Guest Editors' introduction: Special section on mining large uncertain and probabilistic databases

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Intelligent Data Storage and Retrieval for Design Optimisation – an Overview

This paper documents the findings of a literature review conducted by the Sir Lawrence Wackett Centre for Aerospace Design Technology at RMIT University. The review investigates aspects of a proposed system for intelligent design optimisation. Such a system would be capable of efficiently storing (and compressing if required) a range of types of design data into an intelligent database. This database would be accessed by the system during subsequent design processes, allowing for search of relevant design data for re-use in later designs, allowing it to become very efficient in reducing the time for later designs as the database grows in size. Extensive research has been performed, in both theoretical aspects of the project, and practical examples of current similar systems. This research covers the areas of database systems, database queries, representation and compression of design data, geometric representation and heuristic methods for design applications.

A query processing system for very large spatial databases using a new map algebra

Dans cette thèse nous introduisons une approche de traitement de requêtes pour des bases de donnée spatiales. Nous expliquons aussi les concepts principaux que nous avons défini et développé: une algèbre spatiale et une approche à base de graphe utilisée dans l'optimisateur. L'algèbre spatiale est défini pour exprimer les requêtes et les règles de transformation pendant les différentes étapes de l'optimisation de requêtes. Nous avons essayé de définir l'algèbre la plus complète que possible pour couvrir une grande variété d'application. L'opérateur algébrique reçoit et produit seulement des carte. Les fonctions reçoivent des cartes et produisent des scalaires ou des objets. L'optimisateur reçoit la requête en expression algébrique et produit un QEP (Query Evaluation Plan) efficace dans deux étapes: génération de QEG (Query Evaluation Graph) et génération de QEP. Dans première étape un graphe (QEG) équivalent de l'expression algébrique est produit. Les règles de transformation sont utilisées pour transformer le graphe a un équivalent plus efficace. Dans deuxième étape un QEP est produit de QEG passé de l'étape précédente. Le QEP est un ensemble des opérations primitives consécutives qui produit les résultats finals (la réponse finale de la requête soumise au base de donnée). Nous avons implémenté l'optimisateur, un générateur de requête spatiale aléatoire, et une base de donnée simulée. La base de donnée spatiale simulée est un ensemble de fonctions pour simuler des opérations spatiales primitives. Les requêtes aléatoires sont soumis à l'optimisateur. Les QEPs générées sont soumis au simulateur de base de données spatiale. Les résultats expérimentaux sont utilisés pour discuter les performances et les caractéristiques de l'optimisateur.Abstract: In this thesis we introduce a query processing approach for spatial databases and explain the main concepts we defined and developed: a spatial algebra and a graph based approach used in the optimizer. The spatial algebra was defined to express queries and transformation rules during different steps of the query optimization. To cover a vast variety of potential applications, we tried to define the algebra as complete as possible. The algebra looks at the spatial data as maps of spatial objects. The algebraic operators act on the maps and result in new maps. Aggregate functions can act on maps and objects and produce objects or basic values (characters, numbers, etc.). The optimizer receives the query in algebraic expression and produces one efficient QEP (Query Evaluation Plan) through two main consecutive blocks: QEG (Query Evaluation Graph) generation and QEP generation. In QEG generation we construct a graph equivalent of the algebraic expression and then apply graph transformation rules to produce one efficient QEG. In QEP generation we receive the efficient QEG and do predicate ordering and approximation and then generate the efficient QEP. The QEP is a set of consecutive phases that must be executed in the specified order. Each phase consist of one or more primitive operations. All primitive operations that are in the same phase can be executed in parallel. We implemented the optimizer, a randomly spatial query generator and a simulated spatial database. The query generator produces random queries for the purpose of testing the optimizer. The simulated spatial database is a set of functions to simulate primitive spatial operations. They return the cost of the corresponding primitive operation according to input parameters. We put randomly generated queries to the optimizer, got the generated QEPs and put them to the spatial database simulator. We used the experimental results to discuss on the optimizer characteristics and performance. The optimizer was designed for databases with a very large number of spatial objects nevertheless most of the concepts we used can be applied to all spatial information systems."--Résumé abrégé par UMI

Data Warehouse and Business Intelligence: Comparative Analysis of Olap tools

Data Warehouse applications are designed basically to provide the business communities with accurate and consolidated information. The objective of Data Warehousing applications are not just for collecting data and reporting, but rather for analyzing, it requires technical and business expertise tools. To achieve business intelligence it requires proper tools to be selected. The most commonly used Business intelligence (BI) technologies are Online Analytical Processing (OLAP) and Reporting tools for analyzing the data and to make tactical decision for the better performance of the organization, and more over to provide quick and fast access to end user request. This study will review data warehouse environment and architecture, business intelligence concepts, OLAP and the related theories involved on it. As well as the concept of data warehouse and OLAP, this study will also present comparative analysis of commonly used OLAP tools in Organization

Query Optimization Technique in Relational Databases�

Computer Scienc