HIGH-PERFORMANCE PROCESSING OF CONTINUOUS UNCERTAIN DATA MAY 2013 THANH T. L. TRAN B.E., UNIVERSITY OF MELBOURNE M.S., UNIVERSITY OF MASSACHUSETTS AMHERST Ph.D., UNIVERSITY OF MASSACHUSETTS AMHERST Directed by: Professor Yanlei Diao Uncertain data has arisen in a growing number of applications such as sensor networks, RFID systems, weather radar networks, and digital sky surveys. The fact that ...

With the increasing popularity of data streams it has become time to adapt logical and relational learning techniques for dealing with streams. In this note, we present our preliminary results on upgrading the clausal discovery paradigm towards the mining of streams. In this setting, there is a stream of interpretations and the goal is to learn a clausal theory that is satisfied by these interp...

The inherent uncertainty pervasive over the real world often forces business decisions to be made using uncertain data. The conventional relational model does not have the ability to handle uncertain data. In recent years, several approaches have been proposed in the literature for representing uncertain data by extending the relational model, primarily using probability theory. However, the as...

Real-world data are often uncertain and incomplete. In probabilistic relational data models uncertainty can be modeled on two levels. First by representing the uncertain instance of a tuple by a set of possible instances and second by assigning each tuple with its degree of membership to the considered relation. To overcome incompleteness, data from multiple sources need to be combined. In orde...

rapid technological and economic growth over the last several decades has changed human lives and made modern society face complex decision making problems. these kinds of problems are characterized by incommensurate and conflicting criteria or objectives such as cost, reliability, performance, safety and productivity. multi criteria decision making is an approach that can be used to deal compl...

We consider the problem of incrementally learning models from relational data. Most existing learning methods for statistical relational models use batch learning, which becomes computationally expensive and eventually infeasible for large datasets. The majority of the previous work in relational incremental learning assumes the model’s structure is given and only the model’s parameters needed ...

An uncertain data model is a system for representing incomplete or uncertain data. An uncertain database asserts that a database is in one of multiple alternative states (possible worlds), each being a standard database. A probability distribution can be assigned to the set of possible worlds. A detailed account of relational uncertain data models and their evolution, as well as related computa...