We live in an information age, and data are ubiquitous today. Various applications, ranging from scientific computing, medical research, and bioinformatics to administrative management, commercial sales, and financial marketing, generate and utilize data every day. Many of these applications are data intensive, with the amount of data involved potentially reaching hundreds of thousands of gigabytes. Further, different applications store data using different data models. For example, applications could store and manage structured data using a flat (relational) model, semi-structured data using a hierarchical (XML) model, and less-structured data using a more general and flexible graph model. In this thesis, I report my research results on efficiently querying large-scale data in relational, XML, and graph-structured data repositories.3.1 Introduction There has been a growing practical need for querying XML data efficiently. In many emerging applications, such as monitoring stock market data, subscribing to realtime news, and managing network traffic information, XMLanbsp;...
|Title||:||Efficient Algorithms for Querying Large-scale Data in Relational, XML, and Graph-structured Data Repositories|
|Publisher||:||ProQuest - 2008|