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2018 | Book

Model and Event Log Reductions to Boost the Computation of Alignments Read chapter Read first chapter

Data-Driven Process Discovery and Analysis

6th IFIP WG 2.6 International Symposium, SIMPDA 2016, Graz, Austria, December 15-16, 2016, Revised Selected Papers

Editors: Prof. Paolo Ceravolo, Christian Guetl, Stefanie Rinderle-Ma

Publisher: Springer International Publishing

Book Series : Lecture Notes in Business Information Processing

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

This book constitutes the revised selected papers from the 6th IFIP WG 2.6 International Symposium on Data-Driven Process Discovery and Analysis, SIMPDA 2016, held in Graz, Austria in December 2016.

The 5 papers presented in this volume were carefully reviewed and selected from 18 submissions. In this edition, the presentations focused on the adoption of process mining algorithms for continuous monitoring of business process. They underline the most relevant challenges identified and propose novel solutions for their resolution.

Table of Contents

Frontmatter
Model and Event Log Reductions to Boost the Computation of Alignments
Abstract
The alignment of observed and modeled behavior is a pivotal issue in process mining because it opens the door for assessing the quality of a process model, as well as the usage of the model as a precise predictor for the execution of a process. This paper presents a novel technique for reduction of a process model based on the notion of indication, by which, the occurrence of an event in the model reveals the occurrence of some other events, hence relegating the later set as less important information when model and log alignment is computed. Once indications relations are computed in the model, both model and log can be reduced accordingly, and then fed to the state of the art approaches for computing alignments. Finally, the (macro)-alignment derived is expanded in these parts containing high-level events that represent a set of indicated events, by using an efficient algorithm taken from bioinformatics that guarantees optimality in the local parts of the alignment. The implementation of the presented techniques shows a significant reduction both in computation time and in memory usage, the latter being a significant barrier to apply the alignment technology on large instances.
Farbod Taymouri, Josep Carmona
Translating BPMN to Business Rules
Abstract
Business Process Model and Notation (BPMN) is a standard graphical notation that is widely used for modeling Business Processes (BP) in Business Process Management (BPM) systems. A key application of such systems is continuous analysis of BP execution for checking compliance of execution logs with process models. In this paper we introduce a simple, human-readable rule language based on a fragment of First-Order Logic (FOL) and show how compliance rules can be generated directly from BPMN models. We focus on control flow aspects of BPMN models by (1) transforming the model to obtain a uniform representation of task activation (2) dividing the model into sets of components and (3) using our proposed language to generate compliance rules for each component. We show that these rules can be used in the analysis of the business process execution log using British Telecom’s Aperture business process analysis tool.
Hamda Al-Ali, Ernesto Damiani, Mahmoud Al-Qutayri, Mohammad Abu-Matar, Rabeb Mizouni
Execution-Based Model Profiling
Abstract
In model-driven engineering (MDE), models are mostly used in prescriptive ways for system engineering. While prescriptive models are indeed an important ingredient to realize a system, for later phases in the systems’ lifecycles additional model types are beneficial to use. Unfortunately, current MDE approaches mostly neglect the information upstream in terms of descriptive models from operations to (re)design phases. To tackle this limitation, we propose execution-based model profiling as a continuous process to improve prescriptive models at design-time through runtime information. This approach incorporates knowledge in terms of model profiles from execution logs of the running system. To accomplish this, we combine techniques of process mining with runtime models of MDE. In the course of a case study, we make use of a traffic light system example to demonstrate the feasibility and benefits of the introduced execution-based model profiling approach.
Alexandra Mazak, Manuel Wimmer, Polina Patsuk-Bösch
DB-XES: Enabling Process Discovery in the Large
Abstract
Dealing with the abundance of event data is one of the main process discovery challenges. Current process discovery techniques are able to efficiently handle imported event log files that fit in the computer’s memory. Once data files get bigger, scalability quickly drops since the speed required to access the data becomes a limiting factor. This paper proposes a new technique based on relational database technology as a solution for scalable process discovery. A relational database is used both for storing event data (i.e. we move the location of the data) and for pre-processing the event data (i.e. we move some computations from analysis-time to insertion-time). To this end, we first introduce DB-XES as a database schema which resembles the standard XES structure, we provide a transparent way to access event data stored in DB-XES, and we show how this greatly improves on the memory requirements of the state-of-the-art process discovery techniques. Secondly, we show how to move the computation of intermediate data structures to the database engine, to reduce the time required during process discovery. The work presented in this paper is implemented in ProM tool, and a range of experiments demonstrates the feasibility of our approach.
Alifah Syamsiyah, Boudewijn F. van Dongen, Wil M. P. van der Aalst
Extracting Service Process Models from Location Data
Abstract
Services are today over 70% of the Gross National Product in most developed countries. The productivity improvement of services is increasingly important and it relies heavily on a deep understanding of the service processes. However, how to collect data from services has been a problem and service data is largely missing in national statistics, which brings challenges to service process modelling.
This work aims to simplify the procedure of automated process modelling, and focuses on modelling generic service processes that are location-aware. An approach based on wireless indoor positioning is developed to acquire the minimum amount of location-based process data that can be used to automatically extract the process models.
The extracted models can be further used to analyse the possible improvements of the service processes. This approach has been tested and used in dental care clinics. Besides, the automated modelling approach can be used to greatly improve the traditional process modelling in various other service industries.
Ye Zhang, Olli Martikainen, Riku Saikkonen, Eljas Soisalon-Soininen
Erratum to: Translating BPMN to Business Rules
Hamda Al-Ali, Ernesto Damiani, Mahmoud Al-Qutayri, Mohammad Abu-Matar, Rabeb Mizouni
Backmatter
Metadata
Title
Data-Driven Process Discovery and Analysis
Editors
Prof. Paolo Ceravolo
Christian Guetl
Stefanie Rinderle-Ma
Copyright Year
2018
Electronic ISBN
978-3-319-74161-1
Print ISBN
978-3-319-74160-4
DOI
https://doi.org/10.1007/978-3-319-74161-1

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