![](bg8.png)
user wants find the same proportion (recall) of rele-
vant RFT lots from the database. Larger databases
will benefit even more from our system, for example,
a company looking for tendering any RFT lot in the
country may need to analyze tens of thousand lots.
As the ECR is a ratio, proportinally many hours may
be saved by TendeR-Sims. We then conclude that
TendeR-Sims can reduce the amount of manual work
and the associated cost by a significant amount.
5 CONCLUSION
TendeR-Sims is a system carefully designed to filter
requests for tender (RFTs) according to whether or
not a company of interest is suited to tender them.
Considering that product descriptions in a request
document may differ from the companies product ca-
talogs by little variations, so any comparison by iden-
tity (=) will retrieve only exact matches and leave out
relevant items, making this approach unsuited for this
type of data. On the other hand, comparing complex
data is better addressed by how similar (or inversely,
how distant) their are. Thus, our system relies on the
Similarity-aware Relational Division Operator.
We created a dataset, proposed an ontology and
extended a similarity retrieval module so that all data
manipulation runs inside a well-known commercial
RDBMS (Oracle 12c).
Our results show that TendeR-Sims can signifi-
cantly reduce the number of manually analyzed RFT
lots and their overall associated cost by up to 76%
of the original amount. As future work we plan to
improve the ontology, the similarity measure and ex-
plore other ontology domains.
ACKNOWLEDGEMENTS
Special thanks to the Databases and Images Group of
ICMC/USP, to the CAPES, CNPq and FAPESP fun-
ding agencies, to the ICMC/USP staff and also to the
many individuals that contributed to this study.
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