Uncertainty is a pervasive in real world environment due to vagueness, is associated with the difficulty of making sharp distinctions and ambiguity, is associated with situations in which the choices among several precise alternatives cannot be perfectly resolved. Analysis of large collections of uncertain data is a primary task in the real world applications, because data is incomplete, inaccu...

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...

Information Source Tracking (IST) method has been developed recently for the modeling and manipulation of uncertain and inaccurate data in relational databases. In this paper we extend the IST method to deductive databases. We show that positive uncertain databases, i.e. IST-based deductive databases with only positive literals in the heads and the bodies of the rules, enjoy a least model/least...

Preference queries have been largely studied for relational systems but few proposals exist for stream data systems. Most of the existing proposals concern the skyline, top-k or top-k dominating queries, coupled with the sliding-window operator. However, user preferences queries on data streams may be more sophisticated than simple skyline or top-k and may involve more expressive operations on ...

Scientific applications require processing highvolume on-line streams of numerical data from instruments and simulations. We present an extensible stream database system that allows scalable and flexible continuous queries on such streams. Application dependent streams and query functions are defined through an object-relational model. Distributed execution plans for continuous queries are desc...

A number of uncertain data models have been proposed, based on the notion of compact representations of probability distributions over possible worlds. In probabilistic relational models, tuples are annotated with probabilities or formulae over Boolean random variables. In probabilistic XML models, XML trees are augmented with nodes that specify probability distributions over their children. Bo...

The aim of this paper is to specify formally the Relational Model of Data. This specification could be extended to cover Object-Relational Model of Data and Data Streams. 1. Introduction. In this paper, a Relational Model of Data (RMD) is specified following its original presentation given in [1]. Z-notation is used as formal notation. It is now an international standard [2]. The original prese...

Many real applications consume data that is intrinsically uncertain and error-prone. An uncertain data series is a series whose point values are uncertain. An uncertain data stream is a data stream whose tuples are existentially uncertain and/or have an uncertain value. Typical sources of uncertainty in data series and data streams include sensor data, data synopses, privacy-preserving transfor...