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|Title: ||Ranked search over structured and semi-structured data|
|Authors: ||Lin, Guo|
|Keywords: ||ranked search|
|Issue Date: ||2-Jul-2007|
|Abstract: ||Traditionally, relational database systems have been designed for precise
queries over structured data, while information retrieval systems have been
designed for more flexible ranked keyword search queries over unstructured
(text) data. However, many new and emerging applications require data
management capabilities that combine the benefits of database and information
retrieval systems, e.g., e-commerce and content management applications. In
this dissertation, we have proposed and initiated steps toward the larger
goal of integrating relational database and information retrieval systems.
First, we consider the problem of ranked text search in relational databases,
where the traditional ranking paradigms and techniques developed for stand-alone
unstructured documents are not directly applicable. We thus propose a new
ranking paradigm that uses structured data values to score the results of
text search queries. Our experimental results on real and synthetic data
sets show that we can support the new ranking paradigm efficiently in
Second, we explore a novel problem of discovering rich relationships in
databases based on user queries with text search and structural query conditions.
Toward this end, we have introduced the notion of data topology and developed
efficient algorithms for computing ranked topologies based on user queries.
We have evaluated our algorithms using a real biological database, the
Biozon database (http://www.biozon.org).
Third, we consider the problem of efficiently producing results for keyword
search queries over semi-structured XML documents. We present the XRANK system
that is built to address the novel challenges for effective XML keyword search
as apposed to HTML keyword search. Our experimental results show that XRANK
offers both space and performance benefits when compared with existing
|Appears in Collections:||Theses and Dissertations (OPEN)|
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