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Authors: Guilherme Q. Vasconcelos 1 ; Guilherme F. Zabot 1 ; Daniel M. de Lima 1 ; José F. Rodrigues Jr. 1 ; Caetano Traina Jr. 1 ; Daniel dos S. Kaster 2 and Robson L. F. Cordeiro 1

Affiliations: 1 University of São Paulo, Brazil ; 2 State University of Londrina, Brazil

Keyword(s): Relational Databases, Division Operator, Similarity Comparison, Complex Data, Ontology, Public Tendering.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Coupling and Integrating Heterogeneous Data Sources ; Data Mining ; Data Warehouses and OLAP ; Databases and Information Systems Integration ; Enterprise Information Systems ; Enterprise Resource Planning ; Enterprise Software Technologies ; Organisational Issues on Systems Integration ; Query Languages and Query Processing ; Sensor Networks ; Signal Processing ; Simulation and Modeling ; Simulation Tools and Platforms ; Soft Computing ; Software Engineering

Abstract: TendeR-Sims (Tender Retrieval by Similarity) is a system that helps to search for satisfiable request for tender's lots in a database by filtering irrelevant lots, so companies can easily discover the contracts they can win. The system implements the Similarity-aware Relational Division Operator in a commercial Relational Database Management System (RDBMS), and compares products by combining a path distance in a preprocessed ontology with a textual distance. Tender-Sims focuses on answering the following query: select the lots where a company has a similar enough item for each of all required items. We evaluated our proposed system employing a dataset composed of product catologs of Brazilian companies in the food market and real requests for tenders with known results. In the presented experiments, TendeR Sims achieved up to 66\% cost reduction at 90\% recall when compared to the ground truth.

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Paper citation in several formats:
Vasconcelos, G.; Zabot, G.; de Lima, D.; Rodrigues Jr., J.; Traina Jr., C.; Kaster, D. and Cordeiro, R. (2018). TendeR-Sims - Similarity Retrieval System for Public Tenders. In Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-298-1; ISSN 2184-4992, SciTePress, pages 143-150. DOI: 10.5220/0006697601430150

@conference{iceis18,
author={Guilherme Q. Vasconcelos. and Guilherme F. Zabot. and Daniel M. {de Lima}. and José F. {Rodrigues Jr.}. and Caetano {Traina Jr.}. and Daniel dos S. Kaster. and Robson L. F. Cordeiro.},
title={TendeR-Sims - Similarity Retrieval System for Public Tenders},
booktitle={Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2018},
pages={143-150},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006697601430150},
isbn={978-989-758-298-1},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - TendeR-Sims - Similarity Retrieval System for Public Tenders
SN - 978-989-758-298-1
IS - 2184-4992
AU - Vasconcelos, G.
AU - Zabot, G.
AU - de Lima, D.
AU - Rodrigues Jr., J.
AU - Traina Jr., C.
AU - Kaster, D.
AU - Cordeiro, R.
PY - 2018
SP - 143
EP - 150
DO - 10.5220/0006697601430150
PB - SciTePress