Evaluating Logic-Based Scoring Functions on Uncertain Relational Data
Keywords:logic-based scoring functions, probabilistic databases, ProQua, similarity conditions
AbstractNowadays, for many retrieval scenarios a strict
query evaluation just returning
a Boolean truth value
is not sufficient
anymore. We often rather need the support of a gradual query
fulfilment expressed by a score value out of the interval [0,1].
ProQua is a new probabilistic database
system which combines such information retrieval
concepts with database technologies. In contrast
to other state-of-the-art probabilistic database
systems ProQua facilitates logic-based
similarity conditions within its SQL-like
query language by a generic similarity operator.
In this work we formalise logic-based scoring functions
as the underlying concept of the supported similarity conditions
and introduce respective evaluation
techniques implemented by relational query plans.
Additionally, we report on their experimental verification
on a probabilistic TPC-H database.
Download data is not yet available.
How to Cite
Lehrack, S., & Saretz, S. (2012). Evaluating Logic-Based Scoring Functions on Uncertain Relational Data. Journal of Information and Data Management, 3(3), 348. https://doi.org/10.5753/jidm.2012.1458