Managing Uncertainty and Ontologies in Databases
Managing Uncertainty and Ontologies in Databases
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Date
2005-04-18
Authors
Hung, Edward
Advisor
Subrahmanian, V.S.
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Abstract
Nowadays a vast amount of data is generated in Extensible Markup Language (XML). However, it is necessary for applications in some domains to store and manipulate uncertain information, e.g. when the sensor inputs are noisy, or we want to store data that is uncertain. Another big change we can see in applications and web data is the increasing use of ontologies to describe the semantics of data, i.e., the semantic relationships between the terms in the databases.
As such information is usually absent from traditional databases, there is tremendous opportunity to ask new kinds of queries that could not be handled in the past. This provides new challenges on how to manipulate and maintain such new kinds of database systems.
In this dissertation, we will see how we can (i) incorporate and manipulate uncertainty in databases, and (ii) efficiently compute aggregates and maintain views on ontology databases.
First, I explain applications that require manipulating uncertain information in XML databases and maintaining web ontology databases written in Resource Description Framework (RDF). I then introduce the probabilistic semistructured PXML data model with two formal semantics. I describe a set of algebraic operations and its efficient implementation. Aggregations of PXML instances are studied with two semantics proposed: possible-worlds semantics and expectation semantics. Efficient algorithms with pruning are given and evaluated to show their feasibility. I introduce PIXML, an interval probability version of PXML, and develop a formal semantics for it. A query language and its operational semantics are given and proved to be sound and complete. Based on XML, RDF is a language used to describe web ontologies. RDQL, an RDF query language, is extended to support view definition and aggregations. Two sets of algorithms are given to maintain non-aggregate and aggregate views. Experimental results show that they are efficient compared with standard relational view maintenance algorithms.