No description available.

This thesis is concerned with the design and im plementation of a Relational Multimedia Database System, in short RMDBS. RMDBS is designed to efficiently use storage space and manipulate various kinds of data; attribute data, bit-m apped pictures, and program s in binary code. RMDBS is an integrated system which enables the user to manage and control operations on the different forms of data in a user friendly manner. This means that even nonexperienced users can work with the system. The work described in this thesis is novel in that a true multimedia database has been implemented within the framework of a traditional relational DBMS. Previous work in this area has concentrated either in building data base management systems for storing picture-based data or multimedia databases which are not true data base management systems. RMDBS is implemented using the Revelation data base management system.

No description available.

The motivation for this work arose from the recognised inability of relational databases to store and manipulate data that is outside normal commercial applications (e.g. graphical data). The published work in this area is described with respect to the major problems of representation and manipulation of complex data. A general purpose data model, called GDB, that sucessfully tackles these major problems is developed from a formal specification in ML and is implemented using the PRECI/C database system. This model uses three basic graphical primitives (line segments, plane surfaces - facets, and volume elements tetrons) to construct graphical objects and it is shown how user designed primitives can be included. It is argued that graphical database query languages should be designed to be application specific and the user should be protected from the relational algebra which is the basis of the database operations. Such a base language (an extended version of DEAL) is presented which is capable of performing the necessary graphical manipulation by the use of recursive functions and views. The need for object hierarchies is established and the power of the DEAL language is shown to be necessary to handle such complex structures. The importance of integrity constraints is discussed and some ideas for the provision of user defined constraints are put forward.

Database federation is one approach to data integration, in which a middleware, called mediator, provides uniform access to a number of heterogeneous data sources. In this thesis, we focus on the query optimization for distributed joins over database federation. One important observation in query optimization over distributed database system is that run-time conditions (namely available buffer size, CPU utilization in machine and network environment) can significantly affect the execution cost of a query plan. However, in existing database federation systems, very few studies have addressed run-time conditions. It is a challenging problem, because usually the mediator is not able to know the run-time conditions of remote sites and considering run-time conditions will bring about extra complexity to the optimizer. This thesis proposes the Cluster-and-Conquer algorithm for query optimization over database federation while efficiently considering run-time conditions. This algorithm has three-fold benefits. Firstly, the run-time conditions of machines are now available for cluster mediator. Secondly, each cluster mediator can deal with its own sub query concurrently, so the complexity of processing query plan is decreased. Thirdly, the algorithm outperforms other related approaches in terms of“cost of costing", because it removes unnecessary inter-cluster operations in the early stage. I have implemented a prototype data federation system with Cluster-and-Conquer algorithm. The experimental results showed the capabilities and efficiency of our algorithm and described the target scenarios where the algorithm performs better than other related approaches.

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Dai, Juan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 36-39). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Previous Work --- p.3 / Chapter 1.2 --- Proposed Framework --- p.5 / Chapter 1.3 --- Overview --- p.6 / Chapter 2 --- Placement of the Local Classifiers --- p.8 / Chapter 2.1 --- The Uncertainty Map --- p.9 / Chapter 2.2 --- Responsibility Mixture Model --- p.11 / Chapter 2.3 --- EM for Parameter Estimation --- p.12 / Chapter 2.3.1 --- E-Step --- p.14 / Chapter 2.3.2 --- M-Step --- p.15 / Chapter 2.3.3 --- Relationship with Gaussian Mixture Mod- els(GMM) --- p.16 / Chapter 3 --- Fusing of Locally Adaptive Classifiers --- p.18 / Chapter 3.1 --- Training --- p.18 / Chapter 3.2 --- Testing --- p.21 / Chapter 4 --- Algorithmic Characteristics --- p.23 / Chapter 4.1 --- Uncertainty Piloted Placement of Local Classifiers --- p.23 / Chapter 4.2 --- Uncertainty Piloted Fusing of Local Classifiers --- p.24 / Chapter 4.3 --- Related Work --- p.25 / Chapter 5 --- Experiments --- p.27 / Chapter 5.1 --- Dimensionality Reduction --- p.27 / Chapter 5.2 --- Two-Class Classification Problem: Gender Classification --- p.29 / Chapter 5.3 --- Multi-Class Classification: Face Recognition --- p.30 / Chapter 5.3.1 --- Varying the Lighting --- p.31 / Chapter 5.3.2 --- Varying the Pose --- p.32 / Chapter 5.3.3 --- Number of Features Extracted --- p.33 / Chapter 6 --- Conclusion --- p.34 / Bibliography --- p.36

隱私保護是許多應用(特別是和人們有關的)要面對的重要問題。在隱私保護數據發布之研究中，我們探討如何在個人隱私不會被侵犯之情況下發布一個包含個人資料之數據庫，而此數據庫仍包含有用的信息以供研究或其他數據分析之用。 / 本論文著重於隱私保護數據發布之隱私模型及算法。我們首先提出一個預期收益模型，以確認發布一個數據庫會否侵犯個人隱私。預期收益模型符合我們在本論文中提出的六個關於量化私人信息之公理，而第六條公理還會以社會心理學之角度考慮人為因素。而且，這模型考慮敵意信息收集人在發布數據庫之中所得到的好處。所以這模型切實反映出敵意信息收集人利用這些好處而獲得利益，而其他隱私模型並沒有考慮這點。然後，我們還提出了一個算法來生成符合預期收益模型之發布數據庫。我們亦進行了一些包含現實數據庫之實驗來表示出這算法是現實可行的。在那之後，我們提出了一個敏感值抑制算法，使發布數據庫能對負向關聯免疫，而負向關聯是前景/背景知識攻擊之一種。我們亦進行了一些實驗來表示出我們只需要抑制平均數個百份比之敏感值就可以令一個發佈數據庫對負向關聯免疫。最後，我們探討在分散環境之下之隱私保護數據發布，這代表有兩個或以上的數據庫持有人分別生成不同但有關之發布數據庫。我們提出一個在分散環境下可用的相異L多樣性的隱私模型和一個算法來生成符合此模型之發布數據庫。我們亦進行了一些實驗來表示出這算法是現實可行的。 / Privacy preserving is an important issue in many applications, especially for the applications that involve human. In privacy preserving data publishing (PPDP), we study how to publish a database, which contains data records of some individuals, so that the privacy of the individuals is preserved while the published database still contains useful information for research or data analysis. / This thesis focuses on privacy models and algorithms in PPDP. We first propose an expected gain model to define whether privacy is preserved for publishing a database. The expected gain model satisfies the six axioms in quantifying private information proposed in this thesis, where the sixth axiom considers human factors in the view of social psychology. In addition, it considers the amount of advantage gained by an adversary by exploiting the private information deduced from a published database. Hence, the model reflects the reality that the adversary uses such an advantage to earn a profit, which is not conisidered by other existing privacy models. Then, we propose an algorithm to generate published databases that satisfy the expected gain model. Experiments on real datasets are conducted to show that the proposed algorithm is feasible to real applications. After that, we propose a value suppression framework to make the published databases immune to negative association, which is a kind of background / foreground knowledge attacks. Experiments are conducted to show that negative association immunity can be achieved by suppressing only a few percent of sensitive values on average. Finally, we investigate PPDP in a non-centralized environment, in which two or more data holders generate their own different but related published databases. We propose a non-centralized distinct l-diversity requirement as the privacy model and an algorithm to generate published databases for this requirement. Experiments are conducted to show that the proposed algorithm is feasible to real applications. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Cheong, Chi Hong. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 186-193). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Thesis Contributions and Organization --- p.2 / Chapter 1.3 --- Other Related Areas --- p.5 / Chapter 1.3.1 --- Privacy Preserving Data Mining --- p.5 / Chapter 1.3.2 --- Partition-Based Approach vs. Differential Privacy Approach --- p.5 / Chapter 2 --- Expected Gain Model --- p.7 / Chapter 2.1 --- Introduction --- p.8 / Chapter 2.1.1 --- Background and Motivation --- p.8 / Chapter 2.1.2 --- Contributions --- p.11 / Chapter 2.2 --- Table Models --- p.12 / Chapter 2.2.1 --- Private Table --- p.12 / Chapter 2.2.2 --- Published Table --- p.13 / Chapter 2.3 --- Private Information Model --- p.14 / Chapter 2.3.1 --- Proposition --- p.14 / Chapter 2.3.2 --- Private Information and Private Probability --- p.15 / Chapter 2.3.3 --- Public Information and Public Probability --- p.18 / Chapter 2.3.4 --- Axioms in Quantifying Private Information --- p.20 / Chapter 2.4 --- Quantifying Private Information --- p.34 / Chapter 2.4.1 --- Expected Gain of a Fair Guessing Game --- p.34 / Chapter 2.4.2 --- Analysis --- p.41 / Chapter 2.5 --- Tuning the Importance of Opposite Information --- p.48 / Chapter 2.6 --- Conclusions --- p.53 / Chapter 3 --- Generalized Expected Gain Model --- p.56 / Chapter 3.1 --- Introduction --- p.58 / Chapter 3.2 --- Table Models --- p.60 / Chapter 3.2.1 --- Private Table --- p.62 / Chapter 3.2.2 --- Published Table --- p.62 / Chapter 3.3 --- Expected Gain Model --- p.63 / Chapter 3.3.1 --- Random Variable and Probability Distribution --- p.64 / Chapter 3.3.2 --- Public Information --- p.64 / Chapter 3.3.3 --- Private Information --- p.65 / Chapter 3.3.4 --- Expected Gain Model --- p.66 / Chapter 3.4 --- Generalization Algorithm --- p.75 / Chapter 3.4.1 --- Generalization Property and Subset Property --- p.75 / Chapter 3.4.2 --- Modified Version of Incognito --- p.78 / Chapter 3.5 --- Related Work --- p.80 / Chapter 3.5.1 --- k-Anonymity --- p.80 / Chapter 3.5.2 --- l-Diversity --- p.81 / Chapter 3.5.3 --- Confidence Bounding --- p.83 / Chapter 3.5.4 --- t-Closeness --- p.84 / Chapter 3.6 --- Experiments --- p.85 / Chapter 3.6.1 --- Experiment Set 1: Average/Max/Min Expected Gain --- p.85 / Chapter 3.6.2 --- Experiment Set 2: Expected Gain Distribution --- p.90 / Chapter 3.6.3 --- Experiment Set 3: Modified Version of Incognito --- p.95 / Chapter 3.7 --- Conclusions --- p.99 / Chapter 4 --- Negative Association Immunity --- p.100 / Chapter 4.1 --- Introduction --- p.100 / Chapter 4.2 --- Related Work --- p.104 / Chapter 4.3 --- Negative Association Immunity and Value Suppression --- p.107 / Chapter 4.3.1 --- Negative Association --- p.108 / Chapter 4.3.2 --- Negative Association Immunity --- p.111 / Chapter 4.3.3 --- Achieving Negative Association Immunity by Value Suppression --- p.114 / Chapter 4.4 --- Local Search Algorithm --- p.123 / Chapter 4.5 --- Experiments --- p.125 / Chapter 4.5.1 --- Settings --- p.125 / Chapter 4.5.2 --- Results and Discussions --- p.128 / Chapter 4.6 --- Conclusions --- p.129 / Chapter 5 --- Non-Centralized Distinct l-Diversity --- p.130 / Chapter 5.1 --- Introduction --- p.130 / Chapter 5.2 --- Related Work --- p.138 / Chapter 5.3 --- Table Models --- p.140 / Chapter 5.3.1 --- Private Tables --- p.140 / Chapter 5.3.2 --- Published Tables --- p.141 / Chapter 5.4 --- Private Information Deduced from Multiple Published Tables --- p.143 / Chapter 5.4.1 --- Private Information Deduced by Simple Counting on Each Published Tables --- p.143 / Chapter 5.4.2 --- Private Information Deduced from Multiple Published Tables --- p.145 / Chapter 5.4.3 --- Probabilistic Table --- p.156 / Chapter 5.5 --- Non-Centralized Distinct l-Diversity and Algorithm --- p.158 / Chapter 5.5.1 --- Non-centralized Distinct l-diversity --- p.159 / Chapter 5.5.2 --- Algorithm --- p.165 / Chapter 5.5.3 --- Theorems --- p.171 / Chapter 5.6 --- Experiments --- p.174 / Chapter 5.6.1 --- Settings --- p.174 / Chapter 5.6.2 --- Metrics --- p.176 / Chapter 5.6.3 --- Results and Discussions --- p.179 / Chapter 5.7 --- Conclusions --- p.181 / Chapter 6 --- Conclusions --- p.183 / Bibliography --- p.186

Abstract not available

A materialized view is a derived data collecton stored in a database. When the source data for a materialized view is updated, the materialized view also needs to be updated. The process of updating a materialized view in response to changes in the source data is called view maintenance. There are two methods for maintaining a materialized view - recomputation and incremental computation. Recomputation computes the new view instance from scratch using the updated sources data. Incremental computation on the other hand, computes the new view instance by using the update to the source data, the old view instance, and possibly some source data. Incremental computation is widely accepted as a less expensive mathod of maintaining a view when the size of the update to the source data is small in relation to the size of the source data. / thesis (PhD)--University of South Australia, 2000