Thesis (Ph. D.)--Ohio State University, 1992. / Includes bibliographical references (leaves 116-119). Available online via OhioLINK's ETD Center.

Thesis (Ph. D.)--University of California, Los Angeles, 1993. / Includes vita and abstract. Includes bibliographical references (leaves 145-151).

Dissertation (Ph.D.)--Worcester Polytechnic Institute. / Keywords: View Matching; View Maintenance; Materialized View; Data Warehouse; Information Integration. Includes bibliographical references. (p.206-215)

Thesis (Ph. D.)--Rutgers University, 1991. / Vita. Includes bibliographical references (leaves 180-191).

Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2007. / Rehg, James, Committee Member ; Juang, Biing-Hwang, Committee Member ; Essa, Irfan, Committee Chair.

Thesis (M.S.)--University of Florida, 2005. / Title from title page of source document. Document formatted into pages; contains 99 pages. Includes vita. Includes bibliographical references.

Reviews why it is important to search blogs for competitive intelligence, reputation management, and brand image management. Describes the structure of blogs and how to format searches in several search engines to effectively retrieve this information.

No description available.

by Chu, Kam Wing. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 67-73). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgments --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Related Work --- p.6 / Chapter 3 --- Time-Series Searching with Scaling and Shifting --- p.12 / Chapter 3.1 --- Problem Statement --- p.15 / Chapter 3.2 --- Preliminary --- p.16 / Chapter 3.3 --- Geometrical View of the Problem --- p.18 / Chapter 3.3.1 --- Scale-Shift Transformation --- p.21 / Chapter 3.3.2 --- Determine Scaling Factor and Shifting Offset --- p.24 / Chapter 3.4 --- Algorithm --- p.25 / Chapter 3.4.1 --- MBR Penetration --- p.26 / Chapter 3.4.2 --- Long Sequence --- p.28 / Chapter 3.5 --- Implementation Details --- p.29 / Chapter 3.5.1 --- MBR Penetration Checking --- p.29 / Chapter 3.5.2 --- Dimension Reduction --- p.32 / Chapter 3.6 --- Experimental Results --- p.34 / Chapter 4 --- Metric Space Indexing for Multimedia Databases --- p.38 / Chapter 4.1 --- Preliminaries --- p.39 / Chapter 4.1.1 --- M-tree --- p.39 / Chapter 4.1.2 --- Range Queries --- p.41 / Chapter 4.1.3 --- Nearest Neighbor Queries --- p.44 / Chapter 4.2 --- Nearest Neighbor Search by dmin Only --- p.46 / Chapter 4.3 --- Analysis --- p.50 / Chapter 4.3.1 --- Critical Factor dmin --- p.52 / Chapter 4.4 --- Multiple Bounding Regions --- p.54 / Chapter 4.4.1 --- Computing Multiple Bounding Regions --- p.56 / Chapter 4.4.2 --- New Insert Strategy --- p.58 / Chapter 4.5 --- Experimental Results --- p.58 / Chapter 5 --- Conclusions --- p.64 / Chapter 5.1 --- Time-Series Searching with Scaling and Shifting --- p.64 / Chapter 5.2 --- Metric Space Indexing --- p.65 / Bibliography --- p.67

Artificial Intelligence Lab, Department of MIS, University of Arizona / Information science researchers have recently turned to new artificial intelligence-based inductive learning techniques including neural networks, symbolic learning and genetic algorithms. An overview of the new techniques and their usage in information science research is provided. The algorithms adopted for a hybrid genetic algorithms and neural nets based system, called GANNET, are presented. GANNET performed concept (keyword) optimization for user-selected documents during information retrieval using the genetic algorithms. It then used the optimized concepts to perform concept exploration in a large network of related concepts through the Hopfield net parallel relaxation procedure. Based on a test collection of about 3,000 articles from DIALOG and an automatically created thesaurus, and using Jaccard's score as a performance measure, the experiment showed that GANNET improved the Jaccard's scores by about 50% and helped identify the underlying concepts that best describe the user-selected documents.