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Digital Transformation at thyssenkrupp: Challenges, Strategies and Examples Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Aligning Textual and Graphical Descriptions of Processes Through ILP Techniques

Authors : Josep Sànchez-Ferreres, Josep Carmona, Lluís Padró

Published in: Advanced Information Systems Engineering

Publisher: Springer International Publishing

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Abstract

With the aim of having individuals from different backgrounds and expertise levels examine the operations in an organization, different representations of business processes are maintained. To have these different representations aligned is not only a desired feature, but also a real challenge due to the contrasting nature of each process representation. In this paper we present an efficient technique for aligning a textual description and a graphical model of a process. The technique is grounded on using natural language processing techniques to extract linguistic features of each representation, and encode the search as a mathematical optimization encoded using Integer Linear Programming (ILP) whose resolution ensures an optimal alignment between both descriptions. The technique has been implemented and the experiments witness the significance of the approach with respect to the state-of-the-art technique for the same task.

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previous chapter Structural Descriptions of Process Models Based on Goal-Oriented Unfolding
next chapter Use Cases for Understanding Business Process Models
Footnotes
2
In order improve the performance of the word sense disambiguator on model sentences, the sentences from the text are provided as additional context to the analyzer. This greatly improves the disambiguation step when the model and the text are sharing a common semantic domain.
 
3
A word \(w_1\) is a hypernym of \(w_2\) iff \(w_1\) describes a superclass of \(w_2\) (e.g. mammal is a hypernym of cat, and document is a hypernym of letter). Hypernymy is obtained from WordNet.
 
4
The tf-idf of a token t is the product of tf \(:=\) (Number of appearances of t in its sentence/Number of tokens in that sentence) and idf \(:=\) \(log_e(\)Total number of sentences/Sentences containing t).
 
5
In this case, sim(s, t) corresponds to \(WeightedOverlapping(v_s, v_t)\) where \(v_s\) and \(v_t\) are the feature vectors of s and t respectively.
 
6
Note that these equations can also be encoded using the Special Ordered Sets (SOS) constraint \(\forall t: a_{t,1}, \cdots , a_{t,|S|}\), which denotes exactly the same constraint, and yields better performance in the ILP solvers that implement it.
 
7
The web application is available at: http://​xorrai.​cs.​upc.​edu:​8080/​bpmninterface/​. The tool we present in this paper corresponds to the BPMN vs Text tab.
 
8
We could not include all the executions for the approach from [9] since instances bigger than 46 tasks hit the imposed 4 h time limit.
 
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Metadata
Title
Aligning Textual and Graphical Descriptions of Processes Through ILP Techniques
Authors
Josep Sànchez-Ferreres
Josep Carmona
Lluís Padró
Copyright Year
2017
Book
Advanced Information Systems Engineering

Print ISBN: 978-3-319-59535-1

Electronic ISBN: 978-3-319-59536-8

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-59536-8_26

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