Typescript (photocopy). / Digitized by Kansas Correctional Industries / Department: Computer Science.

I denna rapport beskrivs utvecklingen av ett program och en databas för hantering av datatänkt att användas på ett möbelföretag, Horreds Möbel AB. Med en kravspecifikation somutgångspunkt och med hjälp av ett utvecklingsverktyg, UML (Unified Modeling Language)har en mall för programmets utseende arbetats fram. Ur denna mall formas de funktionerprogrammet behöver och hur de rent teoretiskt ser ut. Även vilka objekt, klasser och andraviktiga komponenter som programmet kommer att ha, formas på samma sätt.Med hjälp av två program, MS SQL 2000 och JBuilder2005, och det UML genererat, har endatabas och ett program formats. Dessa kommunicerar med varandra för att hantera data förolika syften. Genom JBuilder2005 skapades ett grafiskt gränssnitt som automatisktgenererade koden för detta. Sedan formades funktionerna manuellt till de olikakomponenterna i programmet.För skapandet av ett användarvänligt gränssnitt tillämpades kunskapen given genom kursenMjukvarudesign på Borås högskola, samt egna tankar och idéer på hur man formararbetsfönstret på ett så tillfredsställande sätt som möjligt för användaren. Som underlag förgränssnittet användes programmen i Microsoft Office.Koden formades klassvis och strukturerades så att en huvudklass ansvarar för grafiken ochskapandet av instanser av de andra klasserna. Databasen formades med tabeller och dessastrukturerades med relationer tagna från företagets produktkatalog.

>Magister Scientiae - MSc / The collection of biological information has a long history, motivated by a variety of reasons and in more recent years is largely being driven for research and academic purposes. As a result biological information is often linked to a specific species or ecosystem management and is discipline specific, not relating to general management actions at a specific conservation site. The biological data that exists is often not consolidated in a central place to allow for effective management of conservation sites. Different databases and formats are often used to cover biological, infrastructural, heritage and management information. Biological information has traditionally not influenced real-time site-specific conservation management, with long term data sets being used to draw conclusions before they can influence management actions. In order to overcome this problem of scattered and unfocused data a biodiversity database related to specific site management was developed. This study focuses on the development of this database and its links to the management of spatially defined sites. Included in the solution of scattered data are the applications of information management tools which interpret data and convert it into management actions, both in terms of long term trends and immediate real- time management actions as the information is received and processed. Information systems are always difficult to describe in words as much of the layout and information is visual and hence difficult to convey I just the text of this document. A breakdown of the resultant information system is outlined in detail in the conclusion section. During the development of a Biodiversity Database it was found that management tools had to be developed to integrated data with management. Furthermore it was found that human error was a significant factor in poor data quality; as a result an observer training programme was developed.

重覆數據删除技術是一個消除冗餘數據存儲塊的技術。尤其是，在儲存數兆位元組的虛擬機器影像時，它已被證明可以減少使用磁碟空間。但是，在會經常加入和讀取虛擬機器影像的雲端平台，部署重覆數據删除技術仍然存在挑戰。我們提出了一個在內核運行的重覆數據删除檔案系統LiveDFS，它可以在一個在低成本硬件配置的開源雲端平台中作為儲存虛擬機器影像的後端。LiveDFS有幾個新穎的特點。具體來說，LiveDFS中最重要的特點是在考慮檔案系統佈局時，它利用空間局部性放置重覆數據删除中繼資料。LiveDFS是POSIX兼容的Linux內核檔案系統。我們透過使用42個不同Linux發行版的虛擬機器影像，在實驗平台測試了LiveDFS的讀取和寫入性能。我們的工作證明了在低成本硬件配置的雲端平台部署LiveDFS的可行性。 / Deduplication is a technique that eliminates the storage of redundant data blocks. In particular, it has been shown to effectively reduce the disk space for storing multi-gigabyte virtual machine (VM) images. However, there remain challenging deployment issues of enabling deduplication in a cloud platform, where VM images are regularly inserted and retrieved. We propose a kernel-space deduplication file systems called LiveDFS, which can serve as a VM image storage backend in an open-source cloud platform that is built on low-cost commodity hardware configurations. LiveDFS is built on several novel design features. Specifically, the main feature of LiveDFS is to exploit spatial locality of placing deduplication metadata on disk with respect to the underlying file system layout. LiveDFS is POSIX-compliant and is implemented as Linux kernel-space file systems. We conduct testbed experiments of the read/write performance of LiveDFS using a dataset of 42 VM images of different Linux distributions. Our work justifies the feasibility of deploying LiveDFS in a cloud platform under commodity settings. / Detailed summary in vernacular field only. / Ng, Chun Ho. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 39-42). / Abstracts also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- LiveDFS Design --- p.5 / Chapter 2.1 --- File System Layout --- p.5 / Chapter 2.2 --- Deduplication Primitives --- p.6 / Chapter 2.3 --- Deduplication Process --- p.8 / Chapter 2.3.1 --- Fingerprint Store --- p.9 / Chapter 2.3.2 --- Fingerprint Filter --- p.11 / Chapter 2.4 --- Prefetching of Fingerprint Stores --- p.14 / Chapter 2.5 --- Journaling --- p.15 / Chapter 2.6 --- Ext4 File System --- p.17 / Chapter 3 --- Implementation Details --- p.18 / Chapter 3.1 --- Choice of Hash Function --- p.18 / Chapter 3.2 --- OpenStack Deployment --- p.19 / Chapter 4 --- Experiments --- p.21 / Chapter 4.1 --- I/O Throughput --- p.21 / Chapter 4.2 --- OpenStack Deployment --- p.26 / Chapter 5 --- Related Work --- p.34 / Chapter 6 --- Conclusions and Future Work --- p.37 / Bibliography --- p.39

在此論文中，我們為一系列應用與數據庫系統的問題設計有理論保證的數據結構與/或算法。這些問題可以分為兩類。第一類是一些計算機科學中的經典問題：近似最近鄰居(approximatenearest neighbor)問題、近似最近點對(approximate closest pair )問題、skyline問題(亦稱maxima問題)和二維正交區域聚合(2d orthogonalrange aggregation)問題。第二類則是在此論文中提出的新問題：歷史分位數(historical quantile)問題、k-跳步最短路徑(k-skipshortest path)問題、XML文檔中的最近關鍵字(nearest keyword)問題、最連通節點(most connected vertex )問題和先入先出索引(FIFOindexing)問題。對於每一個問題，或者我們給出最壞情況亦高效的(worst-case efficient)解決方案；或者當最壞情況性能的意義不大時，我們證明方法的實例最優性(instance optimality)。 / In this thesis, we propose data structures and/or algorithms with theoretical guarantees for solving a series of problems that find applications in database systems. These problems can be classified into two categories. The first one contains several classic problems in computer science, including the approximate nearest neighbor problem, the approximate closest pair problem, the skyline problem (a.k.a. the maxima problem), and 2d orthogonal range search. The second category, on the other hand, consists of problems that are newly introduced by this thesis: the historical quantile problem, the k-skip shortest path problem, the nearest keyword problem on XML documents, the most connected vertex problem, and the FIFO indexing problem. For each problem, we establish either the worstcase efficiency of our solutions, or their instance optimality when worst-case performance is not interesting. / Detailed summary in vernacular field only. / Sheng, Cheng. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 277-298). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Computation models --- p.1 / Chapter 1.2 --- Thesis contributions and organization --- p.2 / Chapter 2 --- Nearest Neighbors and Closest Pairs --- p.7 / Chapter 2.1 --- Introduction --- p.7 / Chapter 2.2 --- Problem settings --- p.10 / Chapter 2.3 --- The preliminaries --- p.11 / Chapter 2.3.1 --- Rigorous-LSH and ball cover --- p.12 / Chapter 2.3.2 --- Adhoc-LSH --- p.13 / Chapter 2.3.3 --- Details of hash functions --- p.15 / Chapter 2.4 --- LSB-tree --- p.16 / Chapter 2.4.1 --- Building a LSB-tree --- p.16 / Chapter 2.4.2 --- Nearest neighbor algorithm --- p.18 / Chapter 2.5 --- Theoretical analysis --- p.21 / Chapter 2.5.1 --- Quality guarantee --- p.21 / Chapter 2.5.2 --- Space and query time --- p.25 / Chapter 2.5.3 --- Comparison with rigorous-LSH --- p.25 / Chapter 2.6 --- Extensions --- p.26 / Chapter 2.7 --- Closest pair search --- p.30 / Chapter 2.7.1 --- Ball pair search --- p.30 / Chapter 2.7.2 --- Solving the closest pair problem --- p.34 / Chapter 2.8 --- Related work --- p.38 / Chapter 2.9 --- Experiments --- p.41 / Chapter 2.9.1 --- Data and queries --- p.41 / Chapter 2.9.2 --- Competitors for nearest neighbor search --- p.42 / Chapter 2.9.3 --- Competitors for closest pair search --- p.43 / Chapter 2.9.4 --- Computing environments and assessment metrics --- p.44 / Chapter 2.9.5 --- Behavior of LSH implementations --- p.45 / Chapter 2.9.6 --- Comparison of NN solutions --- p.48 / Chapter 2.9.7 --- Comparison of CP solutions --- p.51 / Chapter 2.10 --- Chapter summary --- p.54 / Chapter 3 --- The Skyline Problem and Its Variants --- p.57 / Chapter 3.1 --- Introduction --- p.57 / Chapter 3.1.1 --- Previous results --- p.58 / Chapter 3.1.2 --- Our results --- p.60 / Chapter 3.2 --- Preliminaries --- p.63 / Chapter 3.3 --- Our skyline algorithm --- p.66 / Chapter 3.3.1 --- 3d --- p.67 / Chapter 3.3.2 --- 4d --- p.69 / Chapter 3.3.3 --- Higher dimensionalities --- p.70 / Chapter 3.3.4 --- Eliminating the general-position assumption --- p.74 / Chapter 3.4 --- Variants of the skyline problem --- p.74 / Chapter 3.5 --- Low-cardinality domains --- p.77 / Chapter 3.6 --- Non-fixed dimensionality --- p.80 / Chapter 3.6.1 --- An improved algorithm in internal memory --- p.80 / Chapter 3.6.2 --- Externalizing the algorithm --- p.82 / Chapter 3.7 --- Chapter summary --- p.83 / Chapter 4 --- Orthogonal Range Aggregation --- p.85 / Chapter 4.1 --- Introduction --- p.85 / Chapter 4.1.1 --- Applications --- p.86 / Chapter 4.1.2 --- Computation model --- p.87 / Chapter 4.1.3 --- Previous results --- p.87 / Chapter 4.1.4 --- Our results --- p.88 / Chapter 4.2 --- Preliminaries --- p.91 / Chapter 4.3 --- Bundled compressed B-tree --- p.93 / Chapter 4.4 --- Three-sided window-max --- p.95 / Chapter 4.4.1 --- The first structure --- p.96 / Chapter 4.4.2 --- The improved structure --- p.97 / Chapter 4.5 --- Segment-intersection-max --- p.101 / Chapter 4.6 --- Stabbing-max --- p.103 / Chapter 4.6.1 --- Ray-segment-max --- p.103 / Chapter 4.6.2 --- Stabbing-max --- p.106 / Chapter 4.7 --- Rectangle-intersection-sum --- p.106 / Chapter 5 --- Persistent Quantiles --- p.109 / Chapter 5.1 --- Introduction --- p.109 / Chapter 5.1.1 --- Problem definition --- p.111 / Chapter 5.1.2 --- Previous work --- p.112 / Chapter 5.1.3 --- Our main results --- p.113 / Chapter 5.2 --- Space lower bounds for historical quantile search --- p.114 / Chapter 5.3 --- A structure for historical quantile search --- p.119 / Chapter 5.3.1 --- Persistence technique --- p.119 / Chapter 5.3.2 --- High-level rationales and challenges --- p.121 / Chapter 5.3.3 --- The structure and its query algorithm --- p.122 / Chapter 5.3.4 --- Construction algorithm --- p.127 / Chapter 5.3.5 --- Complexity analysis --- p.130 / Chapter 5.3.6 --- An alternative simple solution --- p.132 / Chapter 5.4 --- Experiments --- p.133 / Chapter 5.4.1 --- Competitors and metrics --- p.133 / Chapter 5.4.2 --- Performance characteristics --- p.133 / Chapter 5.4.3 --- Performance on real data --- p.136 / Chapter 5.5 --- Chapter summary --- p.138 / Chapter 6 --- k-Skip Shortest Paths --- p.141 / Chapter 6.1 --- Introduction --- p.141 / Chapter 6.2 --- Related work --- p.144 / Chapter 6.2.1 --- Dijkstra and reach --- p.144 / Chapter 6.2.2 --- More results on SP computation --- p.148 / Chapter 6.3 --- k-skip Shortest Paths --- p.150 / Chapter 6.4 --- k-skip graph --- p.152 / Chapter 6.4.1 --- Size of k-skip cover --- p.152 / Chapter 6.4.2 --- Computing a k-skip cover --- p.154 / Chapter 6.4.3 --- Computing a k-skip graph --- p.156 / Chapter 6.5 --- Query algorithm --- p.158 / Chapter 6.5.1 --- High-level description --- p.158 / Chapter 6.5.2 --- Reach* --- p.160 / Chapter 6.5.3 --- Zoom-in --- p.163 / Chapter 6.6 --- Experiments --- p.164 / Chapter 6.7 --- Chapter summary --- p.168 / Chapter 7 --- Nearest Keyword Queries on XML Documents --- p.171 / Chapter 7.1 --- Introduction --- p.171 / Chapter 7.1.1 --- Motivation --- p.171 / Chapter 7.1.2 --- Contributions --- p.174 / Chapter 7.2 --- Preliminaries --- p.175 / Chapter 7.3 --- Nearest keyword search --- p.178 / Chapter 7.3.1 --- Overview --- p.178 / Chapter 7.3.2 --- TVP characteristics --- p.180 / Chapter 7.3.3 --- Finding the minimum TVP --- p.183 / Chapter 7.4 --- Nearest keyword search as an operator --- p.186 / Chapter 7.4.1 --- XPath evaluation --- p.186 / Chapter 7.4.2 --- Finding approximate group steiner trees --- p.192 / Chapter 7.5 --- Related work --- p.193 / Chapter 7.6 --- Experiments --- p.196 / Chapter 7.7 --- Chapter summary --- p.202 / Chapter 8 --- FIFO Indexes for Decomposable Problems --- p.203 / Chapter 8.1 --- Introduction --- p.203 / Chapter 8.1.1 --- FIFO update scheme and its applications --- p.203 / Chapter 8.1.2 --- Technical motivations --- p.204 / Chapter 8.1.3 --- Problems, computation models, and basic notations --- p.205 / Chapter 8.1.4 --- Previous results --- p.207 / Chapter 8.1.5 --- Our results --- p.210 / Chapter 8.2 --- Making a static structure FIFO --- p.213 / Chapter 8.2.1 --- The RAM model --- p.214 / Chapter 8.2.2 --- The EM model --- p.220 / Chapter 8.3 --- Solving concrete problems --- p.221 / Chapter 8.4 --- Chapter summary --- p.225 / Chapter 9 --- The Most Connected Vertex Problem --- p.227 / Chapter 9.1 --- Introduction --- p.227 / Chapter 9.1.1 --- Motivation --- p.227 / Chapter 9.1.2 --- Our main results --- p.230 / Chapter 9.2 --- Problem and Related Work --- p.232 / Chapter 9.3 --- Preliminaries --- p.235 / Chapter 9.4 --- Exact algorithms --- p.239 / Chapter 9.5 --- Theoretical analysis of the exact algorithms --- p.244 / Chapter 9.5.1 --- The randomized algorithm class --- p.244 / Chapter 9.5.2 --- The deterministic algorithm class --- p.251 / Chapter 9.6 --- Approximate algorithms and their analysis --- p.254 / Chapter 9.6.1 --- 1-MCV --- p.254 / Chapter 9.6.2 --- k-MCV --- p.259 / Chapter 9.7 --- Experiments --- p.262 / Chapter 9.7.1 --- Datasets --- p.262 / Chapter 9.7.2 --- Methods --- p.264 / Chapter 9.7.3 --- How pessimistic is the worst case? --- p.265 / Chapter 9.7.4 --- Performance of random-probe algorithms --- p.266 / Chapter 9.7.5 --- Performance of deterministic-probe algorithms --- p.268 / Chapter 9.7.6 --- Performance of AMCV --- p.269 / Chapter 9.8 --- Chapter summary --- p.274 / Bibliography --- p.277

In the second part, we further investigate algebraic structure of relational databases. The decomposition theory for relational databases is based on data dependencies. Nevertheless, the set-theoretic representations of data dependencies in terms of the attributes of relation schemes are incompatible with partial ordering operations. This brings a gap between the database decomposition theory and our theory. We identify the unique component constraint as a necessary condition for binary decomposition of a relation, i.e. there is a unique component for every join key value in the bipartite graph. We generalize the running intersection property as the partial ordering counterpart under the unique component constraint. It follows that we characterize the multivalued and acyclic join dependencies in terms of commutativity and unique component constraint. This shows the decompositions specified by these dependencies are special cases of our theory. Furthermore, we propose a lossless decomposition method for the class of data dependencies that is based on commutativity, and demonstrate that existing relational operations are sufficient for this method. / Relational information systems, systems that can be represented by tables of finite states, are widely used in many areas such as logic circuits, finite state machines, and relational databases. Decomposition is a natural method to remove redundancy of complex systems. It divides a system into a network of simpler components. In order to preserve the original functionalities of the system, any valid decomposition has to be lossless. This work is divided into two parts. In the first part, we develop a mathematical model for lossless decompositions of relational information systems. Commutative partitions play an important role in decompositions. The commutativity is essentially a general algebraic formulation of independency of two partitions. We express the interdependency of two commutative partitions by a bipartite graph, and classify the hierarchical independency structures by the topological property of bipartite graphs. In particular, we show that two partitions are decomposable, the strongest kind of independency, if and only if the associated bipartite graph is uniform. Moreover, we adopt Shannon's entropy to quantify the amount of information contained in each partition, and formulate information-lossless decompositions by entropy equalities. Under the assumption of running intersection property, we show that the general formulation of information-lossless decompositions of relational information systems is given by the entropy inclusion-exclusion equality. We also present the applications of these formulations to the above engineering systems to manifest the information-lossless decomposition processes. / Lo, Ying Hang. / Adviser: Tony T. Lee. / Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3606. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 159-163). / 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. / Abstracts in English and Chinese. / School code: 1307.

by Huidong Jin. / "August 2002." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (p. 193-204). / 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 Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

by Qiuyue Wang. / "July 2002." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (p. 74-82). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web.

Embora o conceito de Data Warehouse (doravante abreviado DW), em suas várias formas, continue atraindo interesse, muitos projetos de DW não estão gerando os benefícios esperados e muitos estão provando ser excessivamente caro de desenvolver e manter. O presente trabalho visa organizar os conceitos de DW através de uma revisão bibliográfica, discutindo seu real benefício e também de como perceber este benefício a um custo que é aceitável ao empreendimento. Em particular são analisadas metodologias que servirão de embasamento para a proposta de uma metodologia de projeto de DW, que será aplicada a um estudo de caso real para a Cia Zaffari, levando em conta critérios que são encontrados atualmente no desenvolvimento de um Data Warehouse, um subconjunto das quais será tratado no trabalho de dissertação. / Although the concept of Data Warehouse (DW), in its various forms, still attracting interest, many DW projects are not generating the benefits expected and many are proving to be too expensive to develop and to keep. This work organizes the concepts of DW through a literature review, discussing its real benefit and how to realize this benefit at a cost that is acceptable to the company. In particular methods are discussed to serve as a foundation for proposing a design methodology for DW, which will be applied to a real case study for the CIA Zaffari, taking into account criteria that are currently found in developing a data warehouse, a subset of which will be treated in the dissertation.

by Kuok, Chan Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 74-77). / Abstract --- p.i / Acknowledgments --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Data Mining --- p.2 / Chapter 1.2 --- Association Rule Mining --- p.3 / Chapter 2 --- Background --- p.6 / Chapter 2.1 --- Framework of Association Rule Mining --- p.6 / Chapter 2.1.1 --- Large Itemsets --- p.6 / Chapter 2.1.2 --- Association Rules --- p.8 / Chapter 2.2 --- Association Rule Algorithms For Binary Attributes --- p.11 / Chapter 2.2.1 --- AIS --- p.12 / Chapter 2.2.2 --- SETM --- p.13 / Chapter 2.2.3 --- "Apriori, AprioriTid and AprioriHybrid" --- p.15 / Chapter 2.2.4 --- PARTITION --- p.18 / Chapter 2.3 --- Association Rule Algorithms For Numeric Attributes --- p.20 / Chapter 2.3.1 --- Quantitative Association Rules --- p.20 / Chapter 2.3.2 --- Optimized Association Rules --- p.23 / Chapter 3 --- Problem Definition --- p.25 / Chapter 3.1 --- Handling Quantitative Attributes --- p.25 / Chapter 3.1.1 --- Discrete intervals --- p.26 / Chapter 3.1.2 --- Overlapped intervals --- p.27 / Chapter 3.1.3 --- Fuzzy sets --- p.28 / Chapter 3.2 --- Fuzzy association rule --- p.31 / Chapter 3.3 --- Significance factor --- p.32 / Chapter 3.4 --- Certainty factor --- p.36 / Chapter 3.4.1 --- Using significance --- p.37 / Chapter 3.4.2 --- Using correlation --- p.38 / Chapter 3.4.3 --- Significance vs. Correlation --- p.42 / Chapter 4 --- Steps For Mining Fuzzy Association Rules --- p.43 / Chapter 4.1 --- Candidate itemsets generation --- p.44 / Chapter 4.1.1 --- Candidate 1-Itemsets --- p.45 / Chapter 4.1.2 --- Candidate k-Itemsets (k > 1) --- p.47 / Chapter 4.2 --- Large itemsets generation --- p.48 / Chapter 4.3 --- Fuzzy association rules generation --- p.49 / Chapter 5 --- Experimental Results --- p.51 / Chapter 5.1 --- Experiment One --- p.51 / Chapter 5.2 --- Experiment Two --- p.53 / Chapter 5.3 --- Experiment Three --- p.54 / Chapter 5.4 --- Experiment Four --- p.56 / Chapter 5.5 --- Experiment Five --- p.58 / Chapter 5.5.1 --- Number of Itemsets --- p.58 / Chapter 5.5.2 --- Number of Rules --- p.60 / Chapter 5.6 --- Experiment Six --- p.61 / Chapter 5.6.1 --- Varying Significance Threshold --- p.62 / Chapter 5.6.2 --- Varying Membership Threshold --- p.62 / Chapter 5.6.3 --- Varying Confidence Threshold --- p.63 / Chapter 6 --- Discussions --- p.65 / Chapter 6.1 --- User guidance --- p.65 / Chapter 6.2 --- Rule understanding --- p.67 / Chapter 6.3 --- Number of rules --- p.68 / Chapter 7 --- Conclusions and Future Works --- p.70 / Bibliography --- p.74