Large Spatial Database Indexing with aX-tree

Spatial databases are optimized for the management of data stored based on their geometric space. Researchers through high degree scalability have proposed several spatial indexing structures towards this effect. Among these indexing structures is the X-tree. The existing X-trees and its variants are designed for dynamic environment, with the capability for handling insertions and deletions. Notwithstanding, the X-tree degrades on retrieval performance as dimensionality increases and brings about poor worst-case performance than sequential scan. We propose a new X-tree packing techniques for static spatial databases which performs better in space utilization through cautious packing. This new improved structure yields two basic advantage: It reduces the space overhead of the index and produces a better response time, because the aX-tree has a higher fan-out and so the tree always ends up shorter. New model for super-node construction and effective method for optimal packing using an improved str bulk-loading technique is proposed. The study reveals that proposed system performs better than many existing spatial indexing structure

Gestion de métadonnées utilisant tissage et transformation de modèles

The interaction and interoperability between different data sources is a major concern in many organizations. The different formats of data, APIs, and architectures increases the incompatibilities, in a way that interoperability and interaction between components becomes a very difficult task. Model driven engineering (MDE) is a paradigm that enables diminishing interoperability problems by considering every entity as a model. MDE platforms are composed of different kinds of models. Some of the most important kinds of models are transformation models, which are used to define fixed operations between different models. In addition to fixed transformation operations, there are other kinds of interactions and relationships between models. A complete MDE solution must be capable of handling different kinds of relationships. Until now, most research has concentrated on studying transformation languages. This means additional efforts must be undertaken to study these relationships and their implications on a MDE platform. This thesis studies different forms of relationships between models elements. We show through extensive related work that the major limitation of current solutions is the lack of genericity, extensibility and adaptability. We present a generic MDE solution for relationship management called model weaving. Model weaving proposes to capture different kinds of relationships between model elements in a weaving model. A weaving model conforms to extensions of a core weaving metamodel that supports basic relationship management. After proposing the unification of the conceptual foundations related to model weaving, we show how weaving models and transformation models are used as a generic approach for data interoperability. The weaving models are used to produce model transformations. Moreover, we present an adaptive framework for creating weaving models in a semi-automatic way. We validate our approach by developing a generic and adaptive tool called ATLAS Model Weaver (AMW), and by implementing several use cases from different application scenarios.L'interaction et l'interopérabilité entre différentes sources de données sont une préoccupation majeure dans plusieurs organisations. Ce problème devient plus important encore avec la multitude de formats de données, APIs et architectures existants. L'ingénierie dirigée par modèles (IDM) est un paradigme relativement nouveau qui permet de diminuer ces problèmes d'interopérabilité. L'IDM considère toutes les entités d'un système comme un modèle. Les plateformes IDM sont composées par des types de modèles différents. Les modèles de transformation sont des acteurs majeurs de cette approche. Ils sont utilisés pour définir des opérations entre modèles. Par contre, il y existe d'autres types d'interactions qui sont définies sur la base des liens. Une solution d'IDM complète doit supporter des différents types de liens. Les recherches en IDM se sont centrées dans l'étude des transformations de modèles. Par conséquence, il y a beaucoup de travail concernant différents types des liens, ainsi que leurs implications dans une plateforme IDM. Cette thèse étudie des formes différentes de liens entre les éléments de modèles différents. Je montre, à partir d'une étude des nombreux travaux existants, que le point le plus critique de ces solutions est le manque de généricité, extensibilité et adaptabilité. Ensuite, je présente une solution d'IDM générique pour la gestion des liens entre les éléments de modèles. La solution s'appelle le tissage de modèles. Le tissage de modèles propose l'utilisation de modèles de tissage pour capturer des types différents de liens. Un modèle de tissage est conforme à un métamodèle noyau de tissage. J'introduis un ensemble des définitions pour les modèles de tissage et concepts liés. Ensuite, je montre comment les modèles de tissage et modèles de transformations sont une solution générique pour différents problèmes d'interopérabilité des données. Les modèles de tissage sont utilisés pour générer des modèles de transformations. Ensuite, je présente un outil adaptive et générique pour la création de modèles de tissage. L'approche sera validée en implémentant un outil de tissage appel

An Effective Approach to Predicting Large Dataset in Spatial Data Mining Area

Due to enormous quantities of spatial satellite images, telecommunication images, health related tools etc., it is often impractical for users to have detailed and thorough examination of spatial data (S). Large dataset is very common and pervasive in a number of application areas. Discovering or predicting patterns from these datasets is very vital. This research focused on developing new methods, models and techniques for accomplishing advanced spatial data mining (ASDM) tasks. The algorithms were designed to challenge state-of-the-art data technologies and they are tested with randomly generated and actual real-world data. Two main approaches were adopted to achieve the objectives (1) identifying the actual data types (DTs), data structures and spatial content of a given dataset (to make our model versatile and robust) and (2) integrating these data types into an appropriate database management system (DBMS) framework, for easy management and manipulation. These two approaches helped to discover the general and varying types of patterns that exist within any given dataset non-spatial, spatial or even temporal (because spatial data are always influenced by temporal agents) datasets. An iterative method was adopted for system development methodology in this study. The method was adopted as a strategy to combat the irregularity that often exists within spatial datasets. In the course of this study, some of the challenges we encountered which also doubled as current challenges facing spatial data mining includes: (a) time complexity in availing useful data for analysis, (b) time complexity in loading data to storage and (c) difficulties in discovering spatial, non-spatial and temporal correlations between different data objects. However, despite the above challenges, there are some opportunities that spatial data can benefit from including: Cloud computing, Spark technology, Parallelisation, and Bulk-loading methods. Techniques and application areas of spatial data mining (SDM) were identified and their strength and limitations were equally documented. Finally, new methods and algorithms for mining very large data of spatial/non-spatial bias were created. The proposed models/systems are documented in the sections as follows: (a) Development of a new technique for parallel indexing of large dataset (PaX-DBSCAN), (b) Development of new techniques for clustering (X-DBSCAN) in a learning process, (c) Development of a new technique for detecting human skin in an image, (d) Development of a new technique for finding face in an image, (e) Development of a novel technique for management of large spatial and non-spatial datasets (aX-tree). The most prominent among our methods is the new structure used in (c) above -- packed maintained k-dimensional tree (Pmkd-tree), for fast spatial indexing and querying. The structure is a combination system that combines all the proposed algorithms to produce one solid, standard, useful and quality system. The intention of the new final algorithm (system) is to combine the entire initial proposed algorithms to come up with one strong generic effective tool for predicting large dataset SDM area, which it is capable of finding patterns that exist among spatial or non-spatial objects in a DBMS. In addition to Pmkd-tree, we also implemented a novel spatial structure, packed quad-tree (Pquad-Tree), to balance and speed up the performance of the regular quad-tree. Our systems so far have shown a manifestation of efficiency in terms of performance, storage and speed. The final Systems (Pmkd-tree and Pquad-Tree) are generic systems that are flexible, robust, light and stable. They are explicit spatial models for analysing any given problem and for predicting objects as spatially distributed events, using basic SDM algorithms. They can be applied to pattern matching, image processing, computer vision, bioinformatics, information retrieval, machine learning (classification and clustering) and many other computational tasks