I mätrummet på Sandvik Coromant finns en lösning för att visualisera maskinhälsa, mäthistorik och servicetider för olika mätinstrument. Lösningen för datavisualiseringen nyttjar verktyget Power BI och är kopplad till Excelfiler. När data väl hämtats in görs en rad modifieringar på tabellerna för att få fram visualiserbar data. Dessa modifieringar i kombination med många Excelark resulterar i att ledtiderna för att uppdatera en Power BI rapport blir väldigt långa. Nu önskas det att istället nyttja en databaslösning för den data dessa Excelfiler innehåller och därmed förbättra dessa ledtider. Således skapades en databas utifrån den data dessa Excelfiler innehöll. Power BI tillåter användaren att importera data från en databas till applikationen på två sätt, via Import Mode eller DirectQuery. Import Mode läser in samtliga tabeller som efterfrågas och lagrar dessa i minnet. DirectQuery ställer frågor direkt till databasen utifrån vad som efterfrågas. I och med denna skillnad i importsätt finns metoder för att optimera den databas som data läses in ifrån. Studien undersöker hur olika typer av indexering och olika typer av kompression av dessa index påverkar svarstiden på frågor ställda av Power BI för att besvara följande två forskningsfrågor: Hur påverkar olika typer av indexering av en databas datahämtningshastigheten vid användning av Power BI? Hur påverkar olika typer av kompression av index datahämtningshastigheten vid användning av Power BI? Studien utfördes genom att studera execution plans och exekveringshastighet för de frågor som ställdes mot databasen av Power BI. Med hjälp av T-SQL kunde exekveringshastigheten för en specifik fråga tas fram. Denna exekveringshastighet jämfördes sedan för de olika typerna av index och kompression mot exekveringshastigheten för samma fråga mot en tabell helt utan index. Detta utfördes sedan på tabeller med varierande antal rader, där antalet rader som testades var 33 001, 50 081, 100 101, 500 017 och 1 000 217. Resultatet av studien visar att för Import Mode är det bästa typen av index ett clustered rowstore index utan kompression, med undantag för tabeller med över 1 001 217 rader där radkompression presterade bättre. För DirectQuery presterade non-clustered rowstore index bäst, men för vilken kompression var resultatet tvetydigt. Detta eftersom samtliga typer av kompression presterade bäst för olika antal rader i tabellen. För tabeller med fler än 500 017 rader presterade dock ingen kompression allra bäst. / In the measurement room at Sandvik Coromant there is a solution for visualizing machine health, measurement history and service times for different measuring instruments. The data visualization solution uses Power Bi and connects to Excel files. Once the data has been collected, a number of modifications are made on the tables to produce something that is possible to visualize. These modifications in combination with many Excel sheets result in very long lead times for updating a Power BI report. Now it is desired to use a database solution for the data contained in the Excel files and thus improve these lead times. For this, a database was created based on the data that these Excel files contained. Power BI allows the user to import data from a database into the application in two ways, via Import Mode or DirectQuery. Import Mode loads all the requested tables and stores them in memory. DirectQuery runs queries directly to the database, based on what is requested. Due to this difference, there are methods to optimize the database from which the data is loaded. This study examines how different types of indexing and different types of compression affect the response time for queries ran by Power BI to answer the following two research questions: How do different types of indexing affect a database's data retrieval rate when using Power BI? How do different types of compression affect the data retrieval rate when using Power BI? This was done by studying execution plans and execution rate for the queries that was done towards the database by Power BI. With the help of T-SQL, the execution rate for a specific query was obtained. The execution rate for different types of index and compression was then compared against a table without an index. This was then performed on tables with varying numbers of rows, where the numbers of rows that were tested was 33 001, 50 081, 100 101, 500 017 and 1 000 217. The results of the study show that for Import Mode, the best type of index is a clustered rowstore index without compression, with the exception of tables with over 1 001 217 rows where row compression performed better. For DirectQuery, non-clustered rowstore index performed best, but for which compression the result was ambiguous. This was because all types of compression performed best for different number of rows in the table. However, for tables with more than 500 017 rows, no compression performed best.

The aim of this thesis is to create a client xDB database, which supports visualization and management of XML documents and schemas. The first part deals with the introduction of XML, XML schemas (DTD, XML Schema, RelaxNG, etc.) and contextual technologies. After that the thesis deals with the problem of the XML persistence and it focuses on mapping techniques necessary for an efficient storage in a relational database. The main part is devoted to the design and implementation of client application XML Admin, which is programmed in Java. The application uses the XML:DB interface to communicate with the xDB database. It supports storing XML documents to a collection and the XPath language for querying them. The final section is devoted to application performance testing and comparison with existing native database eXist.

This thesis focuses on a implemenation of a a temporal data support within traditional relational environment of PostgreSQL system. I pick up on Radek Jelínek's thesis and an extension developed by him. I've analyzed the extension from functional, practical and performance perspectives. Based on my results, I've designed and implemented changes to the original extension. The work also contains implementation details as well as performance comparison results between the new and the original extensions.

Det finns en stor mängd databaser som används inom många olika sorters användningsområden. Bland dessa finns det sådana som har funktion för att behandla spatiala data. Problemet som detta medför är att välja en databas som kan hantera en viss tänkt typ av spatiala data med bäst prestanda. Denna rapport presenterar en utredning för detta utifrån ett dataset som erhållits från Norconsult Digital. Bland de databaser som valts finns tre SQL databaser (PostgreSQL, MySQL och SQLite) och en NoSQL databas (MongoDB). Dessa databaser genomgick fem likvärdiga operationer/tester som resulterade i att PostgreSQL med dess GiST/SP-GiST index och MongoDB presterade på en nivå långt över resterande databaser som testades. Utifrån detta arbete kan det konstateras att fler utförliga prestandatester bör utföras, där större och mer komplexa dataset, samt fler alternativ till databaser och spatiala index bör finnas med. Detta för att ge en bättre bild över vilka databaser, med stöd för spatiala data, som presterar bättre. / There are a large number of databases that are used in many different areas. Among these, some have a function for processing spatial data. The problem that this entails is the choice of a database that can handle a certain type of spatial data with the best possible performance. This report presents an analysis of this based on a dataset obtained from Norconsult Digital. Among the chosen databases are three SQL databases (PostgreSQL, MySQL and SQLite) and one NoSQL database (MongoDB). These databases underwent five identical operations/tests resulting in PostgreSQL with its GiST/SP-GiST index and MongoDB performing at a level well above the rest of the databases tested. Based on this work, it can be concluded that more detailed performance tests should be carried out, where larger and more complex datasets, as well as more alternatives to databases and spatial indexes, should be included. This is to give a better picture of which databases, with support for spatial data, perform better.

Relational database management systems build on the closed world assumption requiring upfront modeling of a usually stable schema. However, a growing number of today's database applications are characterized by self-descriptive data. The schema of self-descriptive data is very dynamic and prone to frequent changes; a situation which is always troublesome to handle in relational systems. This demo presents the relational database management system FRDM. With flexible relational tables FRDM greatly simplifies the management of self-descriptive data in a relational database system. Self-descriptive data can reside directly next to traditionally modeled data and both can be queried together using SQL. This demo presents the various features of FRDM and provides first-hand experience of the newly gained freedom in relational database systems.

The purpose of this study is to design a database of resources for rational therapy. An investigation of the current health situation and reorientation towards primary health care (PHC) in South Africa evidenced the need for a database of resources which would meet the demand for rational therapy information made on the Helderberg College Library by various user groups as well as make a contribution to the national health information infrastructure. Rational therapy is viewed as an approach within PHC that is rational, common-sense, wholistic and credible, focusing on the prevention and maintenance of health. A model of the steps in database design was developed. A user study identified users' requirements for design and the conceptual schema was developed. The entities, attributes, relationships and policies were presented and graphically summarised in an Entity-Relationship (E-R) diagram. The conceptual schema is the blueprint for further design and implementation of the database. / Information Science / M.Inf.

Este trabalho apresenta o desenvolvimento de um Framework de persistência que utiliza banco de dados objeto-relacional como mecanismo de persistência. Tendo por objetivo usar os conceitos de orientação a objetos descritos na norma SQL:2008, conceitos como: objeto (atributos e métodos), herança, agregação, composição, referências (REF) e estruturas multivaloradas (arrays e multiset). Para tanto se desenvolveu e formalizou (XSD) um arquivo XML que representa um esquema de objetos através da norma SQL:2008. Da mesma forma definiu-se um conjunto de anotações Java, com o intuito de facilitar a utilização e configuração do Framework, o qual é chamado de O-ODBM (Object Object-Relational Database Management). / Persistent Frameworks have been used to aid developers, managing all access to DBMS. This kind of tool maps objects from OO applications to relational databases. Besides, allow developers to persist objects without solid knowledge about DBMSs and specific languages, making better the developers productivity, mainly when different DBMS is used. On the other hands, since 1999 the SQL specification has introduced new features to manipulate objects in relational databases which since then has been called Object-Relational Database (ORDB). At present, many DBMS offer resources to manipulate objects in database, but the most of the application developers just map class to relations tables, failing to exploit the strength of O-R model. The lack of the tools that aid the project of database contributes to this situation. Considering the vantages of use of the persistent frameworks and the ORDBs, this article introduces the O-ODM (Object-Object Database Mapping) a persistent framework for relational object databases.

XML is an emerging standard format for data on the Web as well as in business applications. In order to store and access this information in an efficient manner, database technology must be utilized. A relational database system, the most established and mature technology for query processing and storage, creates a strong foundation for such an XML data management system. However, while relational databases are based on SQL queries, the original user queries are written in XQuery, an XML query language. This XML query language has support for order-sensitive queries as XML is an order-sensitive markup language. A major problem has been discovered with loading XML in a relational database. That problem is the lack of native SQL support for and management of order handling. While XQuery has order and positional support, SQL does not have the same support. For example, individuals who were viewing XML information about music albums would have a hard time querying for the first three songs of a track list from a relational backend. Mapping XML documents to relational backends also proves hard as the data models (hierarchical elements versus flat tables) are so different. For these reasons, and other purposes, the Rainbow System is being developed at WPI as a system that bridges XML data and relational data. This thesis in particular deals with the algebra operators that affect order, order sensitive loading and mapping of XML documents, and the pushdown of order handling into SQL-capable query engines. The contributions of the thesis are the order-sensitive rewrite rules, new XML to relational mappings with different order styles, order-sensitive template-driven SQL generation, and a proposed metadata table for order-sensitive information. A system that implements these proposed techniques with XQuery as the XML query language and Oracle as the backend relational storage system has been developed. Experiments were created to measure execution time based on various factors. First, scalability of the system as backend data set size grows is studied. Second, scalability of the system as results returned from the database grows, and finally, query execution times with different loading types are explored. The experimental results are encouraging. Query execution with the relational backend proves to be much faster than native execution within the Rainbow system. These results confirm the practical utility of our proposed order-sensitive XQuery execution solution over relational data.

Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2016-02-29T10:48:44Z No. of bitstreams: 2 Dissertação - Walquiria Fernandes Marins - 2015.pdf: 2611919 bytes, checksum: 3d20806ae28d0e0e4c891d8dd65cbd88 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2016-02-29T10:54:01Z (GMT) No. of bitstreams: 2 Dissertação - Walquiria Fernandes Marins - 2015.pdf: 2611919 bytes, checksum: 3d20806ae28d0e0e4c891d8dd65cbd88 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2016-02-29T10:54:01Z (GMT). No. of bitstreams: 2 Dissertação - Walquiria Fernandes Marins - 2015.pdf: 2611919 bytes, checksum: 3d20806ae28d0e0e4c891d8dd65cbd88 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2015-10-08 / There is an huge amount of data and information stored digitally. Part of this amount of data is available for consultation by Web, however, a significant portion is hidden, due to its storage form, and can’t be recovered by traditional search engines. This makes users face a common and growing challenge to search and find specific information. This challenge is enhanced by the unpreparedness of users to formulate searches and the limitations inherent in research technologies, that because of syntactic differences, can’t find probably relevant data. Aiming to expand the possibilities of semantic interpretation of a query, this work proposes an approach to interpretation of queries in natural language to relational databases through the use of domain ontologies as a tool for their interpretation and semantic enrichment. With the implementation of the approach and its appreciation against a backdrop of queries without the semantic enrichment it is observed that the approach contributes satisfactorily to identify the user’s intention. / Há uma quantidade enorme de dados e informações armazenadas digitalmente. Uma parte desse volume de dados está disponível para consultas através da Web, entretanto, uma parcela significativa está oculta, devido à sua forma de armazenamento, e não pode ser recuperada pelos mecanismos tradicionais de busca. Isso faz com que os usuários enfrentem um desafio comum e crescente para buscar e encontrar informações específicas. Este desafio é potencializado pelo despreparo dos usuários em formular buscas e pelas limitações inerentes às tecnologias de pesquisa que, em virtude de diferenças sintáticas, não conseguem encontrar dados provavelmente relevantes. Visando ampliar as possibilidades de interpretação semântica de uma consulta, este trabalho propõe uma abordagem de interpretação de consultas em linguagem natural a bancos de dados relacionais através do uso de ontologias de domínio como instrumento para sua interpretação e enriquecimento semântico. Com a implementação da abordagem e sua apreciação em relação a um cená- rio de consultas sem o enriquecimento semântico observa-se que a abordagem contribui satisfatoriamente para identificar a intenção do usuário.

Este trabalho apresenta o desenvolvimento de um Framework de persistência que utiliza banco de dados objeto-relacional como mecanismo de persistência. Tendo por objetivo usar os conceitos de orientação a objetos descritos na norma SQL:2008, conceitos como: objeto (atributos e métodos), herança, agregação, composição, referências (REF) e estruturas multivaloradas (arrays e multiset). Para tanto se desenvolveu e formalizou (XSD) um arquivo XML que representa um esquema de objetos através da norma SQL:2008. Da mesma forma definiu-se um conjunto de anotações Java, com o intuito de facilitar a utilização e configuração do Framework, o qual é chamado de O-ODBM (Object Object-Relational Database Management). / Persistent Frameworks have been used to aid developers, managing all access to DBMS. This kind of tool maps objects from OO applications to relational databases. Besides, allow developers to persist objects without solid knowledge about DBMSs and specific languages, making better the developers productivity, mainly when different DBMS is used. On the other hands, since 1999 the SQL specification has introduced new features to manipulate objects in relational databases which since then has been called Object-Relational Database (ORDB). At present, many DBMS offer resources to manipulate objects in database, but the most of the application developers just map class to relations tables, failing to exploit the strength of O-R model. The lack of the tools that aid the project of database contributes to this situation. Considering the vantages of use of the persistent frameworks and the ORDBs, this article introduces the O-ODM (Object-Object Database Mapping) a persistent framework for relational object databases.