In this thesis we present an automated system that allows users to execute XPATH queries against an XML data source. The system exploits the shared-inlining mapping from XML to Relational data. At the core of the system is an XPATH to SQL transformation algorithm that produces corresponding SQL queries for a subset of XPATH. This approach allows one to utilize standard relational databases to store XML data. Given a DTD, the system creates appropriate relational tables based on the shared-inlining method. The system is capable of transforming an XML data source that conforms to the DTD into relational data. The main component of the system is the XPATH interpreter that parses an XPATH expression for the XML data source and transforms it into an equivalent SQL query. The SQL query is then executed against the relational database and results are packaged into XML and returned as the answer to the XPATH query. The use of the relational database to store and query the XML data is transparent to the user as they interact only with the XPATH interpreter. This methodology provides a novel technique to provide an XML database system implementation. Index Words: XML SQL transformation, XPATH to SQL queries, XSU, Data mapping.

<p>This report includes a development presentation of an information system for managing the staff data within a small company or organization. The system as such as it has been developed is called Employee Management System. It consists of functionally related GUI (application program) and database.</p><p>The choice of the programming tools is individual and particular.</p>

This report includes a development presentation of an information system for managing the staff data within a small company or organization. The system as such as it has been developed is called Employee Management System. It consists of functionally related GUI (application program) and database. The choice of the programming tools is individual and particular.

This master's thesis deals with optimization of structured queries on large databases. Principles of these optimizations are used during creation of application, which allows finding over one specific large database. At the same time this thesis compares efficiency between the new designed SQL constructions and the not optimized SQL constructions.

XML is an emerging standard format for data on the Web as well as in business applications. In order to store and access this information in an efficient manner, database technology must be utilized. A relational database system, the most established and mature technology for query processing and storage, creates a strong foundation for such an XML data management system. However, while relational databases are based on SQL queries, the original user queries are written in XQuery, an XML query language. This XML query language has support for order-sensitive queries as XML is an order-sensitive markup language. A major problem has been discovered with loading XML in a relational database. That problem is the lack of native SQL support for and management of order handling. While XQuery has order and positional support, SQL does not have the same support. For example, individuals who were viewing XML information about music albums would have a hard time querying for the first three songs of a track list from a relational backend. Mapping XML documents to relational backends also proves hard as the data models (hierarchical elements versus flat tables) are so different. For these reasons, and other purposes, the Rainbow System is being developed at WPI as a system that bridges XML data and relational data. This thesis in particular deals with the algebra operators that affect order, order sensitive loading and mapping of XML documents, and the pushdown of order handling into SQL-capable query engines. The contributions of the thesis are the order-sensitive rewrite rules, new XML to relational mappings with different order styles, order-sensitive template-driven SQL generation, and a proposed metadata table for order-sensitive information. A system that implements these proposed techniques with XQuery as the XML query language and Oracle as the backend relational storage system has been developed. Experiments were created to measure execution time based on various factors. First, scalability of the system as backend data set size grows is studied. Second, scalability of the system as results returned from the database grows, and finally, query execution times with different loading types are explored. The experimental results are encouraging. Query execution with the relational backend proves to be much faster than native execution within the Rainbow system. These results confirm the practical utility of our proposed order-sensitive XQuery execution solution over relational data.

In a traditional relational database management system, queries can only be defined over attributes defined in the schema, but are guaranteed to give single, definitive answer structured exactly as specified in the query. In contrast, an information retrieval system allows the user to pose queries without knowledge of a schema, but the result will be a top-k list of possible answers, with no guarantees about the structure or content of the retrieved documents. In this paper, we present DrillBeyond, a novel IR/RDBMS hybrid system, in which the user seamlessly queries a relational database together with a large corpus of tables extracted from a web crawl. The system allows full SQL queries over the relational database, but additionally allows the user to use arbitrary additional attributes in the query that need not to be defined in the schema. The system then processes this semi-specified query by computing a top-k list of possible query evaluations, each based on different candidate web data sources, thus mixing properties of RDBMS and IR systems. We design a novel plan operator that encapsulates a web data retrieval and matching system and allows direct integration of such systems into relational query processing. We then present methods for efficiently processing multiple variants of a query, by producing plans that are optimized for large invariant intermediate results that can be reused between multiple query evaluations. We demonstrate the viability of the operator and our optimization strategies by implementing them in PostgreSQL and evaluating on a standard benchmark by adding arbitrary attributes to its queries.