Belief network discovery from event logs for business process analysis

2018 ◽  
Vol 100 ◽  
pp. 258-266 ◽  
Author(s):  
Titas Savickas ◽  
Olegas Vasilecas
Keyword(s):  
Business Process ◽  
Process Analysis ◽  
Belief Network ◽  
Network Discovery ◽  
Event Logs ◽  
Business Process Analysis

scholarly journals Business process analysis based on anomaly detection in event logs: a study on an incident management case

2021 ◽  
Author(s):  
Esther Maria Rojas Krugger ◽  
Ana Rocío Cárdenas Maita ◽  
Juliana Cristina Barbosa Alves ◽  
Marcelo Fantinato ◽  
Sarajane Marques Peres
Keyword(s):  
Anomaly Detection ◽  
Business Process ◽  
Process Analysis ◽  
Incident Management ◽  
Event Logs ◽  
Business Process Analysis

Efficient Implementation of Hadoop MapReduce-Based Dataflow

2018 ◽  
pp. 372-385 ◽  
Author(s):  
Ishak H. A. Meddah ◽  
Khaled Belkadi
Keyword(s):  
Business Process ◽  
Process Mining ◽  
Process Analysis ◽  
Large Data ◽  
Finite State Automaton ◽  
Hadoop Mapreduce ◽  
Event Logs ◽  
Execution Traces ◽  
Business Process Analysis ◽  
Finite State

MapReduce is a solution for the treatment of large data. With it we can analyze and process data. It does this by distributing the computation in a large set of machines. Process mining provides an important bridge between data mining and business process analysis. This technique allows for the extraction of information from event logs. Firstly, the chapter mines small patterns from log traces. Those patterns are the representation of the traces execution from a business process. The authors use existing techniques; the patterns are represented by finite state automaton; the final model is the combination of only two types of patterns that are represented by the regular expressions. Secondly, the authors compute these patterns in parallel, and then combine those patterns using MapReduce. They have two parties. The first is the Map Step. The authors mine patterns from execution traces. The second is the combination of these small patterns as reduce step. The results are promising; they show that the approach is scalable, general, and precise. It minimizes the execution time by the use of MapReduce.

Discovering Patterns using Process Mining

2016 ◽  
Vol 3 (4) ◽  
pp. 21-31 ◽  
Author(s):  
Ishak Meddah ◽  
Belkadi Khaled
Keyword(s):  
Business Process ◽  
Process Mining ◽  
Process Analysis ◽  
Finite State Automaton ◽  
Regular Expressions ◽  
Final Model ◽  
Event Logs ◽  
Execution Traces ◽  
Business Process Analysis ◽  
Finite State

Process mining provides an important bridge between data mining and business process analysis, his techniques allow for extracting information from event logs. In general, there are two steps in process mining, correlation definition or discovery and then process inference or composition. Firstly, the authors' work consists to mine small patterns from a log traces of two applications; SKYPE, and VIBER, those patterns are the representation of the execution traces of a business process. In this step, the authors use existing techniques; The patterns are represented by finite state automaton or their regular expression; The final model is the combination of only two types of small patterns whom are represented by the regular expressions (ab)* and (ab*c)*. Secondly, the authors compute these patterns in parallel, and then combine those small patterns using the composition rules, they have two parties the first is the mine, they discover patterns from execution traces and the second is the combination of these small patterns. The patterns mining and the composition is illustrated by the automaton existing techniques. The Execution traces are the different actions effected by users in the SKYPE and VIBER. The results are general and precise. It minimizes the execution time and the loss of information.

Mining Patterns Using Business Process Management

2018 ◽  
pp. 78-89 ◽  
Author(s):  
Ishak H. A. Meddah ◽  
Khaled Belkadi
Keyword(s):  
Business Process ◽  
Process Management ◽  
Pattern Mining ◽  
Process Mining ◽  
Process Analysis ◽  
Finite State Automaton ◽  
Event Logs ◽  
Execution Traces ◽  
Business Process Analysis ◽  
Finite State

Process mining provides an important bridge between data mining and business process analysis. This technique allows for the extraction of information from event logs. In general, there are two steps in process mining: correlation definition or discovery and then process inference or composition. Firstly, the authors mine small patterns from log traces of two applications; those patterns are the representation of the execution traces of a business process. In this step, the authors use existing techniques. The patterns are represented by finite state automaton or their regular expression. The final model is the combination of only two types of small patterns that are represented by the regular expressions (ab)* and (ab*c)*. Secondly, the authors compute these patterns in parallel and then combine those small patterns using the composition rules. They have two parties. The first is the mine, where the authors discover patterns from execution traces, and the second is the combination of these small patterns. The pattern mining and the composition is illustrated by the automaton existing techniques.

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