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The present study investigated identification, access and usage of periodicals in university libraries in Kenya, with a view of recommending a tool for assisting users to identify information. Using questionnaires completed by 316 university library users and 27 librarians, backed with participant observations, document analysis as well as interviews, it was found that usage of periodicals was low as most users browse through periodicals to identify information, a method that is not effective. In-house indexing was investigated and found to be an effective tool in facilitating access to relevant information. The study recommends establishment of in-house indexing programs and databases in university libraries; formulation of consistent indexing policies to achieve quality indexing; and that indexing should be focused on both content and user requirements by specifying points- of- view, and study methodologies to enhance retrieval of relevant information. / Information Science / M. A. (Information Science)

ITC/USA 2005 Conference Proceedings / The Forty-First Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2005 / Riviera Hotel & Convention Center, Las Vegas, Nevada / Often it is necessary to retrieve segments of video with certain characteristics, or features, from a large archive of footage. This paper discusses how image processing algorithms can be used to automatically create a relational database, which indexes the video archive. This feature extraction can be performed either upon acquisition or in post processing. The database can then be queried to quickly locate and recover video segments with certain specified key features

Artificial Intelligence Lab, Department of MIS, University of Arizona / Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to “intelligent” information retrieval and indexing. More recently, information science researchers have turned to other newer artificial-intelligence- based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms. These newer techniques, which are grounded on diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information storage and retrieval systems. In this article, we first provide an overview of these newer techniques and their use in information science research. To familiarize readers with these techniques, we present three popular methods: the connectionist Hopfield network; the symbolic ID3/ID5R; and evolution- based genetic algorithms. We discuss their knowledge representations and algorithms in the context of information retrieval. Sample implementation and testing results from our own research are also provided for each technique. We believe these techniques are promising in their ability to analyze user queries, identify users’ information needs, and suggest alternatives for search. With proper user-system interactions, these methods can greatly complement the prevailing full-text, keywordbased, probabilistic, and knowledge-based techniques.

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

Cheung Ching Fung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 71-73). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Biological Sequences --- p.2 / Chapter 1.2 --- User Queries on Biological Sequences --- p.4 / Chapter 1.3 --- Research Contributions --- p.6 / Chapter 1.4 --- Organization of Thesis --- p.6 / Chapter 2 --- Background --- p.7 / Chapter 2.1 --- What is a Suffix-Tree? --- p.7 / Chapter 2.2 --- Disk-Based Suffix-Trees --- p.9 / Chapter 3 --- Disk-Based Suffix Tree Constructions --- p.11 / Chapter 3.1 --- An Existing Algorithm: PrePar-Suff ix --- p.11 / Chapter 3.1.1 --- "Three Issues: Edge Splitting, Random Access and Data Skew" --- p.13 / Chapter 3.2 --- DynaCluster-Suffix: A New Novel Disk-Based Suffix-Tree Construction Algorithm --- p.18 / Chapter 4 --- Suffix Links Rebuilt --- p.29 / Chapter 4.1 --- Suffix-links and Least Common Ancestors --- p.29 / Chapter 5 --- q-Length Exact Sequence Matching --- p.35 / Chapter 5.1 --- q-Length Exact Sequence Matching by Suffix-Tree --- p.35 / Chapter 6 --- Implementation --- p.38 / Chapter 6.1 --- System Overview --- p.38 / Chapter 6.1.1 --- Index Builder --- p.39 / Chapter 6.1.2 --- Exact Query Processor --- p.39 / Chapter 6.1.3 --- Suffix Links Regenerator --- p.40 / Chapter 6.1.4 --- Tandem Repeats Finder --- p.40 / Chapter 6.2 --- Data Structures --- p.40 / Chapter 6.2.1 --- Representation of a Node --- p.40 / Chapter 6.2.2 --- An Alternative Node Representation --- p.42 / Chapter 6.2.3 --- Representation of a Leaf --- p.43 / Chapter 6.3 --- Buffering --- p.44 / Chapter 6.3.1 --- Page Format --- p.44 / Chapter 6.3.2 --- Address Translation --- p.45 / Chapter 6.3.3 --- Page Replacement Strategies --- p.45 / Chapter 7 --- A Performance Studies --- p.48 / Chapter 7.1 --- When Everything Can be Held In Memory --- p.52 / Chapter 7.2 --- When Main Memory Is Limited --- p.54 / Chapter 7.3 --- The Effectiveness of DNA Lengths with Fixed Memory Sizes . --- p.56 / Chapter 7.4 --- The Effectiveness of Memory Sizes --- p.57 / Chapter 7.5 --- Answering q-Length Exact Sequence Matching Queries --- p.60 / Chapter 7.6 --- Suffix Link Rebuilt --- p.61 / Chapter 8 --- Conclusions and Future Works --- p.69 / Chapter 8.1 --- Conclusions --- p.69 / Chapter 8.2 --- Future Works --- p.70 / Bibliography --- p.71

Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003. / Includes bibliographical references (leaves 101-104). Also available in electronic version. Access restricted to campus users.

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Time series datasets are useful in a wide range of diverse real world applications. Retrieving or querying from a collection of time series is a fundamental task, with a key example being the similarity query. A similarity query returns all time series from the collection that are similar to a given reference time series. This type of query is particularly useful in prediction and forecasting applications. / A key challenge for similarity queries is efficiency and for large datasets, it is important to develop efficient indexing techniques. Existing approaches in this area are mainly based on the Generic Multimedia Indexing Method (GEMINI), which is a framework that uses spatial indexes such as the R-tree to index reduced time series. For processing a similarity query, the index is first used to prune candidate time series using a lower bounding distance. Then, all remaining time series are compared using the original similarity measure, to derive the query result. Performance within this framework depends on the tightness of the lower bounding distance with respect to the similarity measure. Indeed much work has been focused on representation and dimensionality reduction, in order to provide a tighter lower bounding distance. / Existing work, however, has not used employed dimensionality reduction in a flexible way, requiring all time series to be reduced to have the same dimension. In contrast, in this thesis, we investigate the possibility of allowing a variable dimension reduction. To this end, we develop a new and more flexible tree based indexing structure called the Multi-Resolution Index (MR-Index), which allows dimensionality to vary across different levels of the tree. We provide efficient algorithms for querying, building and maintaining this structure. Through an experimental analysis, we show that the MR-Index can deliver improved query efficiency compared to the traditional R-tree index, using both the Euclidean and dynamic time warping similarity measures.