Efficient data structures for information retrieval

This dissertation deals with the application of efficient data structures and hashing algorithms to the problems of textual information storage and retrieval. We have developed static and dynamic techniques for handling large dictionaries, inverted lists, and optimizations applied to ranking algorithms. We have carried out an experiment called REVTOLC that demonstrated the efficiency and applicability of our algorithms and data structures. Also, the REVTOLC experiment revealed the effectiveness and ease of use of advanced information retrieval methods, namely extended Boolean (p-norm), vector, and vector with probabilistic feedback methods. We have developed efficient static and dynamic data structures and linear algorithms to find a class of minimal perfect hash functions for the efficient implementation of dictionaries, inverted lists, and stop lists. Further, we have developed a linear algorithm that produces order preserving minimal perfect hash functions. These data structures and algorithms enable much faster indexing of textual data and faster retrieval of best match documents using advanced information retrieval methods. Finally, we summarize our research findings and some open problems that are worth further investigation.