Safwan Mustafa
M.Sc. Candidate
Supervisor: Dr. Jeffrey Parsons

Integrating Structured Data Using Property Precedence

Monday, July 20, 2015, 11:00am, Room EN 2022
Department of Computer Science

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Abstract

Data integration systems offer uniform access to a set of autonomous and heterogeneous data sources. One of the main challenges in data integration is reconciling semantic differences amongdata sources. Approaches that been used to solve this problem can be categorized as schema-based and attribute-based. Schema-based approaches use schema information to identify the semantic similarity in data; furthermore, they focus on reconciling types before reconciling attributes. In contrast, attribute-based approaches use statistical and structural information of attributes to identify the semantic similarity of data in different sources. This research will examine an approach to semantic reconciliation based on integrating properties expressed at different levels of abstraction or granularity using the concept of property precedence. Property precedence reconciles the meaning of attributes by identifying similarities between attributes based on what these attributes represent in the real world. In order to use property precedence for semantic integration, we need to identify the precedence of attributes within and across data sources. The goal of this research is to develop and evaluate a method and algorithms that will identify precedence relations among attributes and build property precedence graph (PPG) that can be used to support integration.

 

Safwan Mohammad Mustafa
M.Sc. Thesis Proposal
Supervisor: Dr. Jeffrey Parsons

Integrating Structured Data Using Property Precedence

Department of Computer Science
Thursday, October 16, 2014, 1:30 p.m., Room EN 2022

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Abstract

Data integration systems offer uniform access to a set of autonomous and heterogeneous data sources. One of the main challenges in data integration is reconciling semantic differences among data sources. Approaches that been used to solve this problem can be categorized as schema-based
and attribute-based. Schema-based approaches use schema information to identify the semantic similarity in data; furthermore, they focus on reconciling types before reconciling attributes. In contrast, attribute-based approaches use statistical and structural information of attributes to identify
the semantic similarity of data in different sources. This research will examine an approach to semantic reconciliation based on integrating properties expressed at different levels of abstraction or granularity using the concept of property precedence. Propertyprecedence reconciles the meaning of attributes by identifying similarities between attributes based on what these attributes represent in the real world. In order to use property precedence for semantic integration, we need to identify the precedence of attributes within and across data sources. The goal of this research is to develop and evaluate a method and algorithms that will identify precedence relations among attributes and build property precedence graph (PPG) that can be used to support integration.