Diseño e implementación de la base de datos para la gestión de regalos grupales de una red social

El presente trabajo consiste en la creación de una base de datos para generar una aplicación que permita la gestión de un regalo grupal.

Abbildung von XML-Dokumenten auf SQL:2003-konforme Datentypen

Die Auszeichnungssprache XML dient zur Annotation von Dokumenten und hat sich als Standard-Datenaustauschformat durchgesetzt. Dabei entsteht der Bedarf, XML-Dokumente nicht nur als reine Textdateien zu speichern und zu transferieren, sondern sie auch persistent in besser strukturierter Form abzulegen. Dies kann unter anderem in speziellen XML- oder relationalen Datenbanken geschehen. Relationale Datenbanken setzen dazu bisher auf zwei grundsätzlich verschiedene Verfahren: Die XML-Dokumente werden entweder unverändert als binäre oder Zeichenkettenobjekte gespeichert oder aber aufgespalten, sodass sie in herkömmlichen relationalen Tabellen normalisiert abgelegt werden können (so genanntes „Flachklopfen“ oder „Schreddern“ der hierarchischen Struktur). Diese Dissertation verfolgt einen neuen Ansatz, der einen Mittelweg zwischen den bisherigen Lösungen darstellt und die Möglichkeiten des weiterentwickelten SQL-Standards aufgreift. SQL:2003 definiert komplexe Struktur- und Kollektionstypen (Tupel, Felder, Listen, Mengen, Multimengen), die es erlauben, XML-Dokumente derart auf relationale Strukturen abzubilden, dass der hierarchische Aufbau erhalten bleibt. Dies bietet zwei Vorteile: Einerseits stehen bewährte Technologien, die aus dem Bereich der relationalen Datenbanken stammen, uneingeschränkt zur Verfügung. Andererseits lässt sich mit Hilfe der SQL:2003-Typen die inhärente Baumstruktur der XML-Dokumente bewahren, sodass es nicht erforderlich ist, diese im Bedarfsfall durch aufwendige Joins aus den meist normalisierten und auf mehrere Tabellen verteilten Tupeln zusammenzusetzen. In dieser Arbeit werden zunächst grundsätzliche Fragen zu passenden, effizienten Abbildungsformen von XML-Dokumenten auf SQL:2003-konforme Datentypen geklärt. Darauf aufbauend wird ein geeignetes, umkehrbares Umsetzungsverfahren entwickelt, das im Rahmen einer prototypischen Applikation implementiert und analysiert wird. Beim Entwurf des Abbildungsverfahrens wird besonderer Wert auf die Einsatzmöglichkeit in Verbindung mit einem existierenden, ausgereiften relationalen Datenbankmanagementsystem (DBMS) gelegt. Da die Unterstützung von SQL:2003 in den kommerziellen DBMS bisher nur unvollständig ist, muss untersucht werden, inwieweit sich die einzelnen Systeme für das zu implementierende Abbildungsverfahren eignen. Dabei stellt sich heraus, dass unter den betrachteten Produkten das DBMS IBM Informix die beste Unterstützung für komplexe Struktur- und Kollektionstypen bietet. Um die Leistungsfähigkeit des Verfahrens besser beurteilen zu können, nimmt die Arbeit Untersuchungen des nötigen Zeitbedarfs und des erforderlichen Arbeits- und Datenbankspeichers der Implementierung vor und bewertet die Ergebnisse.

Model Configuration And Data Management In The Short-Term Water Information Forecasting Tools

The Short-term Water Information and Forecasting Tools (SWIFT) is a suite of tools for flood and short-term streamflow forecasting, consisting of a collection of hydrologic model components and utilities. Catchments are modeled using conceptual subareas and a node-link structure for channel routing. The tools comprise modules for calibration, model state updating, output error correction, ensemble runs and data assimilation. Given the combinatorial nature of the modelling experiments and the sub-daily time steps typically used for simulations, the volume of model configurations and time series data is substantial and its management is not trivial. SWIFT is currently used mostly for research purposes but has also been used operationally, with intersecting but significantly different requirements. Early versions of SWIFT used mostly ad-hoc text files handled via Fortran code, with limited use of netCDF for time series data. The configuration and data handling modules have since been redesigned. The model configuration now follows a design where the data model is decoupled from the on-disk persistence mechanism. For research purposes the preferred on-disk format is JSON, to leverage numerous software libraries in a variety of languages, while retaining the legacy option of custom tab-separated text formats when it is a preferred access arrangement for the researcher. By decoupling data model and data persistence, it is much easier to interchangeably use for instance relational databases to provide stricter provenance and audit trail capabilities in an operational flood forecasting context. For the time series data, given the volume and required throughput, text based formats are usually inadequate. A schema derived from CF conventions has been designed to efficiently handle time series for SWIFT.

From XML to relational view updates: applying old solutions to solve a new problem

XML has become an important medium for data exchange, and is frequently used as an interface to - i.e. a view of - a relational database. Although lots of work have been done on querying relational databases through XML views, the problem of updating relational databases through XML views has not received much attention. In this work, we give the rst steps towards solving this problem. Using query trees to capture the notions of selection, projection, nesting, grouping, and heterogeneous sets found throughout most XML query languages, we show how XML views expressed using query trees can be mapped to a set of corresponding relational views. Thus, we transform the problem of updating relational databases through XML views into a classical problem of updating relational databases through relational views. We then show how updates on the XML view are mapped to updates on the corresponding relational views. Existing work on updating relational views can then be leveraged to determine whether or not the relational views are updatable with respect to the relational updates, and if so, to translate the updates to the underlying relational database. Since query trees are a formal characterization of view de nition queries, they are not well suited for end-users. We then investigate how a subset of XQuery can be used as a top level language, and show how query trees can be used as an intermediate representation of view de nitions expressed in this subset.

Comparative study of algorithms for mining association rules: Traditional approach versus multi-relational approach

The multi-relational Data Mining approach has emerged as alternative to the analysis of structured data, such as relational databases. Unlike traditional algorithms, the multi-relational proposals allow mining directly multiple tables, avoiding the costly join operations. In this paper, is presented a comparative study involving the traditional Patricia Mine algorithm and its corresponding multi-relational proposed, MR-Radix in order to evaluate the performance of two approaches for mining association rules are used for relational databases. This study presents two original contributions: the proposition of an algorithm multi-relational MR-Radix, which is efficient for use in relational databases, both in terms of execution time and in relation to memory usage and the presentation of the empirical approach multirelational advantage in performance over several tables, which avoids the costly join operations from multiple tables. © 2011 IEEE.

A intervenção do design nas superfícies projetadas: processos multifacetados e estudos de caso

Pós-graduação em Design - FAAC

MR-Radix: a multi-relational data mining algorithm

Background: Once multi-relational approach has emerged as an alternative for analyzing structured data such as relational databases, since they allow applying data mining in multiple tables directly, thus avoiding expensive joining operations and semantic losses, this work proposes an algorithm with multi-relational approach. Methods: Aiming to compare traditional approach performance and multi-relational for mining association rules, this paper discusses an empirical study between PatriciaMine - an traditional algorithm - and its corresponding multi-relational proposed, MR-Radix. Results: This work showed advantages of the multi-relational approach in performance over several tables, which avoids the high cost for joining operations from multiple tables and semantic losses. The performance provided by the algorithm MR-Radix shows faster than PatriciaMine, despite handling complex multi-relational patterns. The utilized memory indicates a more conservative growth curve for MR-Radix than PatriciaMine, which shows the increase in demand of frequent items in MR-Radix does not result in a significant growth of utilized memory like in PatriciaMine. Conclusion: The comparative study between PatriciaMine and MR-Radix confirmed efficacy of the multi-relational approach in data mining process both in terms of execution time and in relation to memory usage. Besides that, the multi-relational proposed algorithm, unlike other algorithms of this approach, is efficient for use in large relational databases.

MR-Radix: a multi-relational data mining algorithm

Abstract Background Once multi-relational approach has emerged as an alternative for analyzing structured data such as relational databases, since they allow applying data mining in multiple tables directly, thus avoiding expensive joining operations and semantic losses, this work proposes an algorithm with multi-relational approach. Methods Aiming to compare traditional approach performance and multi-relational for mining association rules, this paper discusses an empirical study between PatriciaMine - an traditional algorithm - and its corresponding multi-relational proposed, MR-Radix. Results This work showed advantages of the multi-relational approach in performance over several tables, which avoids the high cost for joining operations from multiple tables and semantic losses. The performance provided by the algorithm MR-Radix shows faster than PatriciaMine, despite handling complex multi-relational patterns. The utilized memory indicates a more conservative growth curve for MR-Radix than PatriciaMine, which shows the increase in demand of frequent items in MR-Radix does not result in a significant growth of utilized memory like in PatriciaMine. Conclusion The comparative study between PatriciaMine and MR-Radix confirmed efficacy of the multi-relational approach in data mining process both in terms of execution time and in relation to memory usage. Besides that, the multi-relational proposed algorithm, unlike other algorithms of this approach, is efficient for use in large relational databases.

The ciao prolog system

Ciao is a public domain, next generation multi-paradigm programming environment with a unique set of features: Ciao offers a complete Prolog system, supporting ISO-Prolog, but its novel modular design allows both restricting and extending the language. As a result, it allows working with fully declarative subsets of Prolog and also to extend these subsets (or ISO-Prolog) both syntactically and semantically. Most importantly, these restrictions and extensions can be activated separately on each program module so that several extensions can coexist in the same application for different modules. Ciao also supports (through such extensions) programming with functions, higher-order (with predicate abstractions), constraints, and objects, as well as feature terms (records), persistence, several control rules (breadth-first search, iterative deepening, ...), concurrency (threads/engines), a good base for distributed execution (agents), and parallel execution. Libraries also support WWW programming, sockets, external interfaces (C, Java, TclTk, relational databases, etc.), etc. Ciao offers support for programming in the large with a robust module/object system, module-based separate/incremental compilation (automatically -no need for makefiles), an assertion language for declaring (optional) program properties (including types and modes, but also determinacy, non-failure, cost, etc.), automatic static inference and static/dynamic checking of such assertions, etc. Ciao also offers support for programming in the small producing small executables (including only those builtins used by the program) and support for writing scripts in Prolog. The Ciao programming environment includes a classical top-level and a rich emacs interface with an embeddable source-level debugger and a number of execution visualization tools. The Ciao compiler (which can be run outside the top level shell) generates several forms of architecture-independent and stand-alone executables, which run with speed, efficiency and executable size which are very competive with other commercial and academic Prolog/CLP systems. Library modules can be compiled into compact bytecode or C source files, and linked statically, dynamically, or autoloaded. The novel modular design of Ciao enables, in addition to modular program development, effective global program analysis and static debugging and optimization via source to source program transformation. These tasks are performed by the Ciao preprocessor ( ciaopp, distributed separately). The Ciao programming environment also includes lpdoc, an automatic documentation generator for LP/CLP programs. It processes Prolog files adorned with (Ciao) assertions and machine-readable comments and generates manuals in many formats including postscript, pdf, texinfo, info, HTML, man, etc. , as well as on-line help, ascii README files, entries for indices of manuals (info, WWW, ...), and maintains WWW distribution sites.

Information-Analytical System for Design of New Inorganic Compounds

The principles of design of information-analytical system (IAS) intended for design of new inorganic compounds are considered. IAS includes the integrated system of databases on properties of inorganic substances and materials, the system of the programs of pattern recognition, the knowledge base and managing program. IAS allows a prediction of inorganic compounds not yet synthesized and estimation of their some properties.

A semantic paradigm for intelligent data access

An implementation of Sem-ODB—a database management system based on the Semantic Binary Model is presented. A metaschema of Sem-ODB database as well as the top-level architecture of the database engine is defined. A new benchmarking technique is proposed which allows databases built on different database models to compete fairly. This technique is applied to show that Sem-ODB has excellent efficiency comparing to a relational database on a certain class of database applications. A new semantic benchmark is designed which allows evaluation of the performance of the features characteristic of semantic database applications. An application used in the benchmark represents a class of problems requiring databases with sparse data, complex inheritances and many-to-many relations. Such databases can be naturally accommodated by semantic model. A fixed predefined implementation is not enforced allowing the database designer to choose the most efficient structures available in the DBMS tested. The results of the benchmark are analyzed. ^ A new high-level querying model for semantic databases is defined. It is proven adequate to serve as an efficient native semantic database interface, and has several advantages over the existing interfaces. It is optimizable and parallelizable, supports the definition of semantic userviews and the interoperability of semantic databases with other data sources such as World Wide Web, relational, and object-oriented databases. The query is structured as a semantic database schema graph with interlinking conditionals. The query result is a mini-database, accessible in the same way as the original database. The paradigm supports and utilizes the rich semantics and inherent ergonomics of semantic databases. ^ The analysis and high-level design of a system that exploits the superiority of the Semantic Database Model to other data models in expressive power and ease of use to allow uniform access to heterogeneous data sources such as semantic databases, relational databases, web sites, ASCII files, and others via a common query interface is presented. The Sem-ODB engine is used to control all the data sources combined under a unified semantic schema. A particular application of the system to provide an ODBC interface to the WWW as a data source is discussed. ^

Data partitioning for parallel spatial join processing

The cost of spatial join processing can be very high because of the large sizes of spatial objects and the computation-intensive spatial operations. While parallel processing seems a natural solution to this problem, it is not clear how spatial data can be partitioned for this purpose. Various spatial data partitioning methods are examined in this paper. A framework combining the data-partitioning techniques used by most parallel join algorithms in relational databases and the filter-and-refine strategy for spatial operation processing is proposed for parallel spatial join processing. Object duplication caused by multi-assignment in spatial data partitioning can result in extra CPU cost as well as extra communication cost. We find that the key to overcome this problem is to preserve spatial locality in task decomposition. We show in this paper that a near-optimal speedup can be achieved for parallel spatial join processing using our new algorithms.

MHCWeb: Converting a WWW database into a knowledge-based collaborative environment

The World Wide Web (WWW) is useful for distributing scientific data. Most existing web data resources organize their information either in structured flat files or relational databases with basic retrieval capabilities. For databases with one or a few simple relations, these approaches are successful, but they can be cumbersome when there is a data model involving multiple relations between complex data. We believe that knowledge-based resources offer a solution in these cases. Knowledge bases have explicit declarations of the concepts in the domain, along with the relations between them. They are usually organized hierarchically, and provide a global data model with a controlled vocabulary, We have created the OWEB architecture for building online scientific data resources using knowledge bases. OWEB provides a shell for structuring data, providing secure and shared access, and creating computational modules for processing and displaying data. In this paper, we describe the translation of the online immunological database MHCPEP into an OWEB system called MHCWeb. This effort involved building a conceptual model for the data, creating a controlled terminology for the legal values for different types of data, and then translating the original data into the new structure. The 0 WEB environment allows for flexible access to the data by both users and computer programs.

An Approach To Publish a Data Warehouse Content as Linked Data

Mestrado em Engenharia Informática - Área de Especialização em Tecnologias do Conhecimento e Decisão

Armazéns de dados em bases de dados NoSQL

Na atualidade, existe uma quantidade de dados criados diariamente que ultrapassam em muito as mais otimistas espectativas estabelecidas na década anterior. Estes dados têm origens bastante diversas e apresentam-se sobre várias formas. Este novo conceito que dá pelo nome de Big Data está a colocar novos e rebuscados desafios ao seu armazenamento, tratamento e manipulação. Os tradicionais sistemas de armazenamento não se apresentam como a solução indicada para este problema. Estes desafios são alguns dos mais analisados e dissertados temas informáticos do momento. Várias tecnologias têm emergido com esta nova era, das quais se salienta um novo paradigma de armazenamento, o movimento NoSQL. Esta nova filosofia de armazenamento visa responder às necessidades de armazenamento e processamento destes volumosos e heterogéneos dados. Os armazéns de dados são um dos componentes mais importantes do âmbito Business Intelligence e são, maioritariamente, utilizados como uma ferramenta de apoio aos processos de tomada decisão, levados a cabo no dia-a-dia de uma organização. A sua componente histórica implica que grandes volumes de dados sejam armazenados, tratados e analisados tendo por base os seus repositórios. Algumas organizações começam a ter problemas para gerir e armazenar estes grandes volumes de informação. Esse facto deve-se, em grande parte, à estrutura de armazenamento que lhes serve de base. Os sistemas de gestão de bases de dados relacionais são, há algumas décadas, considerados como o método primordial de armazenamento de informação num armazém de dados. De facto, estes sistemas começam a não se mostrar capazes de armazenar e gerir os dados operacionais das organizações, sendo consequentemente cada vez menos recomendada a sua utilização em armazéns de dados. É intrinsecamente interessante o pensamento de que as bases de dados relacionais começam a perder a luta contra o volume de dados, numa altura em que um novo paradigma de armazenamento surge, exatamente com o intuito de dominar o grande volume inerente aos dados Big Data. Ainda é mais interessante o pensamento de que, possivelmente, estes novos sistemas NoSQL podem trazer vantagens para o mundo dos armazéns de dados. Assim, neste trabalho de mestrado, irá ser estudada a viabilidade e as implicações da adoção de bases de dados NoSQL, no contexto de armazéns de dados, em comparação com a abordagem tradicional, implementada sobre sistemas relacionais. Para alcançar esta tarefa, vários estudos foram operados tendo por base o sistema relacional SQL Server 2014 e os sistemas NoSQL, MongoDB e Cassandra. Várias etapas do processo de desenho e implementação de um armazém de dados foram comparadas entre os três sistemas, sendo que três armazéns de dados distintos foram criados tendo por base cada um dos sistemas. Toda a investigação realizada neste trabalho culmina no confronto da performance de consultas, realizadas nos três sistemas.