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.