Wyniki wyszukiwania - Biblioteka Nauki

1

Process mining: usprawnienie przebiegów procesów biznesowych w oparciu o analizę logów systemu zarządzania przepływem pracy - analiza przypadku

100%

Gaweł B.

Zagadnienia Techniczno-Ekonomiczne

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2005

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tom T. 50, z. 1

19-28

PL

Proces biznesowy składa się z wykonywanych przez agentów czynności podlegających ograniczeniom kolejnościowym oraz czasowym, których przekroczenie powoduje uruchomienie odpowiednich scenariuszy ratunkowych lub zakończenie procesu. W artykule dokonano próby zastosowania process miningu w metodzie dynamicznego ustalania żądanych czasów zakończenia zadań w systemie informatycznym koordynującym przepływ procesów usługowych w średnim przedsiębiorstwie.

EN

Business processes consist of multiple activities, and their enactment is carried out by human agents and software systems. Typically, business processes and activities constituting them have deadline. When activity misses its deadline, special action may be triggered. In this article I try to apply process mining to deadline assignment problem in workflow management systems in middle size company.

2

Discovering Object-centric Petri Nets

94%

van der Aalst W. M. P. , Berti A.

Fundamenta Informaticae

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2020

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tom Vol. 175, nr 1-4

1--40

EN

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.

3

EVENT LOG STANDARDS IN BPM DOMAIN

94%

Gontar B. , Gontar Z.

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nr 4

293-304

EN

Automated Business Process Discovery (alias Process Intelligence, Process Analysis, Workflow Mining or Process Mining) includes methods, standards and tools to support the discovery and analysis of operational business processes. Paper aims to review ongoing standards for storing and managing event log data, which is starting point for process mining, and verify in what range chosen standards meet the needs of event log analysis. We discuss CWAD, BPAF, PROV-DM, PROV-O, MXML, SA-MXML, and XES formats. Findings of the paper are based on the literature review and analysis of selected software.

4

Lucent Process Models and Translucent Event Logs

94%

van der Aalst W. M. P.

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2019

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tom Vol. 169, nr 1-2

151--177

EN

A process model is lucent if no two reachable states are enabling the same set of activities. An event log is translucent if each event carries information about the set of activities enabled when the event occurred (normally one only sees the activity performed). Both lucency and translucency focus on the set of enabled activities and are therefore related. Surprisingly, these notions have not been investigated before. This paper aims to (1) characterize process models that are lucent, (2) provide a discovery approach to learn process models from translucent event logs, and (3) relate lucency and translucency. Lucency is defined both in terms of automata and Petri nets. A marked Petri net is lucent if there are no two different reachable markings enabling the same set of transitions, i.e., states are fully characterized by the transitions they enable. We will also provide a novel process discovery technique starting from a translucent event log. It turns out that information about the set of activities is extremely valuable for process discovery. We will provide sufficient conditions to ensure that the discovered model is lucent and show that a translucent event log sampled from a lucent process model can be used to rediscover the original model. We anticipate new analysis techniques exploiting lucency. Moreover, as shown in this paper, translucent event logs provide valuable information that can be exploited by a new breed to process mining techniques.

5

Wsparcie operacyjne w transporcie w kontekście process mining

84%

Dramski M.

Logistyka

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2015

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tom nr 4

7383--7390, CD2

PL

Niniejszy artykuł skupia się na problemie wsparcia operacyjnego w procesach transportowych. Jest on rozwinięciem prac prowadzonych na Akademii Morskiej w Szczecinie prowadzonych w kierunku optymalizacji trasy statku na akwenie ograniczonym. Dotychczas zadanie to sprowadzano do rozwiązania problemu w sensie geometrycznym. Należy jednak zauważyć, że proces transportowy i jego optymalizacja to nie tylko znalezienie najkrótszej drogi, najniższych kosztów itp. Aby zapewnić wymaganą optymalność należy również zapewnić odpowiednie wsparcie operacyjne, polegające na detekcji zaistniałych problemów, dokonywaniu prognoz czy wydawaniu odpowiednich zaleceń. Zwykłe techniki związane z analizą danych takie jak data mining czy Business Intelligence skupiają się na opisie systemu, ale nie analizują związku przyczynowo-skutkowego, który doprowadził do określonego stanu. Odpowiedzią jest tutaj process mining.

EN

This paper focuses on the operational support in transport processes. This is an extension of researches that have been carried out at the Maritime University of Szczecin in the area of optimization of the ship route in restricted area. Until now this problem has been only considered as a geometrical one, but it has to be clear that to ensure the optimal path, optimal transport costs etc. it is necessary to keep the operational support such detection, prediction and recommendations. Usual data processing techniques such data mining or Business Intelligence focus only on the simplified description of the system, but don't analyze the whole process which has lead to the current state of the system. Here the process mining will be the answer.

6

Process Discovery and Conformance Checking Using Passages

83%

van der Aalst W. M. P. , Verbeek H. M. W.

Fundamenta Informaticae

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2014

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tom Vol. 131, nr 1

103--138

EN

The two most prominent process mining tasks are process discovery (i.e., learning a process model from an event log) and conformance checking (i.e., diagnosing and quantifying differences between observed and modeled behavior). The increasing availability of event data makes these tasks highly relevant for process analysis and improvement. Therefore, process mining is considered to be one of the key technologies for Business Process Management (BPM). However, as event logs and process models grow, process mining becomes more challenging. Therefore, we propose an approach to decompose process mining problems into smaller problems using the notion of passages. A passage is a pair of two non-empty sets of activities (X, Y) such that the set of direct successors of X is Y and the set of direct predecessors of Y is X. Any Petri net can be partitioned using passages. Moreover, process discovery and conformance checking can be done per passage and the results can be aggregated. This has advantages in terms of efficiency and diagnostics. Moreover, passages can be used to distribute process mining problems over a network of computers. Passages are supported through ProM plug-ins that automatically decompose process discovery and conformance checking tasks.

7

Practical Aspects of Event Logs Creation for Industrial Process Modelling

83%

Trzcionkowska A. , Brzychczy E.

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tom Vol. 1, Iss. 1

77--83

EN

In the paper we address the challenge of applying process mining techniques for discovering models of underground mining operations based on a sensor data. The paper presents practical approach of creation an event log based on industrial sensors data gathered in an underground mine monitoring systems. The proposed approach enables to generate event logs at different generalization levels based on several numbers of discovered stages of devices performance. For discovering process stages data mining techniques such as exploratory data analysis, clustering and classification have been applied. Created event log has been used in one of the process mining tasks - process model discovery.

8

Enhancing business process event logs with software failure data

83%

Gruszczyński K.

|

2019

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tom Vol. 8, No 1

27--32

EN

Process mining techniques allow for the analysis of the real process flow. This flow might be disturbed for many reasons, including software failures. It is also possible for failure occurrence to be the consequence of the faulty process execution. A method for measuring the harmfulness of the software failure regarding business processes executed by the user would be a valuable asset for quality and reliability improvement. In this paper, we take the first step towards developing this method by providing a tool for enhancing XES event logs with failure data. We begin with an introduction to this topic and background analysis in the field of failure classification and process mining techniques supporting failure analysis. Then we present our method for merging operational and failure data. By carrying out a case study based on real data, we evaluate our tool and present the aim of our future work.

9

Light Region-based Techniques for Process Discovery

83%

Solé M. , Carmona J.

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2011

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tom Vol. 113, nr 3/4

343-376

EN

A central problem in the area of Process Mining is to obtain a formal model that represents selected behavior of a system. The theory of regions has been applied to address this problem, enabling the derivation of a Petri net whose language includes a set of traces. However, when dealing with real-life systems, the available tool support for performing such a task is unsatisfactory, due to the complex algorithms that are required. In this paper, the theory of regions is revisited to devise a novel technique that explores the space of regions by combining the elements of a region basis. Due to its light space requirements, the approach can represent an important step for bridging the gap between the theory of regions and its industrial application. Experimental results show that there is improvement in orders of magnitude in comparison with state-of-the-art tools for the same task.

10

Process Modelling Based on Event Logs

83%

Brzychczy E.

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tom Vol. 1, Iss. 1

385--392

EN

Process modelling is a very important stage in a Business Process Management cycle enabling process analysis and its redesign. Many sources of information for process modelling purposes exist. It may be an analysis of documentation related directly or indirectly to the process being analysed, observations or participation in the process. Nowadays, for this purpose, it is increasingly proposed to use the event logs from organization’s IT systems. Event logs could be analysed with process mining techniques to create process models expressed by various notations (i.e. Petri Nets, BPMN, EPC). Process mining enables also conformance checking and enhancement analysis of the processes. In the paper issues related to process modelling and process mining are briefly discussed. A case study, an example of delivery process modelling with process mining technique is presented.

11

Software architecture design of the information technology for real-time business process monitoring

83%

Batyuk A. , Voityshyn V.

ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes

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2018

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tom Vol. 7, No 3

13--22

EN

Having precise understanding of how business processes are performed in real-life is an important input for decision makers and consequently is a strong competitive advantage for an organization. In the constantly changing modern business environment it is crucial to provide that information as soon as possible, preferably in the real-time mode. In practice, such kind of tasks are usually resolved by means of Business Intelligence solutions implemented either from scratch or based upon customizable packages. Despite of the wide range currently available types of data visualizations, modern BI solutions still lacks features to represent data obtained from process-aware systems, for example control flow charts. Current paper is devoted to the information technology for real-time business process monitoring. The represented solution is an extendable software which is based on the lambda architecture and a streaming process discovery technique.

12

First Steps Towards Process Mining in Distributed Health Information Systems

71%

Helm E. , Paster F.

International Journal of Electronics and Telecommunications

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2015

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tom Vol. 61, No. 2

137-142

EN

Business Intelligence approaches such as process mining can be applied to the healthcare domain in order to gain insight into the complex processes taking place. Disclosing as-is processes helps identify room for improvement and answers questions from medical professionals. Existing approaches are based on proprietary log data as input for mining algorithms. Integrating the Healthcare Enterprise (IHE) defines in its Audit Trail and Node Authentication (ATNA) profile how real-world events must be recorded. Since IHE is used by many healthcare providers throughout the world, an extensive amount of log data is produced. In our research we investigate if audit trails, generated from an IHE test system, carry enough content to successfully apply process mining techniques. Furthermore we assess the quality of the recorded events in accordance with the maturity level scoring system. A simplified simulation of the organizational workflow in a radiological practice is presented. Based on this simulation a process mining task is conducted.

13

Eksploracja procesów w analizie usług elektronicznych administracji publicznej

71%

Janke P.

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

|

2017

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tom z. 101

195--205

PL

W artykule przedstawiono problematykę analizy funkcjonowania usług elektronicznych administracji publicznej. Opisano przykład sposobu wykorzystania rejestrów systemowych potrzebnych do analizy procesów związanych z realizacją e-usług. Szczególną uwagę zwrócono na możliwości metody oraz dostępne narzędzia eksploracji procesów.

EN

This article presents the analysis of the problems in functioning electronic services of public administration. The article describes the possibility of using system logs to analyze the processes related to the implementation of e-services. Special attention in the article is paid to the possibility of process mining methods and tools.

14

Methods of process mining and prediction using deep learning

71%

Cieplak T. , Rymarczyk T. , Kłosowski G. , Maj M. , Pliszczuk D. , Rymarczyk P.

Przegląd Elektrotechniczny

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2021

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tom R. 97, nr 3

146--149

EN

The first part of the article presents analytical methods to understand how processes (security or business) occur and function over time. The second part presents the concept of a predictive system using deep learning methods that would enable the prediction of subsequent operations or steps that are part of the process under consideration. The article was supplemented with a review of scientific publications related to the content and theoretical foundations were provided. The research was of an applied nature, therefore the considerations are based on the example of analysis and forecasts based on historical data contained in process logs.

PL

Pierwsza część artykułu przedstawia metody analityczne pozwalające zrozumieć, w jaki sposób procesy (dotyczące bezpieczeństwa lub biznesu) zachodzą i funkcjonują w czasie. W drugiej części przedstawiono koncepcję systemu predykcyjnego wykorzystującego metody głębokiego uczenia, które umożliwiałyby przewidywanie kolejnych operacji lub kroków wchodzących w skład rozważanego procesu. Uzupełnieniem artykułu był przegląd publikacji naukowych pod kątem merytorycznym oraz podano podstawy teoretyczne. Badania miały charakter aplikacyjny, dlatego rozważania opierają się na przykładzie analiz i prognoz opartych na danych historycznych zawartych w logach procesów.

15

Guidelines for recording transport event logs

71%

Dramski M.

Zeszyty Naukowe Akademii Morskiej w Szczecinie

|

2018

|

tom nr 53 (125)

102--105

EN

A process is an ordered set of related activities taking place in a given time. Processes are present in all branches of the economy, engineering, science, etc. Due to the huge amount of data produced the rapid development of data mining techniques has been observed. Similar methods are also used in the context of processes and are called process mining. The main task of process mining is to create a process model, which is used to reason about the process and to make decisions inside it. The process model may be used to discuss responsibilities, simulations, predictions, etc. The main data structures in process mining are event logs. It is always very important to have correct data which makes creating a reliable process model possible. In this paper the basic guidelines for recording such event logs have been described and conclusions were drawn. The main focus of this research was transport problems.

16

Event log standards in BPM domain

71%

Gontar B. , Gontar Z.

Information Systems in Management

|

2012

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tom Vol. 1, No. 4

293--304

EN

Automated Business Process Discovery (alias Process Intelligence, Process Analysis, Workflow Mining or Process Mining) includes methods, standards and tools to support the discovery and analysis of operational business processes. Paper aims to review ongoing standards for storing and managing event log data, which is starting point for process mining, and verify in what range chosen standards meet the needs of event log analysis. We discuss CWAD, BPAF, PROV-DM, PROV-O, MXML, SA-MXML, and XES formats. Findings of the paper are based on the literature review and analysis of selected software.

17

Extensible event stream format for navigational data

71%

Dramski M.

Zeszyty Naukowe Akademii Morskiej w Szczecinie

|

2016

|

tom nr 47 (119)

61--65

EN

The eXtensible Event Stream (XES) format is a new approach to illustrate the process data. Every ship journey is a sequence of some activities which can be read using different sources of data such ARPA, AIS etc. So we can say that this is a kind of process and its data can be organized in ordered and simple form. The most popular data formats to show the process data were of course XML and CSV. Currently, we can observe huge progress in the domain of process mining. Every year, new tools appeared and the need for some data standard became necessary. This standard is called Extensible Event Stream. In this paper, the use of XES format in navigational data is described.

18

Inferring Unobserved Events in Systems with Shared Resources and Queues

71%

Fahland D. , Denisov V. , van der Aalst W. M. P.

Fundamenta Informaticae

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2021

|

tom Vol. 183, nr 3-4

203--242

EN

To identify the causes of performance problems or to predict process behavior, it is essential to have correct and complete event data. This is particularly important for distributed systems with shared resources, e.g., one case can block another case competing for the same machine, leading to inter-case dependencies in performance. However, due to a variety of reasons, real-life systems often record only a subset of all events taking place. To understand and analyze the behavior and performance of processes with shared resources, we aim to reconstruct bounds for timestamps of events in a case that must have happened but were not recorded by inference over events in other cases in the system. We formulate and solve the problem by systematically introducing multi-entity concepts in event logs and process models. We introduce a partial-order based model of a multi-entity event log and a corresponding compositional model for multi-entity processes. We define PQR-systems as a special class of multi-entity processes with shared resources and queues. We then study the problem of inferring from an incomplete event log unobserved events and their timestamps that are globally consistent with a PQR-system. We solve the problem by reconstructing unobserved traces of resources and queues according to the PQR-model and derive bounds for their timestamps using a linear program. While the problem is illustrated for material handling systems like baggage handling systems in airports, the approach can be applied to other settings where recording is incomplete. The ideas have been implemented in ProM and were evaluated using both synthetic and real-life event logs.

19

The K-means Grouping Method as a Mean to Control the Performance of the Production Process

67%

Kęsek M.

Inżynieria Mineralna

|

2020

|

tom no. 1, vol. 1

257--264

EN

The paper presents a concept of using clusters of objects using the k-means method to control the performance of the production process, which runs under variable conditions. The distribution of the production process performance in production cycles grouped according to similarity is the basis for controlling the performance of subsequent production cycles. The practical part of the paper contains an example of calculations carried out according to this concept using the VBA and R languages, and is relates to the bolting process in underground mines.

PL

W artykule przedstawiono koncepcję wykorzystania grupowania obiektów metodą k-średnich do kontroli wydajności procesu produkcyjnego, który przebiega w zmiennych warunkach. Rozkłady wydajności procesu produkcyjnego w pogrupowanych pod względem podobieństwa cyklach produkcyjnych, stanowią podstawę kontroli wydajności kolejnych cykli produkcyjnych. Część praktyczna pracy zawiera przykład obliczeń przeprowadzonych według tej koncepcji z użyciem języka VBA oraz języka R i dotyczy procesu kotwienia w kopalniach podziemnych.

20

An Overview of Data Mining and Process Mining Applications in Underground Mining

59%

Brzychczy E.

Inżynieria Mineralna

|

2019

|

tom R. 21, nr 1

301--314

EN

The underground mining process can be analysed with a data-oriented or process-oriented approach. The first of them is popular and wide known as data mining while the second is still not often used in the conditions of the mining companies. The aim of this paper is an overview of data mining and process mining applications in an underground mining domain and an investigation of the most popular analytic techniques used in the defined analytic perspectives (“Diagnostics and machinery”, “Geomechanics”, “Hazards”, “Mine planning and safety”). In the paper two research questions are formulated: RQ1: What are the most popular data mining/process mining tasks in the analysis of the underground mining process? and RQ2: What are the most popular data mining/process mining techniques applied in the multi-perspective analysis of the underground mining process? In the paper sixty--two published articles regarding to data mining tasks and analytic techniques in the mentioned domain have been analysed. The results show that predominatingly predictive tasks were formulated with regard to the analysed phenomena, with strong overrepresentation of classification task. The most frequent data mining algorithms is comprised of the following: artificial neural networks, decision trees, rule induction and regression. Only a few applications of process mining in analysis of the underground mining process have been found – they were briefly described in the paper.

PL

Celem artykułu jest przegląd zastosowań eksploracji danych (data mining) i procesów (process mining) w analizie procesu wydobywczego w kopalniach podziemnych oraz identyfikacja najpopularniejszych technik analizy danych w tym zakresie. W artykule sformułowano dwa pytania badawcze: P1: Jakie są najpopularniejsze zadania eksploracji danych/eksploracji procesów w analizie procesu wydobywczego w kopalniach podziemnych? oraz P2: Jakie są najpopularniejsze techniki eksploracji danych/eksploracji procesów stosowane w wielowymiarowej analizie procesu wydobywczego w kopalniach podziemnych? W artykule przeanalizowano sześćdziesiąt dwie opublikowane prace dotyczące eksploracji danych w ujęciu zdefiniowanych perspektyw analitycznych (“Diagnostyka i maszyny”, “Geomechanika”, “Zagrożenia”, “Projektowanie kopalń i bezpieczeństwo”). Wyniki pokazują, że w odniesieniu do analizowanych zjawisk formułowano głównie zadania predykcyjne, z silną nadreprezentacją zadania klasyfikacji. Do najczęściej wykorzystywanych technik eksploracji danych należą: sztuczne sieci neuronowe, drzewa decyzyjne, indukcja reguł i regresja. Eksploracja procesów w analizie procesu wydobywczego w kopalniach podziemnych została opisana tylko w kilku artykułach, które pokrótce omówiono.