Automated Analysis with Event Log Enrichment of the European Public Procurement Processes

Roberto Nai (Corresponding author), Emilio Sulis, Laura Genga

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)
2 Downloads (Pure)

Abstract

The length and extension of legal processes are two of the main problems of contemporary justice. Adopting process analysis techniques can serve to understand the course of these processes, to identify bottlenecks, and to propose improvements. This paper showcases an exploration of legal event logs using process mining techniques. First, we discuss the results obtained by applying state-of-the art process discovery techniques to data obtained from public tenders. Then, we show how to use natural language processing to automatically extract events and dates from the texts of the tenders and leverage this information for improving the results of process mining techniques. As a proof-of-concept, we propose a comparison of the French, Spanish, and Italian cases through process discovery based on calls for tenders from the European TED repository.

Original languageEnglish
Title of host publicationAdvances in Conceptual Modeling
Subtitle of host publicationER 2023 Workshops, CMLS, CMOMM4FAIR, EmpER, JUSMOD, OntoCom, QUAMES, and SmartFood, Lisbon, Portugal, November 6–9, 2023, Proceedings
EditorsTiago Prince Sales, Giancarlo Guizzardi, João Araújo, José Borbinha, Giancarlo Guizzardi
Place of PublicationCham
PublisherSpringer
Pages178-188
Number of pages11
ISBN (Electronic)978-3-031-47112-4
ISBN (Print)978-3-031-47111-7
DOIs
Publication statusPublished - 26 Oct 2023
Event42nd International Conference on Conceptual Modeling, ER 2023 - Lisbon, Portugal
Duration: 6 Nov 20239 Nov 2023

Publication series

NameLecture Notes in Computer Science (LNCS)
Volume14319
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference42nd International Conference on Conceptual Modeling, ER 2023
Country/TerritoryPortugal
CityLisbon
Period6/11/239/11/23

Keywords

  • Event log enrichment
  • Natural Language Processing
  • Process mining
  • Public procurement

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