The rise of XML as a de facto standard for document and data exchange has created a need to store and query XML documents in relational databases, today's de facto standard for data storage. Two common strategies for storing XML documents in relational databases, a process known as document shredding, are Interval encoding and ORDPATH Encoding. Interval encoding, which uses a fixed mapping for shredding XML documents, tends to favor selection queries, at a potential cost of O(N) for supporting insertion queries. ORDPATH Encoding, which uses a looser mapping for shredding XML, supports fixed-cost insertions, at a potential cost of longer-running selection queries. Experiments conducted for this research suggest that the breakeven point between the two algorithms occurs when users offer an average 1 insertion to every 5.6 queries, relative to documents of between 1.5 MB and 4 MB in size. However, heterogeneous tests of varying mixes of selects and inserts indicate that Interval always outperforms ORDPATH for mixes ranging from 76% selects to 88% selects. Queries for this experiment and sample documents were drawn from the XMark benchmark suite.

With the rapid growth of XML documents to serve as a popular and major media for storage and interchange of the data on the Web, there is an increasing interest in using existing traditional relational database techniques to store and/or query XML data. Since XQuery is becoming a standard XML query language, significant effort has been made in developing an efficient and comprehensive XQuery-to-SQL query processor. In this thesis, we design and implement an <em>XQuery-to-SQL Query Processor</em> based on the <em>Dynamic Intervals</em> approach. We also provide a comprehensive translation for XQuery basic operations and FLWR expressions. The query processor is able to translate a complex XQuery query, which might include arbitrarily composed and nested FLWR expressions, basic functions, and element constructors, into a single SQL query for RDBMS and a physical plan for the <em>XQuery-enhanced Relational Engine</em>. In order to produce efficient and concise SQL queries, succinct XQuery to SQL translation templates and the optimization algorithms for the SQL query generation are proposed and implemented. The preferable <em>merge-join</em> approach is also proposed to avoid the inefficient <em>nested-loop</em> evaluation for FLWR expressions. <em>Merge-join</em> patterns and query rewriting rules are designed to identify XQuery fragments that can utilize the efficient <em>merge-join</em> evaluation. Proofs of correctness of the approach are provided in the thesis. Experimental results justify the correctness of our work.

With the rapid growth of XML documents to serve as a popular and major media for storage and interchange of the data on the Web, there is an increasing interest in using existing traditional relational database techniques to store and/or query XML data. Since XQuery is becoming a standard XML query language, significant effort has been made in developing an efficient and comprehensive XQuery-to-SQL query processor. In this thesis, we design and implement an <em>XQuery-to-SQL Query Processor</em> based on the <em>Dynamic Intervals</em> approach. We also provide a comprehensive translation for XQuery basic operations and FLWR expressions. The query processor is able to translate a complex XQuery query, which might include arbitrarily composed and nested FLWR expressions, basic functions, and element constructors, into a single SQL query for RDBMS and a physical plan for the <em>XQuery-enhanced Relational Engine</em>. In order to produce efficient and concise SQL queries, succinct XQuery to SQL translation templates and the optimization algorithms for the SQL query generation are proposed and implemented. The preferable <em>merge-join</em> approach is also proposed to avoid the inefficient <em>nested-loop</em> evaluation for FLWR expressions. <em>Merge-join</em> patterns and query rewriting rules are designed to identify XQuery fragments that can utilize the efficient <em>merge-join</em> evaluation. Proofs of correctness of the approach are provided in the thesis. Experimental results justify the correctness of our work.