on models and methods to support event-driven analysis of service processes. Specifically, I study how event logs produced by information systems are used to automatically construct models for qualitative and quantitative analysis. Aiming at online assessment and predictive analysis of a process' behaviour, I develop monitoring techniques that utilise streams of event data produced by diverse sources. Architectures that enable efficient handling of event streams are another focal point of my research. In this article, I outline some of the related research questions and highlight my recent results in these areas.

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