PRETSA: Event Log Sanitization for Privacy-aware Process Discovery

2019 ◽  
Vol 42 (5) ◽  
pp. 352-353
Author(s):  
Stephan A. Fahrenkrog-Petersen ◽  
Han van der Aa ◽  
Matthias Weidlich
Keyword(s):  
Process Discovery ◽  
Event Log

scholarly journals Temporal Logics Over Finite Traces with Uncertainty

2020 ◽  
Vol 34 (06) ◽  
pp. 10218-10225 ◽  
Author(s):  
Fabrizio M Maggi ◽  
Marco Montali ◽  
Rafael Peñaloza
Keyword(s):  
Decision Making ◽  
Temporal Logic ◽  
Business Process ◽  
Dynamic Systems ◽  
Real Life ◽  
Temporal Logics ◽  
Business Process Modelling ◽  
Process Discovery ◽  
Event Log ◽  
Computational Properties

Temporal logics over finite traces have recently seen wide application in a number of areas, from business process modelling, monitoring, and mining to planning and decision making. However, real-life dynamic systems contain a degree of uncertainty which cannot be handled with classical logics. We thus propose a new probabilistic temporal logic over finite traces using superposition semantics, where all possible evolutions are possible, until observed. We study the properties of the logic and provide automata-based mechanisms for deriving probabilistic inferences from its formulas. We then study a fragment of the logic with better computational properties. Notably, formulas in this fragment can be discovered from event log data using off-the-shelf existing declarative process discovery techniques.

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.

Discovering Process Horizontal Boundaries to Facilitate Process Comprehension

2018 ◽  
Vol 9 (2) ◽  
pp. 1-31 ◽  
Author(s):  
Pavlos Delias ◽  
Kleanthi Lakiotaki
Keyword(s):  
Process Model ◽  
Process Mining ◽  
A Priori ◽  
Quality Criteria ◽  
Large Set ◽  
Process Discovery ◽  
Event Log ◽  
Human Interpretation ◽  
Automated Discovery ◽  
Priori Information

Automated discovery of a process model is a major task of Process Mining that means to produce a process model from an event log, without any a-priori information. However, when an event log contains a large number of distinct activities, process discovery can be real challenging. The goal of this article is to facilitate process discovery in such cases when a process is expected to contain a large set of unique activities. To this end, this article proposes a clustering approach that recommends horizontal boundaries for the process. The proposed approach ultimately partitions the event log in a way that human interpretation efforts are decomposed. In addition, it makes automated discovery more efficient as well as effective by simultaneously considering two quality criteria: informativeness and robustness of the derived groups of activities. The authors conducted several experiments to test the behavior of the algorithm under different settings, and to compare it against other techniques. Finally, they provide a set of recommendations that may help process analysts during the process discovery endeavor.

Recovering Truncated Streaming Event Log Using Coupled Hidden Markov Model

2019 ◽  
Vol 34 (04) ◽  
pp. 2059012
Author(s):  
Riyanarto Sarno ◽  
Kelly Rossa Sungkono
Keyword(s):  
Information Systems ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Process Model ◽  
Hidden Markov ◽  
Transition Probability ◽  
Process Discovery ◽  
Model Based ◽  
Event Logs ◽  
Event Log

Process discovery is a technique for obtaining process model based on traces recorded in the event log. Nowadays, information systems produce streaming event logs to record their huge processes. The truncated streaming event log is a big issue in process discovery because it inflicts incomplete traces that make process discovery depict wrong processes in a process model. Earlier research suggested several methods for recovering the truncated streaming event log and none of them utilized Coupled Hidden Markov Model. This research proposes a method that combines Coupled Hidden Markov Model with Double States and the Modification of Viterbi–Backward method for recovering the truncated streaming event log. The first layer of states contains the transition probability of activities. The second layer of states uses patterns for detecting traces which have a low appearance in the event log. The experiment results showed that the proposed method recovered appropriately the truncated streaming event log. These results also have proven that the accuracies of recovered traces obtained by the proposed method are higher than those obtained by the Hidden Markov Model and the Coupled Hidden Markov Model.

Recommendation of Process Discovery Algorithms Through Event Log Classification

2015 ◽  
pp. 3-12 ◽  
Author(s):  
Damián Pérez-Alfonso ◽  
Osiel Fundora-Ramírez ◽  
Manuel S. Lazo-Cortés ◽  
Raciel Roche-Escobar
Keyword(s):  
Process Discovery ◽  
Event Log ◽  
Discovery Algorithms

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 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.

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