The Proposition of a Framework for Semantic Process Mining

2014 ◽  
Vol 1051 ◽  
pp. 995-999
Author(s):  
Yong Xin Liao ◽  
Eduardo Rocha Loures ◽  
Eduardo Alves Portela Santos ◽  
Osiris Canciglieri
Keyword(s):  
General Framework ◽  
Business Process Management ◽  
Process Management ◽  
Process Mining ◽  
Related Information ◽  
Event Logs ◽  
Semantic Mining ◽  
Semantic Models ◽  
Footprint Analysis ◽  
Semantic Process

As one of the hot topics in Business Process Management (BPM), process mining aims at constructing models to explain what is actually happening from different perspectives based on the process-related information that automatically extracted from event logs. Because the semantics of the data that recorded in event logs are not usually explicit, current mining approaches are somewhat limited. A number of studies have been carried out in the combination use of formalized semantic models and process mining technologies to obtain the semantic mining capability. However, among these researches, there is lack of a guideline that can clearly illustrate different stages during the semantic process mining. The objective of this study is to present a general framework, which unambiguously expresses the main stages of the semantic process mining. Based on this framework, an example about carbon footprint analysis is used to show the possibility of obtaining advantages from semantic process mining.

Semantic Business Process Mining of SAP Transactions

2010 ◽  
pp. 416-429
Author(s):  
Jon Espen Ingvaldsen ◽  
Jon Atle Gulla
Keyword(s):  
Business Process ◽  
Business Process Management ◽  
Process Management ◽  
Process Mining ◽  
Structured Data ◽  
The Core ◽  
Management Perspective ◽  
System Logs ◽  
Business Process Mining ◽  
Semantic Process

This chapter introduces semantic business process mining of SAP transaction logs. SAP systems are promising domains for semantic process mining as they contain transaction logs that are linked to large amounts of structured data. A challenge with process mining these transaction logs is that the core of SAP systems was not originally designed from the business process management perspective. The business process layer was added later without full rearrangement of the system. As a result, system logs produced by SAP are not process-based, but transaction-based. This means that the system does not produce traces of process instances that are needed for process mining. In this chapter, we show how data available in SAP systems can enrich process instance logs with ontologically structured concepts, and evaluate techniques for mapping executed transaction sequences with predefined process hierarchies.

Semantic Business Process Mining of SAP Transactions

2011 ◽  
pp. 866-878 ◽  
Author(s):  
Jon Espen Ingvaldsen ◽  
Jon Atle Gulla
Keyword(s):  
Business Process ◽  
Business Process Management ◽  
Process Management ◽  
Process Mining ◽  
Structured Data ◽  
The Core ◽  
Management Perspective ◽  
System Logs ◽  
Business Process Mining ◽  
Semantic Process

This chapter introduces semantic business process mining of SAP transaction logs. SAP systems are promising domains for semantic process mining as they contain transaction logs that are linked to large amounts of structured data. A challenge with process mining these transaction logs is that the core of SAP systems was not originally designed from the business process management perspective. The business process layer was added later without full rearrangement of the system. As a result, system logs produced by SAP are not process-based, but transaction-based. This means that the system does not produce traces of process instances that are needed for process mining. In this chapter, we show how data available in SAP systems can enrich process instance logs with ontologically structured concepts, and evaluate techniques for mapping executed transaction sequences with predefined process hierarchies.

Business process management for Industry 4.0 – Three application cases in the DFKI-Smart-Lego-Factory

2018 ◽  
Vol 60 (3) ◽  
pp. 133-141 ◽  
Author(s):  
Jana-Rebecca Rehse ◽  
Sharam Dadashnia ◽  
Peter Fettke
Keyword(s):  
Business Process ◽  
Industry 4.0 ◽  
Business Process Management ◽  
Manufacturing Industry ◽  
Process Management ◽  
Process Mining ◽  
Smart Factory ◽  
Manufacturing Processes ◽  
Planning And Control ◽  
And Control

Abstract The advent of Industry 4.0 is expected to dramatically change the manufacturing industry as we know it today. Highly standardized, rigid manufacturing processes need to become self-organizing and decentralized. This flexibility leads to new challenges to the management of smart factories in general and production planning and control in particular. In this contribution, we illustrate how established techniques from Business Process Management (BPM) hold great potential to conquer challenges in Industry 4.0. Therefore, we show three application cases based on the DFKI-Smart-Lego-Factory, a fully automated “smart factory” built out of LEGO® bricks, which demonstrates the potentials of BPM methodology for Industry 4.0 in an innovative, yet easily accessible way. For each application case (model-based management, process mining, prediction of manufacturing processes) in a smart factory, we describe the specific challenges of Industry 4.0, how BPM can be used to address these challenges, and, their realization within the DFKI-Smart-Lego-Factory.

Integration of process mining techniques in simulation results analysis

2019 ◽  
Author(s):  
Irīna Šitova ◽  
Jeļena Pečerska
Keyword(s):  
Process Mining ◽  
Process Analysis ◽  
Discrete Event ◽  
Discrete Event System ◽  
Log Analysis ◽  
Related Information ◽  
Event Logs ◽  
Event System ◽  
Simulation Results ◽  
Parameter Values

The research is carried out in the area of analysis of simulation results. The aim of this research is to explore the applicability of process mining techniques, and to introduce the process mining techniques integration into results analysis of discrete-event system simulations. As soon as the dynamic discrete-event system simulation (DESS) is based on events list or calendar, most of simulators provide the events lists. These events lists are interpreted as event logs in this research, and are used for process mining. The information from the events list is analysed to extract process-related information and perform in-depth process analysis. Event log analysis verified applicability of the proposed approach. Based on the results of this research, it can be concluded that process mining techniques in simulation results analysis provide a possibility to reveal new knowledge about the performance of the system, and to find the parameter values providing the advisable performance.

A Survey of Risk-Aware Business Process Modelling

2020 ◽  
pp. 1004-1016
Author(s):  
Hanane Lhannaoui ◽  
Mohammed Issam Kabbaj ◽  
Zohra Bakkoury
Keyword(s):  
Business Process ◽  
Business Process Management ◽  
Process Management ◽  
Process Modelling ◽  
Process Models ◽  
Business Process Modelling ◽  
Business Process Models ◽  
Related Information

For organizations, risk is a key concept when dealing with business process. Integrating risks aspects during business process management starts with an accurate consideration of risk's characteristics in the modelling phase. Most research is needed on integrating risk and business process modelling. Actually, the literature suggests various approaches to represent risk-related information in business process models. The diversity of those methods and the fact that this domain is still emerging make it difficult to choose the most suitable language. This paper aims to represent a survey of the existing risk-annotated business process model's notations.

A Survey of Risk-Aware Business Process Modelling

2017 ◽  
Vol 6 (3) ◽  
pp. 14-26
Author(s):  
Hanane Lhannaoui ◽  
Mohammed Issam Kabbaj ◽  
Zohra Bakkoury
Keyword(s):  
Business Process ◽  
Business Process Management ◽  
Process Management ◽  
Process Modelling ◽  
Process Models ◽  
Business Process Modelling ◽  
Business Process Models ◽  
Related Information

For organizations, risk is a key concept when dealing with business process. Integrating risks aspects during business process management starts with an accurate consideration of risk's characteristics in the modelling phase. Most research is needed on integrating risk and business process modelling. Actually, the literature suggests various approaches to represent risk-related information in business process models. The diversity of those methods and the fact that this domain is still emerging make it difficult to choose the most suitable language. This paper aims to represent a survey of the existing risk-annotated business process model's notations.

scholarly journals Process Modelling Based on Event Logs

2018 ◽  
Vol 1 (1) ◽  
pp. 385-392
Author(s):  
Edyta Brzychczy
Keyword(s):  
Process Management ◽  
Process Mining ◽  
Process Analysis ◽  
Process Modelling ◽  
Process Models ◽  
Sources Of Information ◽  
Event Logs ◽  
It Systems ◽  
Many Sources

Abstract 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.

Associating event logs with ontologies for semantic process mining and analysis

2015 ◽  
Author(s):  
Ossi Nykänen ◽  
Alejandro Rivero-Rodriguez ◽  
Paolo Pileggi ◽  
Pekka A. Ranta ◽  
Meri Kailanto ◽  
...  
Keyword(s):  
Process Mining ◽  
Event Logs ◽  
Semantic Process
Sign in / Sign up

Export Citation Format

Share Document