An uncertain or probabilistic database is defined as a probability distribution over a set of deterministic database instances called possible worlds. In the classical deterministic setting, the query processing problem is to compute the set of tuples representing the answer of a given query on a given database. In the probabilistic setting, this problem becomes the computation of all pairs (t,...

◮ It can compute exact and approximate probabilities with error guarantees for the clustering output. State-of-the-art techniques (e.g. UK-means, UKmedoids, MMVar): ◮ do not support the possible worlds semantics, ◮ lack support for correlations and assume probabilistic independence, ◮ use deterministic cluster medoids or expected means, and ◮ can only compute clustering based on expected distan...

Singh, Sarvjeet Ph.D., Purdue University, May 2009. Database Support for Uncertain Data. Major Professor: Sunil Prabhakar. In recent years, the field of uncertainty management in databases has received considerable interest due to the presence of numerous applications that handle probabilistic data. In this dissertation, we identify and solve important issues for managing uncertain data nativel...

Graphical models are a popular and well-studied framework for compact representation of a joint probability distribution over a large number of interdependent variables, and for efficient reasoning about such a distribution. They have been proven useful in a wide range of domains from natural language processing to computer vision to bioinformatics. In this chapter, we present an approach to us...

Real world applications that deal with information extraction, such as business intelligence software or sensor data management, must often process data provided with varying degrees of uncertainty. Uncertainty can result from multiple or inconsistent sources, as well as approximate schema mappings. Modeling, managing and integrating uncertain data from multiple sources has been an active area ...

Given a set P of n uncertain points on the real line, each represented by its one-dimensional probability density function, we consider the problem of building data structures on P to answer range queries of the following three types for any query interval I : (1) top-1 query: find the point in P that lies in I with the highest probability, (2) top-k query: given any integer k ≤ n as part of th...

As the volume of uncertain data increases, the cost of evaluating queries over this data will also increase. In order to scale uncertain databases to large data volumes, efficient query processing methods are needed. One of the key techniques for efficient query evaluation is indexing. Due to the nature of uncertain data and queries over this data, existing indexing solutions for precise data a...

There has been a longstanding interest in building systems that can handle uncertain data. Traditional database systems inherently assume exact data and harbour fundamental limitations when it comes to handling uncertain data. In this dissertation, we present a probabilistic database model that can compactly represent uncertainty models in full generality. Our representation is associated with ...