scholarly journals Efficient Time and Space Representation of Uncertain Event Data

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 285 ◽  
Author(s):  
Marco Pegoraro ◽  
Merih Seran Uysal ◽  
Wil M. P. van der Aalst
Keyword(s):  
Process Mining ◽  
Graph Representation ◽  
Space Representation ◽  
Event Data ◽  
New Approach ◽  
Performance Improvements ◽  
Order Of Magnitude ◽  
Normative Models ◽  
Operational Processes ◽  
Asymptotic Time

Process mining is a discipline which concerns the analysis of execution data of operational processes, the extraction of models from event data, the measurement of the conformance between event data and normative models, and the enhancement of all aspects of processes. Most approaches assume that event data is accurately captured behavior. However, this is not realistic in many applications: data can contain uncertainty, generated from errors in recording, imprecise measurements, and other factors. Recently, new methods have been developed to analyze event data containing uncertainty; these techniques prominently rely on representing uncertain event data by means of graph-based models explicitly capturing uncertainty. In this paper, we introduce a new approach to efficiently calculate a graph representation of the behavior contained in an uncertain process trace. We present our novel algorithm, prove its asymptotic time complexity, and show experimental results that highlight order-of-magnitude performance improvements for the behavior graph construction.

scholarly journals Spreadsheets for business process management

2018 ◽  
Vol 24 (1) ◽  
pp. 105-127 ◽  
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.

scholarly journals Towards a Domain-Specific Modeling Language for Extracting Event Logs from ERP Systems

2021 ◽  
Vol 11 (12) ◽  
pp. 5476
Author(s):  
Ana Pajić Simović ◽  
Slađan Babarogić ◽  
Ognjen Pantelić ◽  
Stefan Krstović
Keyword(s):  
Process Mining ◽  
Modeling Language ◽  
Sufficient Information ◽  
Event Data ◽  
Erp Systems ◽  
Domain Experts ◽  
Domain Specific ◽  
Event Logs ◽  
Domain Specific Modeling ◽  
Specific Modeling

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.

Half division two-dimensional method for simple sinusoidal quantities parameters accurate measurement

2021 ◽  
pp. 16-21
Author(s):  
Kirill Yu. Solomentsev ◽  
Vyacheslav I. Lachin ◽  
Aleksandr E. Pasenchuk
Keyword(s):  
Measuring Instruments ◽  
Measured Signal ◽  
Two Dimensional ◽  
Measuring Device ◽  
New Approach ◽  
Precision Measuring ◽  
Useful Signal ◽  
Midpoint Method ◽  
Order Of Magnitude ◽  
Current Measuring

Several variants of half division two-dimensional method are proposed, which is the basis of a fundamentally new approach for constructing measuring instruments for sinusoidal or periodic electrical quantities. These measuring instruments are used in the diagnosis of electric power facilities. The most general variant, called midpoint method, is considered. The proposed midpoint method allows you to measure much smaller than using widespread methods, alternating currents or voltages, especially when changing the amplitude of the measured signal in very wide ranges, by 1–2 orders of magnitude. It is shown that using the midpoint method it is possible to suppress sinusoidal or periodic interference in the measuring path, in particular, to measure small alternating current when sinusoidal or periodic interference is 1–2 orders of magnitude higher than the useful signal. Based on the results of comparative tests, it was found that the current measuring device implementing the midpoint method is an order of magnitude more sensitive than the currently used high-precision measuring instruments.

Continual Process Improvement Based on ITIL and Process Mining

2011 ◽  
pp. 145-167
Author(s):  
Kerstin Gerke ◽  
Konstantin Petruch ◽  
Gerrit Tamm
Keyword(s):  
Process Improvement ◽  
Service Management ◽  
Business Processes ◽  
Reference Model ◽  
Process Mining ◽  
It Services ◽  
New Approach ◽  
Customer Support ◽  
It Service ◽  
Key Indicators

The inherent quality of business processes and their support through information technology (IT) increasingly plays a significant role in the economic success of an organization. More and more business processes are supported through IT services. In order to provide IT services with the required quality and at minimum costs, the importance of effective and efficient IT service management (ITSM) processes is crucial. In this contribution, the authors present a new approach, which allows the continual process improvement by the interconnection of the ITIL reference model, the 7-step improvement process, and process mining. On the basis of the reference model, to-be processes are set and key indicators are determined. As-is processes and their key indicators derived by process mining are subsequently compared to the to-be processes. This new approach enables the design, control, and improvement of ITIL based customer support processes, which will be trialed in practice.

Recurrent Process Mining with Live Event Data

2018 ◽  
pp. 178-190 ◽  
Author(s):  
Alifah Syamsiyah ◽  
Boudewijn F. van Dongen ◽  
Wil M. P. van der Aalst
Keyword(s):  
Process Mining ◽  
Event Data ◽  
Recurrent Process

scholarly journals Genetic Programming for Evolving Similarity Functions for Clustering: Representations and Analysis

2020 ◽  
Vol 28 (4) ◽  
pp. 531-561 ◽  
Author(s):  
Andrew Lensen ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Similarity Measures ◽  
Small Subset ◽  
Similarity Functions ◽  
New Approach ◽  
Performance Improvements ◽  
Consistent Performance ◽  
High Dimensional Datasets

Clustering is a difficult and widely studied data mining task, with many varieties of clustering algorithms proposed in the literature. Nearly all algorithms use a similarity measure such as a distance metric (e.g., Euclidean distance) to decide which instances to assign to the same cluster. These similarity measures are generally predefined and cannot be easily tailored to the properties of a particular dataset, which leads to limitations in the quality and the interpretability of the clusters produced. In this article, we propose a new approach to automatically evolving similarity functions for a given clustering algorithm by using genetic programming. We introduce a new genetic programming-based method which automatically selects a small subset of features (feature selection) and then combines them using a variety of functions (feature construction) to produce dynamic and flexible similarity functions that are specifically designed for a given dataset. We demonstrate how the evolved similarity functions can be used to perform clustering using a graph-based representation. The results of a variety of experiments across a range of large, high-dimensional datasets show that the proposed approach can achieve higher and more consistent performance than the benchmark methods. We further extend the proposed approach to automatically produce multiple complementary similarity functions by using a multi-tree approach, which gives further performance improvements. We also analyse the interpretability and structure of the automatically evolved similarity functions to provide insight into how and why they are superior to standard distance metrics.

scholarly journals Information-preserving abstractions of event data in process mining

2019 ◽  
Vol 62 (3) ◽  
pp. 1143-1197
Author(s):  
Sander J. J. Leemans ◽  
Dirk Fahland
Keyword(s):  
Process Mining ◽  
Event Data

The Formation of Molecules in Three Atom Collisions using the Eikonal Approximation

1976 ◽  
Vol 31 (12) ◽  
pp. 1489-1499
Author(s):  
E. Czuchaj
Keyword(s):  
Experimental Data ◽  
Numerical Calculation ◽  
Rate Constant ◽  
Recombination Rate ◽  
Eikonal Approximation ◽  
New Approach ◽  
Order Of Magnitude ◽  
Recombination Rate Constant ◽  
Good Agreement

Abstract A new approach to the calculation of a teratomic recombination rate constant k(T) has been dem-onstrated. An expression for k(T) has been obtained in the eikonal approximation. The numerical calculation has been carried out for the Rb*-Xe system. Good agreement in the order of magnitude between the present results and the experimental data of Carrington et al. has been obtained.

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