Fast and Simple Relational Processing of Uncertain Data

Abstract

This paper introduces U-relations, a succinct and purely relational representation system for uncertain databases. U-relations support attribute-level uncertainty using vertical partitioning. If we consider positive relational algebra extended by an operation for computing possible answers, a query on the logical level can be translated into, and evaluated as, a single relational algebra query on the U-relational representation. The translation scheme essentially preserves the size of the query in terms of number of operations and, in particular, number of joins. Standard techniques employed in off-the-shelf relational database management systems are effective for optimizing and processing queries on U-relations. In our experiments we show that query evaluation on U-relations scales to large amounts of data with high degrees of uncertainty.

Anytime approximation in probabilistic databases

“…A more effective independent-and partitioning can be achieved if Φ is in DNF, since we can apply existing algebraic factorisation of DNF formulas [6]. For a relational encoding of DNF formulas of arity k and n clauses, as used in query evaluation on probabilistic databases [3], the ⊗-decomposition is unique and requires time O(k · n · log n) [42].…”

Uncertain Groupings: Probabilistic Combination of Grouping Data

“…The idea behind the COLL-view on trust is that if two sources disagree on a group, i.e., the groups collide, only one can be correct. 2 In other words, each collision is in essence a choice. Note, however, that there are dependencies between these choices.…”

“…We want to have the product of the data combination available as a database as well. Probabilistic databases such as MayBMS [2] and Trio [14] allow the use of normal database techniques to apply to probabilistic data. As such, they provide a platform on which uncertain data integration can be implemented.…”

Probabilistic Similarity Search for Uncertain Time Series

“…Due to the uncertainty of the data objects, similarity queries are probabilistic rather than exact: we can only assign to each database object a probability that it meets the query predicate. As a consequence, there is a need to adapt storage models and indexing/search techniques to deal with uncertainty [1][2][3][4]. Furthermore several approaches for probabilistic query processing have been proposed recently including probabilistic range queries [5,6], probabilistic kNN and top-k queries [7-9, 2, 10] and probabilistic ranking [10][11][12][13][14].…”