This thesis researches the problems concerning the specification and implementation of the temporal requirements in data warehouses. The thesis focuses on two areas, firstly, the methods for identifying and capturing the business information needs and associated temporal requirements at the conceptual level and; secondly, methods for classifying and implementing the requirements at the logical level using the relational model. At the conceptual level, eight candidate methodologies were investigated to examine their suitability for the creation of data models that are appropriate for a data warehouse. The methods were evaluated to assess their representation of time, their ability to reflect the dimensional nature of data warehouse models and their simplicity of use. The research found that none of the methods under review fully satisfied the criteria. At the logical level, the research concluded that the methods widely used in current practice result in data structures that are either incapable of answering some very basic questions involving history or that return inaccurate results. Specific proposals are made in three areas. Firstly, a new conceptual model is described that is designed to capture the information requirements for dimensional models and has full support for time. Secondly, a new approach at the logical level is proposed. It provides the data structures that enable the requirements captured in the conceptual model to be implemented, thus enabling the historical questions to be answered simply and accurately. Thirdly, a set of rules is developed to help minimise the inaccuracy caused by time. A guide has been produced that provides practitioners with the tools and instructions on how to implement data warehouses using the methods developed in the thesis.

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Wong, Siu Fung. / Thesis submitted in: December 2003. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 77-84). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgement --- p.vi / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Preliminary --- p.6 / Chapter 2.1 --- Dynamic Time Warping (DTW) --- p.6 / Chapter 2.2 --- Spatial Indexing --- p.10 / Chapter 2.3 --- Relevance Feedback --- p.11 / Chapter 3 --- Literature Review --- p.13 / Chapter 3.1 --- Searching Sequences under Euclidean Metric --- p.13 / Chapter 3.2 --- Searching Sequences under Dynamic Time Warping Metric --- p.17 / Chapter 4 --- Subsequence Matching under Time Warping --- p.21 / Chapter 4.1 --- Subsequence Matching --- p.22 / Chapter 4.1.1 --- Sequential Search --- p.22 / Chapter 4.1.2 --- Indexing Scheme --- p.23 / Chapter 4.2 --- Lower Bound Technique --- p.25 / Chapter 4.2.1 --- Properties of Lower Bound Technique --- p.26 / Chapter 4.2.2 --- Existing Lower Bound Functions --- p.27 / Chapter 4.3 --- Point-Based indexing --- p.28 / Chapter 4.3.1 --- Lower Bound for subsequences matching --- p.28 / Chapter 4.3.2 --- Algorithm --- p.35 / Chapter 4.4 --- Rectangle-Based indexing --- p.37 / Chapter 4.4.1 --- Lower Bound for subsequences matching --- p.37 / Chapter 4.4.2 --- Algorithm --- p.41 / Chapter 4.5 --- Experimental Results --- p.43 / Chapter 4.5.1 --- Candidate ratio vs Width of warping window --- p.44 / Chapter 4.5.2 --- CPU time vs Number of subsequences --- p.45 / Chapter 4.5.3 --- CPU time vs Width of warping window --- p.46 / Chapter 4.5.4 --- CPU time vs Threshold --- p.46 / Chapter 4.6 --- Summary --- p.47 / Chapter 5 --- Relevance Feedback under Time Warping --- p.49 / Chapter 5.1 --- Integrating Relevance Feedback with DTW --- p.49 / Chapter 5.2 --- Query Reformulation --- p.53 / Chapter 5.2.1 --- Constraint Updating --- p.53 / Chapter 5.2.2 --- Weight Updating --- p.55 / Chapter 5.2.3 --- Overall Strategy --- p.58 / Chapter 5.3 --- Experiments and Evaluation --- p.59 / Chapter 5.3.1 --- Effectiveness of the strategy --- p.61 / Chapter 5.3.2 --- Efficiency of the strategy --- p.63 / Chapter 5.3.3 --- Usability --- p.64 / Chapter 5.4 --- Summary --- p.71 / Chapter 6 --- Conclusion --- p.72 / Chapter A --- Deduction of Data Bounding Hyper-rectangle --- p.74 / Chapter B --- Proof of Theorem2 --- p.76 / Bibliography --- p.77 / Publications --- p.84

Lu, Shi. / Thesis submitted in: December 2004. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 100-109). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgement --- p.vi / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation and Objectives --- p.1 / Chapter 1.2 --- Our Contributions --- p.3 / Chapter 1.3 --- Thesis Outline --- p.4 / Chapter 2 --- Related Work --- p.6 / Chapter 2.1 --- Static Video Summary --- p.6 / Chapter 2.2 --- Dynamic Video Skimming --- p.10 / Chapter 2.3 --- Summary --- p.14 / Chapter 3 --- Greedy Method Based Skim Generation --- p.16 / Chapter 3.1 --- Selected Video Features for Video Summarization --- p.17 / Chapter 3.2 --- Video Summarization Problem --- p.18 / Chapter 3.3 --- Experiments --- p.22 / Chapter 3.4 --- Summary --- p.25 / Chapter 4 --- Video Structure Analysis --- p.27 / Chapter 4.1 --- Video Shot Detection --- p.29 / Chapter 4.1.1 --- Shot Cut Detection --- p.30 / Chapter 4.1.2 --- Fade Detection --- p.35 / Chapter 4.2 --- Video Shot Group Construction --- p.38 / Chapter 4.2.1 --- Shot Pairwise Similarity Measure --- p.39 / Chapter 4.2.2 --- Video Shot Grouping by VToC --- p.41 / Chapter 4.2.3 --- Spectral Graph Partitioning --- p.42 / Chapter 4.3 --- Video Scene Detection --- p.46 / Chapter 4.4 --- Shot Arrangement Patterns --- p.48 / Chapter 4.5 --- Experiments --- p.50 / Chapter 4.6 --- Summary --- p.53 / Chapter 5 --- Graph Optimization-Based Video Summary Generation --- p.55 / Chapter 5.1 --- Video Scene Analysis --- p.56 / Chapter 5.1.1 --- Scene Content Entropy --- p.57 / Chapter 5.1.2 --- Target Skim Length Assignment --- p.58 / Chapter 5.2 --- Graph Modelling of Video Scenes --- p.59 / Chapter 5.2.1 --- Decompose the Video Scene into Candidate Video Strings --- p.60 / Chapter 5.2.2 --- The Spatial-Temporal Relation Graph --- p.61 / Chapter 5.2.3 --- The Optimal Skim Problem --- p.62 / Chapter 5.3 --- Graph Optimization --- p.64 / Chapter 5.4 --- Static Video Summary Generation --- p.65 / Chapter 5.5 --- Experiments --- p.68 / Chapter 5.6 --- Summary --- p.74 / Chapter 6 --- Video Content Annotation and Semantic Video Summarization --- p.75 / Chapter 6.1 --- Semantic Video Content Annotation --- p.77 / Chapter 6.1.1 --- Video Shot Segmentation --- p.77 / Chapter 6.1.2 --- Semi-Automatic Video Shot Annotation --- p.77 / Chapter 6.2 --- Video Structures and Semantics --- p.78 / Chapter 6.2.1 --- Video Structure Analysis --- p.78 / Chapter 6.2.2 --- Video Structure and Video Edit Process --- p.80 / Chapter 6.2.3 --- Mutual Reinforcement and Semantic Video Shot Group Detection --- p.81 / Chapter 6.3 --- Semantic Video Summarization --- p.84 / Chapter 6.3.1 --- Summarization Requests and Goals --- p.84 / Chapter 6.3.2 --- Determine the Sub-Skimming Length for Each Scene --- p.85 / Chapter 6.3.3 --- Extracting Video Shots by String Analysis --- p.86 / Chapter 6.4 --- Experiments --- p.88 / Chapter 6.5 --- Summary --- p.92 / Chapter 7 --- Concluding Remarks --- p.93 / Chapter 7.1 --- Summary --- p.93 / Chapter 7.2 --- Future Work --- p.95 / Chapter A --- Notations --- p.97 / Bibliography --- p.100

Time series data is ubiquitous in real world, and the similarity search in time series data is of great importance to many applications. This problem consists of two major parts: how to define the similarity between time series and how to search for similar time series efficiently. As for the similarity measure, the Euclidean distance is a good starting point; however, it also has several limitations. First, it is sensitive to the shifting and scaling transformations. Under a geometric model, we analyze this problem extensively and propose an angle-based similarity measure which is invariant to the shifting and scaling transformations. We then extend the conical index to support for the proposed angle-based similarity measure efficiently. Besides the distortions in amplitude axis, the Euclidean distance is also sensitive to the distortion in time axis; Dynamic Time Warping (DTW) distance is a very good similarity measure which is invariant to the time distortion. However, the time complexity of DTW is high which inhibits its application on large datasets. The index method under DTW distance is a common solution for this problem, and the lower-bound technique plays an important role in the indexing of DTW. We explain the existing lower-bound functions under a unified frame work and propose a group of new lower-bound functions which are much better. Based on the proposed lower-bound functions, an efficient index structure under DTW distance is implemented. In spite of the great success of DTW, it is not very suitable for the time scaling search problem where the time distortion is too large. We modify the traditional DTW distance and propose the Segment-wise Time Warping (STW) distance to adapt to the time scaling search problem. Finally, we devise an efficient search algorithm for the problem of online pattern detection in data streams under DTW distance. / Zhou, Mi. / "January 2007." / Adviser: Man Hon Wong. / Source: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 6100. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 167-180). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract in English and Chinese. / School code: 1307.

Fung, Siu Yuen. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 58-63). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background and Related Work --- p.7 / Chapter 2.1 --- Request Forgery Attacks --- p.7 / Chapter 2.2 --- Current Defense Approaches --- p.10 / Chapter 2.3 --- Lessons Learned --- p.13 / Chapter 3 --- Design of DeRef --- p.15 / Chapter 3.1 --- Threat Model --- p.16 / Chapter 3.2 --- Fine-Grained Access Control --- p.18 / Chapter 3.3 --- Two-Phase Privacy-Preserving Checking --- p.24 / Chapter 3.4 --- Putting It All Together --- p.29 / Chapter 3.5 --- Implementation --- p.33 / Chapter 4 --- Deployment Case Studies --- p.36 / Chapter 4.1 --- WordPress --- p.37 / Chapter 4.2 --- Joomla! and Drupal --- p.42 / Chapter 5 --- Evaluation --- p.44 / Chapter 5.1 --- Performance Overhead of DeRef in Real Deployment --- p.45 / Chapter 5.2 --- Performance Overhead of DeRef with Various Configurations --- p.50 / Chapter 6 --- Conclusions --- p.56 / Bibliography --- p.58

by Sze-Kin Lam. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 71-76). / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Related Work --- p.6 / Chapter 2.1 --- Sequence query processing --- p.8 / Chapter 2.2 --- Text sequence searching --- p.8 / Chapter 2.3 --- Numerical sequence searching --- p.11 / Chapter 2.4 --- Indexing schemes --- p.17 / Chapter 3 --- Sequence Data Searching using the Projection Algorithm --- p.21 / Chapter 3.1 --- Sequence Similarity --- p.21 / Chapter 3.2 --- Searching Method --- p.24 / Chapter 3.2.1 --- Sequential Algorithm --- p.24 / Chapter 3.2.2 --- Projection Algorithm --- p.25 / Chapter 3.3 --- Handling Scaling Problem by the Projection Algorithm --- p.33 / Chapter 4 --- Sequence Data Searching using Hashing Algorithm --- p.37 / Chapter 4.1 --- Sequence Similarity --- p.37 / Chapter 4.2 --- Hashing algorithm --- p.39 / Chapter 4.2.1 --- Motivation of the Algorithm --- p.40 / Chapter 4.2.2 --- Hashing Algorithm using dynamic hash function --- p.44 / Chapter 4.2.3 --- Handling Scaling Problem by the Hashing Algorithm --- p.47 / Chapter 5 --- Comparisons between algorithms --- p.50 / Chapter 5.1 --- Performance comparison with the sequence searching algorithms --- p.54 / Chapter 5.2 --- Comparison between indexing structures --- p.54 / Chapter 5.3 --- Comparison between sequence searching algorithms in coping some deficits --- p.55 / Chapter 6 --- Performance Evaluation --- p.58 / Chapter 6.1 --- Performance Evaluation using Projection Algorithm --- p.58 / Chapter 6.2 --- Performance Evaluation using Hashing Algorithm --- p.61 / Chapter 7 --- Conclusion --- p.66 / Chapter 7.1 --- Motivation of the thesis --- p.66 / Chapter 7.1.1 --- Insufficiency of Euclidean distance --- p.67 / Chapter 7.1.2 --- Insufficiency of orthonormal transforms --- p.67 / Chapter 7.1.3 --- Insufficiency of multi-dimensional indexing structure --- p.68 / Chapter 7.2 --- Major contribution --- p.68 / Chapter 7.2.1 --- Projection algorithm --- p.68 / Chapter 7.2.2 --- Hashing algorithm --- p.69 / Chapter 7.3 --- Future work --- p.70 / Bibliography --- p.71

by Chan Mei Shuen Polly. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 67-70). / Abstract --- p.ii / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Definitions --- p.3 / Chapter 1.2 --- Thesis Overview --- p.5 / Chapter 2 --- Background and Related Work --- p.6 / Chapter 2.1 --- Feature-Based Index Structures --- p.6 / Chapter 2.2 --- Distance Preserving Methods --- p.8 / Chapter 2.3 --- Distance-Based Index Structures --- p.9 / Chapter 2.3.1 --- The Vantage-Point Tree Method --- p.10 / Chapter 3 --- The Problem of Distance Preserving Methods in Querying --- p.12 / Chapter 3.1 --- Some Experimental Results --- p.13 / Chapter 3.2 --- Discussion --- p.15 / Chapter 4 --- Nearest Neighbor Search in VP-trees --- p.17 / Chapter 4.1 --- The sigma-factor Algorithm --- p.18 / Chapter 4.2 --- The Constant-α Algorithm --- p.22 / Chapter 4.3 --- The Single-Pass Algorithm --- p.24 / Chapter 4.4 --- Discussion --- p.25 / Chapter 4.5 --- Performance Evaluation --- p.26 / Chapter 4.5.1 --- Experimental Setup --- p.27 / Chapter 4.5.2 --- Results --- p.28 / Chapter 5 --- Update Operations on VP-trees --- p.41 / Chapter 5.1 --- Insert --- p.41 / Chapter 5.2 --- Delete --- p.48 / Chapter 5.3 --- Performance Evaluation --- p.51 / Chapter 6 --- Minimizing Distance Computations --- p.57 / Chapter 6.1 --- A Single Vantage Point per Level --- p.58 / Chapter 6.2 --- Reuse of Vantage Points --- p.59 / Chapter 6.3 --- Performance Evaluation --- p.60 / Chapter 7 --- Conclusions and Future Work --- p.63 / Chapter 7.1 --- Future Work --- p.65 / Bibliography --- p.67