We describe a framework for supporting arbitrarily complex SQL queries with “uncertain”
predicates. The query semantics is based on a probabilistic model and the results are …

With the increasing amount of data and the need to integrate data from multiple data
sources, one of the challenging issues is to identify near-duplicate records efficiently. In this …

We propose a novel approach to performing efficient similarity search and classification in
high dimensional data. In this framework, the database elements are vectors in a Euclidean …

Trio is a new database system that manages not only data, but also the accuracy and
lineage of the data. Approximate (uncertain, probabilistic, incomplete, fuzzy, and imprecise!) …

This paper introduces\uldb s, an extension of relational databases with simple yet
expressive constructs for representing and manipulating both {\em lineage} and {\em …

ABSTRACT The World Wide Web is witnessing an increase in the amount of structured
content–vast heterogeneous collections of structured data are on the rise due to the Deep …

This paper explores an inherent tension in modeling and querying uncertain data: simple,
intuitive representations of uncertain data capture many application requirements, but these …

Similarity join is a useful primitive operation underlying many applications, such as near
duplicate Web page detection, data integration, and pattern recognition. Traditional similarity …

Top-k queries based on ranking elements of multidimensional datasets are a fundamental
building block for many kinds of information discovery. The best known general-purpose …

This paper addresses the efficient processing of top-k queries in wide-area distributed data
repositories where the index lists for the attribute values (or text terms) of a query are …