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Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1992. / Vita. Abstract. Includes bibliographical references (leaves 93-95). Also available via the Internet.

Le choix d'un système de gestion de bases de données (SGBD) et de plateforme d'exécution pour le déploiement est une tâche primordiale pour la satisfaction des besoins non-fonctionnels(comme la performance temporelle et la consommation d'énergie). La difficulté de ce choix explique la multitude de tests pour évaluer la qualité des bases de données (BD) développées.Cette évaluation se base essentiellement sur l'utilisation des métriques associées aux besoins non fonctionnels. En effet, une mine de tests existe couvrant toutes les phases de cycle de vie de conception d'une BD. Les tests et leurs environnements sont généralement publiés dans des articles scientifiques ou dans des sites web dédiés comme le TPC (Transaction Processing Council).Par conséquent, cette thèse contribue à la capitalisation et l'exploitation des tests effectués afin de diminuer la complexité du processus de choix. En analysant finement les tests, nous remarquons que chaque test porte sur les jeux de données utilisés, la plateforme d'exécution, les besoins non fonctionnels, les requêtes, etc. Nous proposons une démarche de conceptualisation et de persistance de toutes .ces dimensions ainsi que les résultats de tests. Cette thèse a donné lieu aux trois contributions. (1) Une conceptualisation basée sur des modélisations descriptive,prescriptive et ontologique pour expliciter les différentes dimensions. (2) Le développement d'un entrepôt de tests multidimensionnel permettant de stocker les environnements de tests et leurs résultats. (3) Le développement d'une méthodologie de prise de décision basée sur un système de recommandation de SGBD et de plateformes. / Choosing appropriate database management systems (DBMS) and/or execution platforms for given database (DB) is complex and tends to be time- and effort-intensive since this choice has an important impact on the satisfaction of non-functional requirements (e.g., temporal performance or energy consumption). lndeed, a large number of tests have been performed for assessing the quality of developed DB. This assessment often involves metrics associated with non-functional requirement. That leads to a mine of tests covering all life-cycle phases of the DB's design. Tests and their environments are usually published in scientific articles or specific websites such as Transaction Processing Council (TPC). Therefore, this thesis bas taken a special interest to the capitalization and the reutilization of performed tests to reduce and mastery the complexity of the DBMS/platforms selection process. By analyzing the test accurately, we identify that tests concem: the data set, the execution platform, the addressed non-functional requirements, the used queries, etc. Thus, we propose an approach of conceptualization and persistence of all dimensions as well as the results of tests. Conseguently, this thesis leads to the following contributions. (1) The design model based on descriptive, prescriptive and ontological concepts to raise the different dimensions. (2) The development of a multidimensional repository to store the test environments and their results. (3) The development of a decision making methodology based on a recommender system for DBMS and platforms selection.

Knowing which database management system to use for a project is difficult to know in advance. Luckily, there are tools that can help the developer apply the same database design on multiple different database management systems without having to change the code. In this thesis, we investigate the strengths of SQLAlchemy, which is an SQL toolkit for Python. We compared SQLite, PostgreSQL and MySQL using SQLAlchemy as well as compared a pure MySQL implementation against the results from SQLAlchemy. We conclude that, for our database design, PostgreSQL was the best database management system and that for the average SQL-user, SQLAlchemy is an excellent substitution to writing regular SQL.

Although there has been a rise in the use of recycled materials in highway and geotechnical systems, many tons of potentially useful industrial and domestic by-products are still being discarded in the United States each year. While extensive research has been conducted to investigate the use of recycled materials in engineering applications, the dissemination of the findings is often limited. The problem is compounded by the lack of a single resource containing relevant engineering and environmental characteristics of each material; the tendency of the researchers to publish their findings in technical reports rather than archived publications; and the wide discrepancies among local and state environmental regulations and acceptability. A relational database is proposed as a method to improve implementation of recycled material research. A comprehensive review is conducted on data available for a wide variety of recycled materials and their usage in highway and geotechnical applications. Mechanical and environmental data and information from case histories are organized into approximately 10 tables in a relational database management system. More than 30 parameters, including engineering properties, availability and cost, are recorded for 23 materials in a highly-organized compendium. Through a simple user interface, a vast amount of data can be sorted to implement a recycled material program based on historic and current data. The DBMS is updatable and the design is amendable to account for future expansion.

The Karst Feature Database (KFD) of Minnesota is a relational GIS-based Database Management System (DBMS). Previous karst feature datasets used inconsistent attributes to describe karst features in different areas of Minnesota. Existing metadata were modified and standardized to represent a comprehensive metadata for all the karst features in Minnesota. Microsoft Access 2000 and ArcView 3.2 were used to develop this working database. Existing county and sub-county karst feature datasets have been assembled into the KFD, which is capable of visualizing and analyzing the entire data set. By November 17 2002, 11,682 karst features were stored in the KFD of Minnesota. Data tables are stored in a Microsoft Access 2000 DBMS and linked to corresponding ArcView applications. The current KFD of Minnesota has been moved from a Windows NT server to a Windows 2000 Citrix server accessible to researchers and planners through networked interfaces.

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