scholarly journals Improving the performance of process discovery algorithms by instance selection

2020 ◽  
Vol 17 (3) ◽  
pp. 927-958
Author(s):  
Mohammadreza Sani ◽  
Sebastiaan van Zelst ◽  
Aalst van der
Keyword(s):  
Process Model ◽  
Business Processes ◽  
Process Models ◽  
Instance Selection ◽  
Event Data ◽  
Process Discovery ◽  
Selection Strategies ◽  
Speed Up ◽  
The Right ◽  
Discovery Algorithms

Process discovery algorithms automatically discover process models based on event data that is captured during the execution of business processes. These algorithms tend to use all of the event data to discover a process model. When dealing with large event logs, it is no longer feasible using standard hardware in limited time. A straightforward approach to overcome this problem is to down-size the event data by means of sampling. However, little research has been conducted on selecting the right sample, given the available time and characteristics of event data. This paper evaluates various subset selection methods and evaluates their performance on real event data. The proposed methods have been implemented in both the ProM and the RapidProM platforms. Our experiments show that it is possible to considerably speed up discovery using instance selection strategies. Furthermore, results show that applying biased selection of the process instances compared to random sampling will result in simpler process models with higher quality.

scholarly journals The impact of biased sampling of event logs on the performance of process discovery

Computing ◽  
2021 ◽  
Author(s):  
Mohammadreza Fani Sani ◽  
Sebastiaan J. van Zelst ◽  
Wil M. P. van der Aalst
Keyword(s):  
Process Model ◽  
Business Processes ◽  
Sampling Technique ◽  
Process Models ◽  
Biased Sampling ◽  
Event Data ◽  
Model Quality ◽  
Process Discovery ◽  
The Impact ◽  
Discovery Algorithms

AbstractWith Process discovery algorithms, we discover process models based on event data, captured during the execution of business processes. The process discovery algorithms tend to use the whole event data. When dealing with large event data, it is no longer feasible to use standard hardware in a limited time. A straightforward approach to overcome this problem is to down-size the data utilizing a random sampling method. However, little research has been conducted on selecting the right sample, given the available time and characteristics of event data. This paper systematically evaluates various biased sampling methods and evaluates their performance on different datasets using four different discovery techniques. Our experiments show that it is possible to considerably speed up discovery techniques using biased sampling without losing the resulting process model quality. Furthermore, due to the implicit filtering (removing outliers) obtained by applying the sampling technique, the model quality may even be improved.

An Integrated Approach for Discovering Process Models According to Business Process Types

2021 ◽  
Vol 16 ◽  
pp. 1-14
Author(s):  
Zineb Lamghari
Keyword(s):  
Business Process ◽  
Domain Knowledge ◽  
Process Model ◽  
Process Mining ◽  
Integrated Approach ◽  
Process Models ◽  
Management Support ◽  
Event Data ◽  
Process Discovery ◽  
Discovery Algorithms

Process discovery technique aims at automatically generating a process model that accurately describes a Business Process (BP) based on event data. Related discovery algorithms consider recorded events are only resulting from an operational BP type. While the management community defines three BP types, which are: Management, Support and Operational. They distinguish each BP type by different proprieties like the main business process objective as domain knowledge. This puts forward the lack of process discovery technique in obtaining process models according to business process types (Management and Support). In this paper, we demonstrate that business process types can guide the process discovery technique in generating process models. A special interest is given to the use of process mining to deal with this challenge.

scholarly journals Strategies to Automatically Derive A Process Model From A Configurable Process Model Based on Event Data

2017 ◽  
Author(s):  
Mauricio Arriagada-Benítez ◽  
Marcos Sepúlveda ◽  
Jorge Munoz-Gama ◽  
Joos C. A. M. Buijs
Keyword(s):  
Process Model ◽  
Business Processes ◽  
Discrete Event ◽  
Search Space ◽  
Process Models ◽  
Greedy Heuristic ◽  
Event Data ◽  
Case Scenario ◽  
Erp System ◽  
The Right

Configurable process models are frequently used to represent business workflows and other discrete event systems among different branches of large organizations: they unify commonalities shared by all branches and describe their differences, at the same time. The configuration of such models is usually done manually, which is challenging. On the one hand, when the number of configurable nodes in the configurable process model grows, the size of the search space increases exponentially. On the other hand, the person performing the configuration may lack the holistic perspective to make the right choice for all configurable nodes at the same time, since choices influence each other. Nowadays, information systems that support the execution of business processes create event data reflecting how processes are performed. In this article, we propose three strategies (based on exhaustive search, genetic algorithms, and greedy heuristic) that use event data to automatically derive a process model from a configurable process model that better represents the characteristics of the process in a specific branch. These strategies have been implemented in our proposed framework, and tested in both business-like event logs as recorded in a higher educational ERP system, and a real case scenario involving a set of Dutch municipalities.

scholarly journals PARALLEL PROCESS DISCOVERY USING A NEW TIME-BASED ALPHA++ MINER

2020 ◽  
Vol 21 (1) ◽  
pp. 126-141
Author(s):  
Yutika Amelia Effendi ◽  
Riyanarto Sarno
Keyword(s):  
Process Model ◽  
Business Processes ◽  
Heuristic Algorithms ◽  
Process Models ◽  
Time Interval ◽  
Log Data ◽  
Process Discovery ◽  
Fitness Value ◽  
Event Log ◽  
New Time

A lot of services in business processes lead information systems to build huge amounts of event logs that are difficult to observe. The event log will be analysed using a process discovery technique to mine the process model by implementing some well-known algorithms such as deterministic algorithms and heuristic algorithms. All of the algorithms have their own benefits and limitations in analysing and discovering the event log into process models. This research proposed a new Time-based Alpha++ Miner with an improvement of the Alpha++ Miner and Modified Time-based Alpha Miner algorithm. The proposed miner is able to consider noise traces, loop, and non-free choice when modelling a process model where both of original algorithms cannot override those issues. A new Time-based Alpha++ Miner utilizing Time Interval Pattern can mine the process model using new rules defined by the time interval pattern using a double-time stamp event log and define sequence and parallel (AND, OR, and XOR) relation. The original miners are only able to discover sequence and parallel (AND and XOR) relation. To know the differences between the original Alpha++ Miner and the new one including the process model and its relations, the evaluation using fitness and precision was done in this research. The results presented that the process model obtained by a new Time-based Alpha++ Miner was better than that of the original Alpha++ Miner algorithm in terms of parallel OR, handling noise, fitness value, and precision value. ABSTRAK: Banyak sistem perniagaan perkhidmatan menghasilkan sejumlah besar log data maklumat yang payah dipantau. Log data ini akan dianalisis menggunakan teknik proses penemuan bagi memperoleh model proses dengan menerapkan beberapa algoritma terkenal, seperti algoritma deterministik dan algoritma heuristik. Semua algoritma ini memiliki kehebatan dan kekurangannya dalam menganalisis dan mencari log data ke dalam model proses. Kajian ini mencadangkan Time-based Alpha++ Miner baru yang merupakan pembaharuan dari algoritma Alpha++ Miner dan Modified Time-based Alpha Miner. Algoritma baru ini dapat mempertimbangkan kesan bunyi, pusingan, dan pilihan tidak bebas ketika memodelkan model proses di mana kedua algoritma asal tidak dapat menggantikan isu tersebut. Time-based Alpha++ Miner baru mengguna pakai Pola Interval Waktu berjaya memperoleh model proses menggunakan peraturan baru berdasarkan Pola Interval Waktu menggunakan log peristiwa waktu-ganda dan menentukan jujukan dan hubungan selari (AND, OR, dan XOR). Dibandingkan algoritma asal, ia hanya dapat menemukan jujukan dan hubungan selari (AND dan XOR). Bagi membezakan Alpha++ Miner asal dan yang baru termasuk model proses dan kaitannya, penilaian menggunakan nilai padanan dan penelitian telah dijalankan dalam kajian ini. Hasil kajian model proses yang diperoleh oleh Time-based Alpha++ Miner baru, adalah lebih baik keputusannya berbanding menggunakan algoritma Alpha++ Miner asal, berdasarkan hubungan selari OR, bunyi kawalan, nilai padanan, dan nilai penelitian.

scholarly journals Improving efficiency for discovering business processes containing invisible tasks in non-free choice

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Riyanarto Sarno ◽  
Kelly Rossa Sungkono ◽  
Muhammad Taufiqulsa’di ◽  
Hendra Darmawan ◽  
Achmad Fahmi ◽  
...  
Keyword(s):  
Process Model ◽  
Free Choice ◽  
Business Processes ◽  
Computing Time ◽  
Time Efficiency ◽  
Process Discovery ◽  
Event Logs ◽  
Event Log ◽  
Discovery Algorithms ◽  
Formula Method

AbstractProcess discovery helps companies automatically discover their existing business processes based on the vast, stored event log. The process discovery algorithms have been developed rapidly to discover several types of relations, i.e., choice relations, non-free choice relations with invisible tasks. Invisible tasks in non-free choice, introduced by $$\alpha ^{\$ }$$ α $ method, is a type of relationship that combines the non-free choice and the invisible task. $$\alpha ^{\$ }$$ α $ proposed rules of ordering relations of two activities for determining invisible tasks in non-free choice. The event log records sequences of activities, so the rules of $$\alpha ^{\$ }$$ α $ check the combination of invisible task within non-free choice. The checking processes are time-consuming and result in high computing times of $$\alpha ^{\$ }$$ α $ . This research proposes Graph-based Invisible Task (GIT) method to discover efficiently invisible tasks in non-free choice. GIT method develops sequences of business activities as graphs and determines rules to discover invisible tasks in non-free choice based on relationships of the graphs. The analysis of the graph relationships by rules of GIT is more efficient than the iterative process of checking combined activities by $$\alpha ^{\$ }$$ α $ . This research measures the time efficiency of storing the event log and discovering a process model to evaluate GIT algorithm. Graph database gains highest storing computing time of batch event logs; however, this database obtains low storing computing time of streaming event logs. Furthermore, based on an event log with 99 traces, GIT algorithm discovers a process model 42 times faster than α++ and 43 times faster than α$. GIT algorithm can also handle 981 traces, while α++ and α$ has maximum traces at 99 traces. Discovering a process model by GIT algorithm has less time complexity than that by $$\alpha ^{\$ }$$ α $ , wherein GIT obtains $$O(n^{3} )$$ O ( n 3 ) and $$\alpha ^{\$ }$$ α $ obtains $$O(n^{4} )$$ O ( n 4 ) . Those results of the evaluation show a significant improvement of GIT method in term of time efficiency.

scholarly journals Business processes exceptions in relation to operational performance

2019 ◽  
Vol 25 (5) ◽  
pp. 908-922 ◽  
Author(s):  
Remco Dijkman ◽  
Oktay Turetken ◽  
Geoffrey Robert van IJzendoorn ◽  
Meint de Vries
Keyword(s):  
Business Process ◽  
Process Model ◽  
Business Processes ◽  
Process Models ◽  
Operational Performance ◽  
Normal Flow ◽  
Throughput Time ◽  
Content Type ◽  
Business Process Models ◽  
Practical Implications

Purpose Business process models describe the way of working in an organization. Typically, business process models distinguish between the normal flow of work and exceptions to that normal flow. However, they often present an idealized view. This means that unexpected exceptions – exceptions that are not modeled in the business process model – can also occur in practice. This has an effect on the efficiency of the organization, because information systems are not developed to handle unexpected exceptions. The purpose of this paper is to study the relation between the occurrence of exceptions and operational performance. Design/methodology/approach The paper does this by analyzing the execution logs of business processes from five organizations, classifying execution paths as normal or exceptional. Subsequently, it analyzes the differences between normal and exceptional paths. Findings The results show that exceptions are related to worse operational performance in terms of a longer throughput time and that unexpected exceptions relate to a stronger increase in throughput time than expected exceptions. Practical implications These findings lead to practical implications on policies that can be followed with respect to exceptions. Most importantly, unexpected exceptions should be avoided by incorporating them into the process – and thus transforming them into expected exceptions – as much as possible. Also, as not all exceptions lead to longer throughput times, continuous improvement should be employed to continuously monitor the occurrence of exceptions and make decisions on their desirability in the process. Originality/value While work exists on analyzing the occurrence of exceptions in business processes, especially in the context of process conformance analysis, to the best of the authors’ knowledge this is the first work that analyzes the possible consequences of such exceptions.

scholarly journals Pengujian Unit Plugin Pemberian Anotasi Efek Secara Semantik pada Proses Bisnis BPMN

2021 ◽  
Vol 6 (3) ◽  
pp. 170
Author(s):  
Hilman Nuril Hadi
Keyword(s):  
Business Process ◽  
Process Model ◽  
Business Processes ◽  
Process Models ◽  
Unit Testing ◽  
Business Process Model ◽  
Testing Technique ◽  
Modeling Tools ◽  
Path Testing ◽  
Future Business

Business process model was created to make it easier for business process stakeholders to communicate and discuss the structure of the process more effectively and efficiently. Business process models can also be business artifacts and media that can be analyzed further to improve and maintain organizational competitiveness. To analyze business processes in a structured manner, the effect/results of the execution of business processes will be one of the important information. The effect/result of the execution of certain activities or a business process as a whole are useful for managing business processes, including for improvements related to future business processes. This effect annotation approach needs to be supported by business process modeling tools to assist business analysts in managing business processes properly. In previous research, the author has developed a plugin that supports business analysts to describe the effects semantically attached to activities in the Business Process Model and Notation (BPMN) business process model. In this paper, the author describes the unit testing process and its results on the plugin of semantic effect annotation that have been developed. Unit testing was carried out using the basic path testing technique and has obtained three test paths. The results of unit test for plugin are also described in this paper.

scholarly journals Collaborative method to maintain business process models updated

2014 ◽  
Vol 11 (2) ◽  
pp. 461-480 ◽  
Author(s):  
Nuno Castela ◽  
Paulo Dias ◽  
Marielba Zacarias ◽  
José Tribolet
Keyword(s):  
Case Studies ◽  
Business Process ◽  
Process Model ◽  
Business Processes ◽  
Process Models ◽  
Organizational Environment ◽  
Business Process Model ◽  
Business Process Models ◽  
Definition Of ◽  
Over Time

Business process models are often forgotten after their creation and its representation is not usually updated. This appears to be negative as processes evolve over time. This paper discusses the issue of business process models maintenance through the definition of a collaborative method that creates interaction contexts enabling business actors to discuss about business processes, sharing business knowledge. The collaboration method extends the discussion about existing process representations to all stakeholders promoting their update. This collaborative method contributes to improve business process models, allowing updates based in change proposals and discussions, using a groupware tool that was developed. Four case studies were developed in real organizational environment. We came to the conclusion that the defined method and the developed tool can help organizations to maintain a business process model updated based on the inputs and consequent discussions taken by the organizational actors who participate in the processes.

Ontology-Based Framework for Quality in Configurable Process Models

2020 ◽  
pp. 464-478
Author(s):  
Loubna El Faquih ◽  
Mounia Fredj
Keyword(s):  
Business Process ◽  
Process Modeling ◽  
Process Model ◽  
Business Processes ◽  
Research Work ◽  
Process Models ◽  
Novel Method ◽  
Ontology Languages ◽  
Variability Management

In recent years, business process modeling has increasingly drawn the attention of enterprises. As a result of the wide use of business processes, redundancy problems have arisen and researchers introduced the variability management, in order to enhance the business process reuse. The most approach used in this context is the Configurable Process Model solution, which consists in representing the variable and the fixed parts together in a unique model. Due to the increasing number of variants, the configurable models become complex and incomprehensible, and their quality is therefore impacted. Most of research work is limited to the syntactic quality of process variants. The approach presented in this paper aims at providing a novel method towards syntactic verification and semantic validation of configurable process models based on ontology languages. We define validation rules for assessing the quality of configurable process models. An example in the e-healthcare domain illustrates the main steps of our approach.

scholarly journals The RALph miner for automated discovery and verification of resource-aware process models

2020 ◽  
Vol 19 (6) ◽  
pp. 1415-1441
Author(s):  
Cristina Cabanillas ◽  
Lars Ackermann ◽  
Stefan Schönig ◽  
Christian Sturm ◽  
Jan Mendling
Keyword(s):  
Process Model ◽  
Model Verification ◽  
Process Models ◽  
Resource Assignment ◽  
Process Discovery ◽  
Event Logs ◽  
Different Types ◽  
Automated Discovery ◽  
And Performance ◽  
Resource Aware

Abstract Automated process discovery is a technique that extracts models of executed processes from event logs. Logs typically include information about the activities performed, their timestamps and the resources that were involved in their execution. Recent approaches to process discovery put a special emphasis on (human) resources, aiming at constructing resource-aware process models that contain the inferred resource assignment constraints. Such constraints can be complex and process discovery approaches so far have missed the opportunity to represent expressive resource assignments graphically together with process models. A subsequent verification of the extracted resource-aware process models is required in order to check the proper utilisation of resources according to the resource assignments. So far, research on discovering resource-aware process models has assumed that models can be put into operation without modification and checking. Integrating resource mining and resource-aware process model verification faces the challenge that different types of resource assignment languages are used for each task. In this paper, we present an integrated solution that comprises (i) a resource mining technique that builds upon a highly expressive graphical notation for defining resource assignments; and (ii) automated model-checking support to validate the discovered resource-aware process models. All the concepts reported in this paper have been implemented and evaluated in terms of feasibility and performance.

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