A Semantics-Based Approach to Design of Query Languages for Partial Information

Most of work on partial information in databases asks which operations of standard languages, like relational algebra, can still be performed correctly in the presence of nulls. In this paper a different point of view is advocated. We believe that the semantics of partiality must be clearly understood and it should give us new design principles for languages for databases with partial information. There are different sources of partial information, such as missing information and conflicts that occur when different databases are merged. In this paper, we develop a common semantic framework for them which can be applied in a context more general than the flat relational model. This ordered semantics, which is based on ideas used in the semantics of programming languages, cleanly intergrates all kinds of partial information and serves as a tool to establish connections between them. Analyzing properties of semantic domains of types suitable for representing partial information, we come up with operations that are naturally associated with those types, and we organize programming syntax around these operations. We show how the languages that we obtain can be used to ask typical queries about incomplete information in relational databases, and how they can express some previously proposed languages. Finally, we discuss a few related topics such as mixing traditional constraints with partial information and extending semantics and languages to accommodate bags and recursive types

Visões em bancos de dados de grafos : uma abordagem multifoco para dados heterogêneos

Orientador: Claudia Maria Bauzer MedeirosTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: A pesquisa científica tornou-se cada vez mais dependente de dados. Esse novo paradigma de pesquisa demanda técnicas e tecnologias computacionais sofisticadas para apoiar tanto o ciclo de vida dos dados científicos como a colaboração entre cientistas de diferentes áreas. Uma demanda recorrente em equipes multidisciplinares é a construção de múltiplas perspectivas sobre um mesmo conjunto de dados. Soluções atuais cobrem vários aspectos, desde o projeto de padrões de interoperabilidade ao uso de sistemas de gerenciamento de bancos de dados não-relacionais. Entretanto, nenhum desses esforços atende de forma adequada a necessidade de múltiplas perspectivas, denominadas focos nesta tese. Em termos gerais, um foco é projetado e construído para atender um determinado grupo de pesquisa (mesmo no escopo de um único projeto) que necessita manipular um subconjunto de dados de interesse em múltiplos níveis de agregação/generalização. A definição e criação de um foco são tarefas complexas que demandam mecanismos capazes de manipular múltiplas representações de um mesmo fenômeno do mundo real. O objetivo desta tese é prover múltiplos focos sobre dados heterogêneos. Para atingir esse objetivo, esta pesquisa se concentrou em quatro principais problemas. Os problemas inicialmente abordados foram: (1) escolher um paradigma de gerenciamento de dados adequado e (2) elencar os principais requisitos de pesquisas multifoco. Nossos resultados nos direcionaram para a adoção de bancos de dados de grafos como solução para o problema (1) e a utilização do conceito de visões, de bancos de dados relacionais, para o problema (2). Entretanto, não há consenso sobre um modelo de dados para bancos de dados de grafos e o conceito de visões é pouco explorado nesse contexto. Com isso, os demais problemas tratados por esta pesquisa são: (3) a especificação de um modelo de dados de grafos e (4) a definição de um framework para manipular visões em bancos de dados de grafos. Nossa pesquisa nesses quatro problemas resultaram nas contribuições principais desta tese: (i) apontar o uso de bancos de dados de grafos como camada de persistência em pesquisas multifoco - um tipo de banco de dados de esquema flexível e orientado a relacionamentos que provê uma ampla compreensão sobre as relações entre os dados; (ii) definir visões para bancos de dados de grafos como mecanismo para manipular múltiplos focos, considerando operações de manipulação de dados em grafos, travessias e algoritmos de grafos; (iii) propor um modelo de dados para grafos - baseado em grafos de propriedade - para lidar com a ausência de um modelo de dados pleno para grafos; (iv) especificar e implementar um framework, denominado Graph-Kaleidoscope, para prover o uso de visões em bancos de dados de grafos e (v) validar nosso framework com dados reais em aplicações distintas - em biodiversidade e em recursos naturais - dois típicos exemplos de pesquisas multidisciplinares que envolvem a análise de interações de fenômenos a partir de dados heterogêneosAbstract: Scientific research has become data-intensive and data-dependent. This new research paradigm requires sophisticated computer science techniques and technologies to support the life cycle of scientific data and collaboration among scientists from distinct areas. A major requirement is that researchers working in data-intensive interdisciplinary teams demand construction of multiple perspectives of the world, built over the same datasets. Present solutions cover a wide range of aspects, from the design of interoperability standards to the use of non-relational database management systems. None of these efforts, however, adequately meet the needs of multiple perspectives, which are called foci in the thesis. Basically, a focus is designed/built to cater to a research group (even within a single project) that needs to deal with a subset of data of interest, under multiple ggregation/generalization levels. The definition and creation of a focus are complex tasks that require mechanisms and engines to manipulate multiple representations of the same real world phenomenon. This PhD research aims to provide multiple foci over heterogeneous data. To meet this challenge, we deal with four research problems. The first two were (1) choosing an appropriate data management paradigm; and (2) eliciting multifocus requirements. Our work towards solving these problems made as choose graph databases to answer (1) and the concept of views in relational databases for (2). However, there is no consensual data model for graph databases and views are seldom discussed in this context. Thus, research problems (3) and (4) are: (3) specifying an adequate graph data model and (4) defining a framework to handle views on graph databases. Our research in these problems results in the main contributions of this thesis: (i) to present the case for the use of graph databases in multifocus research as persistence layer - a schemaless and relationship driven type of database that provides a full understanding of data connections; (ii) to define views for graph databases to support the need for multiple foci, considering graph data manipulation, graph algorithms and traversal tasks; (iii) to propose a property graph data model (PGDM) to fill the gap of absence of a full-fledged data model for graphs; (iv) to specify and implement a framework, named Graph-Kaleidoscope, that supports views over graph databases and (v) to validate our framework for real world applications in two domains - biodiversity and environmental resources - typical examples of multidisciplinary research that involve the analysis of interactions of phenomena using heterogeneous dataDoutoradoCiência da ComputaçãoDoutora em Ciência da Computaçã

Iphone Book

The iPhone is a line of Internet- and multimedia-enabled smartphones designed and marketed by Apple Inc. The first iPhone was unveiled by Apple CEO Steve Jobs on January 9, 2007, and released on June 29, 2007. An iPhone can function as a video camera (video recording was not a standard feature until the iPhone 3GS was released), a camera phone, can send texts and receive visual voicemail, a portable media player, and an Internet client with email and web browsing capabilities, and both Wi-Fi and 3G connectivity. The user interface is built around the device's multi-touch screen, including a virtual keyboard rather than a physical one. Third-party as well as Apple application software is available from the App Store, which launched in mid-2008 and now has over 350,000 "apps" approved by Apple. These apps have diverse functionalities, including games, reference, GPS navigation, social networking, e-booksEscuela Técnica Superior de Ingeniería de TelecomunicaciónUniversidad Politécnica de Cartagen

Iphone Bookshelf

The iPhone is a line of Internet- and multimedia-enabled smartphones designed and marketed by Apple Inc. The first iPhone was unveiled by Apple CEO Steve Jobs on January 9, 2007, and released on June 29, 2007. An iPhone can function as a video camera (video recording was not a standard feature until the iPhone 3GS was released), a camera phone, can send texts and receive visual voicemail, a portable media player, and an Internet client with email and web browsing capabilities, and both Wi-Fi and 3G connectivity. The user interface is built around the device's multi-touch screen, including a virtual keyboard rather than a physical one. Third-party as well as Apple application software is available from the App Store, which launched in mid-2008 and now has over 350,000 "apps" approved by Apple. These apps have diverse functionalities, including games, reference, GPS navigation, social networking, e-books... To create applications for this device it’s use the APPLE SDK.Escuela Técnica Superior de Ingeniería de TelecomunicaciónUniversidad Politécnica de Cartagen

A contribution for data processing and interoperability in Industry 4.0

Dissertação de mestrado em Engenharia de SistemasIndustry 4.0 is expected to drive a significant change in companies’ growth. The idea is to cluster important information from all the company’s supply chain, enabling valuable decision-making while permitting interactions between machines and humans in real time. Autonomous systems powered with Information Technologies are enablers of Industry 4.0 – like Internet of Things (IoT), Cyber Physical-Systems (CPS) and Big Data and analytics. IoT gather information from every piece of the big puzzle which is the manufacturing process. Cloud Computing store all that information in one place. People share information across the company, between its supply chain and hierarchical levels through integration of systems. Finally, Big Data and analytics are of intelligence that will improve Industry 4.0. Methods and tools in Industry 4.0 are designed to increase interoperability across industrial stakeholders. In order to make the complete process possible, standardisation must be implemented across the company. Two reference models for Industry 4.0 were studied - RAMI 4.0 and IIRA. RAMI 4.0, a German initiative, focuses on industrial digitalization while IIRA, an American initiative, focuses on “Internet of Things” world, i.e. energy, healthcare and transportation. The two initiatives aim to obtain intelligence data from processes while enabling interoperability among systems. Representatives from the two reference models are working together on the technological interface standards that could be used by companies joining this new era. This study aims at the interoperability between systems. Even though there must be a model to guide the company into Industry 4.0, this model ought to be mutable and flexible enough to handle differences in manufacturing process, as an example automotive industry 4.0 will not have the same approach as aviation Industry 4.0.Espera-se que a Indústria 4.0 seja uma mudança significativa no crescimento das empresas. O objetivo é agrupar informações importantes de toda a cadeia de suprimentos da empresa, proporcionando uma tomada de decisão mais acertada, ao mesmo tempo que permite interações entre seres humanos e máquinas em tempo real. Sistemas autônomos equipados com Tecnologias da Informação possibilitam a Indústria 4.0 como a Internet das Coisas (IoT), sistemas ciber-físicos (CPS) e Big Data e analytics. A IoT coleta informações de cada peça do grande quebra-cabeça que é o processo de fabricação. Cloud Computing lida com armazenamento de toda essa informação em um só lugar. As pessoas compartilham informações em toda a empresa, na cadeia de abastecimento e níveis hierárquicos por meio da integração de sistemas. Por fim, Big Data e analytics são de inteligência que melhorarão a Indústria 4.0. Os métodos e ferramentas da Indústria 4.0 são projetadas para aumentar a interoperabilidade entre os stakeholders. Para tornar possível essa interoperabilidade, um padrão em toda a empresa deve ser implementado. Dois modelos de referência para a Indústria 4.0 foram estudados - RAMI 4.0 e IIRA. RAMI 4.0, a iniciativa alemã, concentra-se na digitalização industrial, enquanto IIRA, a iniciativa americana, foca no mundo da Internet das Coisas, como energia, saúde e transporte. As duas iniciativas visam obter dados inteligentes dos processos e, ao mesmo tempo, permitir a interoperabilidade entre os sistemas. Representantes dos dois modelos de referência estão a trabalhar juntos para discutir os padrões de interface tecnológica que podem ser usados pelas empresas que entram nessa nova era. Este estudo visa a interoperabilidade entre sistemas. Embora deva haver um modelo para orientar a empresa na Indústria 4.0, esse modelo deve ser mutável e flexível o suficiente para lidar com diferenças no processo de fabricação, como exemplo a indústria 4.0 automotiva não terá a mesma abordagem que a Indústria 4.0 de aviação

Attribute-Level Versioning: A Relational Mechanism for Version Storage and Retrieval

Data analysts today have at their disposal a seemingly endless supply of data and repositories hence, datasets from which to draw. New datasets become available daily thus making the choice of which dataset to use difficult. Furthermore, traditional data analysis has been conducted using structured data repositories such as relational database management systems (RDBMS). These systems, by their nature and design, prohibit duplication for indexed collections forcing analysts to choose one value for each of the available attributes for an item in the collection. Often analysts discover two or more datasets with information about the same entity. When combining this data and transforming it into a form that is usable in an RDBMS, analysts are forced to deconflict the collisions and choose a single value for each duplicated attribute containing differing values. This deconfliction is the source of a considerable amount of guesswork and speculation on the part of the analyst in the absence of professional intuition. One must consider what is lost by discarding those alternative values. Are there relationships between the conflicting datasets that have meaning? Is each dataset presenting a different and valid view of the entity or are the alternate values erroneous? If so, which values are erroneous? Is there a historical significance of the variances? The analysis of modern datasets requires the use of specialized algorithms and storage and retrieval mechanisms to identify, deconflict, and assimilate variances of attributes for each entity encountered. These variances, or versions of attribute values, contribute meaning to the evolution and analysis of the entity and its relationship to other entities. A new, distinct storage and retrieval mechanism will enable analysts to efficiently store, analyze, and retrieve the attribute versions without unnecessary complexity or additional alterations of the original or derived dataset schemas. This paper presents technologies and innovations that assist data analysts in discovering meaning within their data and preserving all of the original data for every entity in the RDBMS