Thesis (M.S.)--West Virginia University, 2001. / Title from document title page. Document formatted into pages; contains iii, 47 p. Vita. Includes abstract. Includes bibliographical references (p. 36-37).

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2006. / Includes bibliographical references (leaves 69-71). Also available in electronic version.

Approved for public release; distribution is unlimited / This thesis describes the development of a prototype application which collects, manages, and distributes knowledge gained by Coast Guard cutter crews making port calls throughout the world. The system uses XML technologies in server/client and stand alone environments. With a web browser, the user views and navigates the system's content from a downloaded file collection or from a centralized data source via a network connection. Users add and modify content with Hypertext Markup Language (HTML) forms using their existing network connections. Client-side data access and navigation, as well as data storage, is performed using non-proprietary standards developed by the World Wide Web Consortium (W3C) and the Internet Engineering Task Force (IETF). The prototype application's purpose is to fulfill the strategic goal of achieving superiority of maritime domain awareness over the areas in which the Coast Guard operates. The need for this application is based upon the lack of specific information from currently available reference publications, the absence of a system to distribute port call knowledge, and the data bandwidth limitations of cutters at sea. The need for knowledge retention aboard cutters is elevated by shortened crewmember assignment lengths due to the stressful and arduous duties of life at sea. / Lieutenant, U.S. Coast Guard

by Kuo Yin-Hung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 91-97). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgments --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Web Document Classification --- p.3 / Chapter 1.2 --- Web Document Integration --- p.5 / Chapter 1.3 --- Dictionary and Incremental Update --- p.5 / Chapter 1.4 --- IR-Tree --- p.6 / Chapter 1.5 --- Thesis Overview --- p.7 / Chapter 2 --- Related Works --- p.9 / Chapter 2.1 --- Semi-structured Data and OEM --- p.9 / Chapter 2.1.1 --- Semi-structured Data --- p.9 / Chapter 2.1.2 --- Object Exchange Model --- p.10 / Chapter 2.2 --- Related Work on Web Document Partitioning --- p.11 / Chapter 2.2.1 --- Retrieval of Authoritatives --- p.12 / Chapter 2.2.2 --- Document Categorization Methodology --- p.13 / Chapter 2.3 --- Semi-structured Data Indexing --- p.14 / Chapter 2.3.1 --- Lore --- p.14 / Chapter 2.3.2 --- Tsimmis --- p.15 / Chapter 2.3.3 --- Other Algorithms --- p.15 / Chapter 2.4 --- Related Work on SAMs --- p.15 / Chapter 2.4.1 --- R-Tree and R*-Tree --- p.16 / Chapter 2.4.2 --- SS-Tree and SR-Tree --- p.16 / Chapter 2.4.3 --- TV-Tree and X-Tree --- p.18 / Chapter 2.5 --- Clustering Algorithms --- p.18 / Chapter 2.5.1 --- DBSCAN and Incremental-DBSCAN --- p.20 / Chapter 3 --- Web Document Classification --- p.21 / Chapter 3.1 --- Basic Definitions --- p.21 / Chapter 3.2 --- Similarity Computation --- p.26 / Chapter 3.2.1 --- Structural Transformation --- p.27 / Chapter 3.2.2 --- Node Similarity --- p.29 / Chapter 3.2.3 --- Edge Label Similarity --- p.30 / Chapter 3.2.4 --- Structural Similarity --- p.31 / Chapter 3.2.5 --- Overall Similarity --- p.32 / Chapter 3.2.6 --- Representative Selection --- p.33 / Chapter 3.3 --- Incremental Update --- p.34 / Chapter 3.3.1 --- Documents related to a subset --- p.35 / Chapter 3.3.2 --- Documents unrelated to any subset --- p.35 / Chapter 3.3.3 --- Documents linking up two or more subsets --- p.35 / Chapter 3.4 --- Experimental Results --- p.36 / Chapter 3.4.1 --- Compare with K-NN --- p.36 / Chapter 3.4.2 --- Representative vs Feature Vector --- p.38 / Chapter 4 --- Web Document Integration --- p.40 / Chapter 4.1 --- Structure Borrowing --- p.40 / Chapter 4.2 --- Integration of Seeds --- p.42 / Chapter 4.3 --- Incremental Update --- p.48 / Chapter 4.3.1 --- New OEM record is a normal record --- p.49 / Chapter 4.3.2 --- New record is a potential seed --- p.50 / Chapter 5 --- Dictionary --- p.51 / Chapter 5.1 --- Structure of a Dictionary Entry --- p.52 / Chapter 5.2 --- Dictionary: Relation Identifier --- p.54 / Chapter 5.3 --- Dictionary: Complement of Representative --- p.55 / Chapter 5.4 --- Incremental Update --- p.56 / Chapter 5.5 --- Experimental Result --- p.57 / Chapter 5.5.1 --- Search based on keyword --- p.57 / Chapter 5.5.2 --- Search by submitting ambiguous words --- p.58 / Chapter 5.5.3 --- Retrieval of related words --- p.59 / Chapter 6 --- Structured Data Manipulation: IR-Tree --- p.61 / Chapter 6.1 --- Range Search vs Nearest Neighbor Search --- p.61 / Chapter 6.2 --- Why R*-Tree and Incremental-DBSCAN? --- p.63 / Chapter 6.3 --- IR-Tree: The Integration of Clustering and Indexing --- p.64 / Chapter 6.3.1 --- Index Structure --- p.64 / Chapter 6.3.2 --- Insertion of IR-Tree --- p.66 / Chapter 6.3.3 --- Deletion on IR-tree --- p.68 / Chapter 6.3.4 --- Nearest Neighbor Search --- p.69 / Chapter 6.3.5 --- Discussion on IR-Tree --- p.73 / Chapter 6.4 --- Experimental Results --- p.73 / Chapter 6.4.1 --- General knn-search performance --- p.74 / Chapter 6.4.2 --- Performance on Varying Dimensionality and Distribution --- p.76 / Chapter 7 --- IM-Tree: An Review --- p.80 / Chapter 7.1 --- Indexing Techniques on Metric Space --- p.80 / Chapter 7.1.1 --- Definition --- p.81 / Chapter 7.1.2 --- Metric Space Indexing Algorithms --- p.81 / Chapter 7.2 --- Clustering Algorithms on Metric Space --- p.83 / Chapter 7.3 --- The Integration of Clustering and Metric-Space Indexing Algorithm --- p.84 / Chapter 7.4 --- Proposed Algorithm --- p.85 / Chapter 7.4.1 --- Index Structure --- p.85 / Chapter 7.4.2 --- Nearest Neighbor Search --- p.86 / Chapter 7.5 --- Future Works --- p.86 / Chapter 8 --- Conclusion and Future Works --- p.87 / Chapter 8.1 --- Semi-structured Data Manipulation --- p.88 / Chapter 8.2 --- Structured Data Manipulation --- p.89

Thesis (M.S.)--Brigham Young University. Dept. of Computer Science, 2004. / Includes bibliographical references (p. 47-50).

Thesis (M.S.)--Brigham Young University. Dept. of Computer Science, 2003. / Includes bibliographical references (p. 51-56).

Not available / text

Thesis (M.S.)--California Polytechnic State University, 1999. / Includes bibliographical references (leaf 55). Also available on microfiche (1 sheet).

Thesis (M.S.)--Rochester Institute of Technology, 1995. / Typescript. Bibliography: leaves 41-44.

Thesis (M.S.)--Brigham Young University. Dept. of Computer Science, 2005. / Includes bibliographical references (p. 71-74).