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 Process Discovery of Business Processes Using Temporal Causal Relation

2019 ◽  
Vol 5 (2) ◽  
pp. 183
Author(s):  
Yutika Amelia Effendi ◽  
Nania Nuzulita
Keyword(s):  
Business Process ◽  
Business Processes ◽  
Process Mining ◽  
Causal Relation ◽  
Real Life ◽  
Process Models ◽  
Time Interval ◽  
Start Time ◽  
Process Discovery ◽  
Event Log

Background: Nowadays, enterprise computing manages business processes which has grown up rapidly. This situation triggers the production of a massive event log. One type of event log is double timestamp event log. The double timestamp has a start time and complete time of each activity executed in the business process. It also has a close relationship with temporal causal relation. The temporal causal relation is a pattern of event log that occurs from each activity performed in the process.Objective: In this paper, seven types of temporal causal relation between activities were presented as an extended version of relations used in the double timestamp event log. Since the event log was not always executed sequentially, therefore using temporal causal relation, the event log was divided into several small groups to determine the relations of activities and to mine the business process.Methods: In these experiments, the temporal causal relation based on time interval which were presented in Gantt chart also determined whether each case could be classified as sequential or parallel relations. Then to obtain the business process, each temporal causal relation was combined into one business process based on the timestamp of activity in the event log.Results: The experimental results, which were implemented in two real-life event logs, showed that using temporal causal relation and double timestamp event log could discover business process models.Conclusion: Considering the findings, this study concludes that business process models and their sequential and parallel AND, OR, XOR relations can be discovered by using temporal causal relation and double timestamp event log.Keywords:Business Process, Process Discovery, Process Mining, Temporal Causal Relation, Double Timestamp Event Log

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 Discovering Block–Structured Parallel Process Models from Causally Complete Event Logs

2016 ◽  
Vol 67 (2) ◽  
pp. 111-123 ◽  
Author(s):  
Julijana Lekić ◽  
Dragan Milićev
Keyword(s):  
Process Model ◽  
Business Processes ◽  
Process Models ◽  
Business Process Models ◽  
Modified Method ◽  
Event Logs ◽  
Event Log ◽  
Partial Correction ◽  
Modified Algorithm ◽  
Block Structured

Abstract α-algorithm is suitable to discover a large class of workflow (WF) nets based on the behaviour recorded in event logs, with the main limiting assumption that the event log is complete. Our research has been aimed at finding ways of discovering business process models based on examples of traces, ie, logs of workflow actions that do not meet the requirement of completeness. In this aim, we have modified the existing and introduced a new relation between activities recorded in the event log, which has led to a partial correction of the process models discovering technique, including the α-algorithm. We have also introduced the notion of causally complete logs, from which our modified algorithm can produce the same result as the α-algorithm from complete logs. The effect of these modifications on the efficiency of the process model discovering is mostly evident for business processes in which many activities can be performed in parallel. The application of the modified method for discovering block-structured models of parallel business processes is presented in this paper.

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 Improving Efficiency for Discovering Business Processes Containing Invisible tasks in Non-free Choice

2020 ◽  
Author(s):  
Riyanarto Sarno ◽  
Kelly Rossa Sungkono ◽  
Muhammad Taufiqulsa’di ◽  
Hendra Darmawan ◽  
Achmad Fahmi ◽  
...  
Keyword(s):  
Iterative Process ◽  
Time Complexity ◽  
Process Model ◽  
Free Choice ◽  
Business Processes ◽  
Computing Time ◽  
Graph Database ◽  
Time Efficiency ◽  
Process Discovery ◽  
Event Log

Abstract Process discovery helps companies to automatically discover their existing business processes based on the huge, stored event log. The algorithms of process discovery 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 α $ method, is a type of relation that combines the non-free choice and the invisible task. 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 α $ check the combination of invisible task within non-free choice. The checking processes is time consuming, and results in high computing times of 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 relations of the graphs. The analysis of the graph relations by rules of GIT is more efficient than the iterative process of checking combined activities by α $. This research measures the time efficiency of storing the event log and discovering a process model to evaluate GIT algorithm. Storing a streaming event log in a graph-database has the lowest computing time than storing in other databases, i.e., SQL and MongoDB. Discovering a process model by GIT algorithm has less time complexity than that by α $, wherein GIT obtains O(n3) and α $ obtains O(n4) . In terms of computing time, GIT algorithm is 0.89 faster on batch event log and 0.85 seconds faster on streaming event log than α $. Those results of the evaluation show a significant improvement of GIT method in term of time efficiency.

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.

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 Role of Stochastic Petri Net (SPN) in Process Discovery for Modelling and Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Shabnam Shahzadi ◽  
Xianwen Fang ◽  
David Anekeya Alilah
Keyword(s):  
Petri Net ◽  
Business Processes ◽  
Time Perspective ◽  
Process Mining ◽  
Vital Role ◽  
Process Discovery ◽  
Stochastic Petri Net ◽  
Event Log ◽  
The Stability ◽  
Generalized Stochastic Petri Net

For exploitation and extraction of an event’s data that has vital information which is related to the process from the event log, process mining is used. There are three main basic types of process mining as explained in relation to input and output. These are process discovery, conformance checking, and enhancement. Process discovery is one of the most challenging process mining activities based on the event log. Business processes or system performance plays a vital role in modelling, analysis, and prediction. Recently, a memoryless model such as exponential distribution of the stochastic Petri net SPN has gained much attention in research and industry. This paper uses time perspective for modelling and analysis and uses stochastic Petri net to check the performance, evolution, stability, and reliability of the model. To assess the effect of time delay in firing the transition, stochastic reward net SRN model is used. The model can also be used in checking the reliability of the model, whereas the generalized stochastic Petri net GSPN is used for evaluation and checking the performance of the model. SPN is used to analyze the probability of state transition and the stability from one state to another. However, in process mining, logs are used by linking log sequence with the state and, by this, modelling can be done, and its relation with stability of the model can be established.

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.

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