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Exploitation and Merge of Information Sources for Public Procurement Improvement

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  • First Online:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1752))

Abstract

The analysis of big data on public procurement can improve the process of carrying out public tenders. The goal is to increase the quality and the correctness of the process, the efficiency of administrations, and reduce the time spent by economic operators and the costs of the public administrations. As a consequence, being able to recognize as early as possible if a public tender might contain some flaws, can enable a better relationship between the public organizations and the privates, and improve the economic conditions through the correct use of public funds. With the proliferation of e-procurement systems in the public sector, valuable and open information sources are available and can be accessed jointly. In particular, we consider the sentences published on the Italian Administrative Justice website and the Italian Anti-Corruption Authority database on public procurement. In this paper, we describe how to find connections between the procurement data and the appeals and how to exploit the resulting data for the measurement of litigation and clustering into communities the nodes representing entities having similar interests.

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Notes

  1. 1.

    https://www.anticorruzione.it.

  2. 2.

    https://www.giustizia-amministrativa.it.

  3. 3.

    https://neo4j.com.

  4. 4.

    https://mechanicalsoup.readthedocs.io/en/stable.

  5. 5.

    https://beautiful-soup-4.readthedocs.io/en/latest.

  6. 6.

    https://github.com/roberto-nai-unito/ANACLucene.

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Authors and Affiliations

  1. Computer Science Department, University of Turin, Turin, Italy

    Roberto Nai, Emilio Sulis, Paolo Pasteris, Mirko Giunta & Rosa Meo

Authors
  1. Roberto Nai
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  2. Emilio Sulis
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  3. Paolo Pasteris
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  4. Mirko Giunta
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  5. Rosa Meo
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Corresponding author

Correspondence to Roberto Nai .

Editor information

Editors and Affiliations

  1. University of Sydney, Sydney, Australia

    Irena Koprinska

  2. University of Bari Aldo Moro, Bari, Italy

    Paolo Mignone

  3. University of Pisa, Pisa, Italy

    Riccardo Guidotti

  4. Warsaw University of Technology, Warsaw, Poland

    Szymon Jaroszewicz

  5. Heidelberg University, Heidelberg, Germany

    Holger Fröning

  6. UniCredit, Rome, Italy

    Francesco Gullo

  7. University of Lisbon, Lisbon, Portugal

    Pedro M. Ferreira

  8. Roche, Basel, Switzerland

    Damian Roqueiro

  9. Barcelona Supercomputing Center, Barcelona, Spain

    Gaia Ceddia

  10. Halmstad University, Halmstad, Sweden

    Slawomir Nowaczyk

  11. University of Porto, Porto, Portugal

    João Gama

  12. University of Porto, Porto, Portugal

    Rita Ribeiro

  13. UPC BarcelonaTech, Barcelona, Spain

    Ricard Gavaldà

  14. University of Naples Federico II, Naples, Italy

    Elio Masciari

  15. University of North Carolina, Charlotte, USA

    Zbigniew Ras

  16. ICAR-CNR, Rende, Italy

    Ettore Ritacco

  17. University of Pisa, Pisa, Italy

    Francesca Naretto

  18. Aalen University of Applied Sciences, Aalen, Germany

    Andreas Theissler

  19. Warsaw University of Technology, Warszaw, Poland

    Przemyslaw Biecek

  20. KU Leuven, Leuven, Belgium

    Wouter Verbeke

  21. University of Duisburg-Essen, Essen, Germany

    Gregor Schiele

  22. Graz University of Technology, Graz, Austria

    Franz Pernkopf

  23. AMD, Dublin, Ireland

    Michaela Blott

  24. UniCredit, Rome, Italy

    Ilaria Bordino

  25. UniCredit, Milan, Italy

    Ivan Luciano Danesi

  26. National Agency for New Technologies, Rome, Italy

    Giovanni Ponti

  27. Unicredit, Rome, Italy

    Lorenzo Severini

  28. University of Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  29. University of Bari Aldo Moro, Bari, Italy

    Giuseppina Andresini

  30. University of Lisbon, Lisbon, Portugal

    Ibéria Medeiros

  31. University of Lisbon, Lisbon, Portugal

    Guilherme Graça

  32. Northwestern University, Chicago, USA

    Lee Cooper

  33. Roche, Basel, Switzerland

    Naghmeh Ghazaleh

  34. University of Lausanne, Lausanne, Switzerland

    Jonas Richiardi

  35. Novartis, Basel, Switzerland

    Diego Saldana

  36. Novartis, Basel, Switzerland

    Konstantinos Sechidis

  37. Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy

    Arif Canakoglu

  38. Politecnico di Milano, Milan, Italy

    Sara Pido

  39. Politecnico di Milano, Milan, Italy

    Pietro Pinoli

  40. University of Waikato, Hamilton, New Zealand

    Albert Bifet

  41. Halmstad University, Halmstad, Sweden

    Sepideh Pashami

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Nai, R., Sulis, E., Pasteris, P., Giunta, M., Meo, R. (2023). Exploitation and Merge of Information Sources for Public Procurement Improvement. In: Koprinska, I., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022. Communications in Computer and Information Science, vol 1752. Springer, Cham. https://doi.org/10.1007/978-3-031-23618-1_6

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  • DOI: https://doi.org/10.1007/978-3-031-23618-1_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23617-4

  • Online ISBN: 978-3-031-23618-1

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