Uma proposta de mapeamento do modelo XML Schema para o modelo relacional

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Ciência da Computação.O uso da XML como padrão para intercâmbio de dados gera a necessidade de um esquema comum a ser seguido pelos sistemas envolvidos. Os mecanismos mais usados para a definição de esquemas XML são a DTD e a XML Schema. Com estas tecnologias, é possível definir a estrutura a ser seguida pelos documentos XML a serem intercambiados, estabelecendo um protocolo de troca de informações independente do mecanismo de armazenamento de dados usado pelos sistemas. Neste contexto, é necessário que os sistemas comunicantes sejam capazes de transformar seu modelo de dados em XML para o modelo de dados utilizado pelo sistema e vice-versa. O modelo de dados relacional é utilizado por muitos destes sistemas, considerando a grande disponibilidade de Sistemas Gerenciadores de Bancos de Dados (SGBDs) que adotam este modelo. Para isso, estes sistemas devem desenvolver mecanismos para exportar os dados de suas tabelas no formato XML e também para decompor documentos XML e armazená-los no SGBD. Estes mecanismos devem ser genéricos, dinâmicos e eficientes para garantir uma atividade adequada de intercâmbio de dados. Assim sendo, este trabalho propõe um mecanismo baseado em regras para transformar um esquema de dados XML, definido com o uso de XML Schema, para um esquema de dados relacional, que pode ser usado por SGBDs relacionais existentes no mercado. Como contribuição específica, este trabalho realiza uma análise detalhada dos conceitos do modelo XML Schema, considerando estes conceitos nas regras para transformação do XML Schema em um esquema de dados relacional

A Framework for Industry 4.0

The potential of the Industry 4.0 will allow the national industry to develop all kinds of procedures, especially in terms of competitive differentiation. The prospects and motivations behind Industry 4.0 are related to the management that is essentially geared towards industrial internet, to the integrated analysis and use of data, to the digitalization of products and services, to new disruptive business models and to the cooperation within the value chain. It is through the integration of Cyber-Physical Systems (CPS), into the maintenance process that it is possible to carry out a continuous monitoring of industrial machines, as well as to apply advanced techniques for predictive and proactive maintenance. The present work is based on the MANTIS project, aiming to construct a specific platform for the proactive maintenance of industrial machines, targeting particularly the case of GreenBender ADIRA Steel Sheet. In other words, the aim is to reduce maintenance costs, increase the efficiency of the process and consequently the profit. Essentially, the MANTIS project is a multinational research project, where the CISTER Research Unit plays a key role, particularly in providing the communications infrastructure for one MANTIS Pilot. The methodology is based on a follow-up study, which is jointly carried with the client, as well as within the scope of the implementation of the ADIRA Pilot. The macro phases that are followed in the present work are: 1) detailed analysis of the business needs; 2) preparation of the architecture specification; 3) implementation/development; 4) tests and validation; 5) support; 6) stabilization; 7) corrective and evolutionary maintenance; and 8) final project analysis and corrective measures to be applied in future projects. The expected results of the development of such project are related to the integration of the industrial maintenance process, to the continuous monitoring of the machines and to the application of advanced techniques of preventive and proactive maintenance of industrial machines, particularly based on techniques and good practices of the Software Engineering area and on the integration of Cyber-Physical Systems.O potencial desenvolvido pela Indústria 4.0 dotará a indústria nacional de capacidades para desenvolver todo o tipo de procedimentos, especialmente a nível da diferenciação competitiva. As perspetivas e as motivações por detrás da Indústria 4.0 estão relacionadas com uma gestão essencialmente direcionada para a internet industrial, com uma análise integrada e utilização de dados, com a digitalização de produtos e de serviços, com novos modelos disruptivos de negócio e com uma cooperação horizontal no âmbito da cadeia de valor. É através da integração dos sistemas ciber-físicos no processo de manutenção que é possível proceder a um monitoramento contínuo das máquinas, tal como à aplicação de técnicas avançadas para a manutenção preditiva e pró-ativa das mesmas. O presente trabalho é baseado no projeto MANTIS, objetivando, portanto, a construção de uma plataforma específica para a manutenção pró-ativa das máquinas industriais, neste caso em concreto das prensas, que serão as máquinas industriais analisadas ao longo do presente trabalho. Dito de um outro modo, objetiva-se, através de uma plataforma em específico, reduzir todos os custos da sua manutenção, aumentando, portanto, os lucros industriais advindos da produção. Resumidamente, o projeto MANTIS consiste num projeto de investigação multinacional, onde a Unidade de Investigação CISTER desenvolve um papel fundamental, particularmente no fornecimento da infraestrutura de comunicação no Piloto MANTIS. A metodologia adotada é baseada num estudo de acompanhamento, realizado em conjunto com o cliente, e no âmbito da implementação do Piloto da ADIRA. As macro fases que são compreendidas por esta metodologia, e as quais serão seguidas, são: 1) análise detalhada das necessidades de negócio; 2) preparação da especificação da arquitetura; 3) implementação/desenvolvimento; 4) testes e validação; 5) suporte; 6) estabilização; 7) manutenção corretiva e evolutiva; e 8) análise final do projeto e medidas corretivas a aplicar em projetos futuros. Os resultados esperados com o desenvolvimento do projeto estão relacionados com a integração do processo de manutenção industrial, a monitorização contínua das máquinas e a aplicação de técnicas avançadas de manutenção preventiva e pós-ativa das máquinas, especialmente com base em técnicas e boas práticas da área de Engenharia de Software

Migrating relational databases into object-based and XML databases

Rapid changes in information technology, the emergence of object-based and WWW applications, and the interest of organisations in securing benefits from new technologies have made information systems re-engineering in general and database migration in particular an active research area. In order to improve the functionality and performance of existing systems, the re-engineering process requires identifying and understanding all of the components of such systems. An underlying database is one of the most important component of information systems. A considerable body of data is stored in relational databases (RDBs), yet they have limitations to support complex structures and user-defined data types provided by relatively recent databases such as object-based and XML databases. Instead of throwing away the large amount of data stored in RDBs, it is more appropriate to enrich and convert such data to be used by new systems. Most researchers into the migration of RDBs into object-based/XML databases have concentrated on schema translation, accessing and publishing RDB data using newer technology, while few have paid attention to the conversion of data, and the preservation of data semantics, e.g., inheritance and integrity constraints. In addition, existing work does not appear to provide a solution for more than one target database. Thus, research on the migration of RDBs is not fully developed. We propose a solution that offers automatic migration of an RDB as a source into the recent database technologies as targets based on available standards such as ODMG 3.0, SQL4 and XML Schema. A canonical data model (CDM) is proposed to bridge the semantic gap between an RDB and the target databases. The CDM preserves and enhances the metadata of existing RDBs to fit in with the essential characteristics of the target databases. The adoption of standards is essential for increased portability, flexibility and constraints preservation. This thesis contributes a solution for migrating RDBs into object-based and XML databases. The solution takes an existing RDB as input, enriches its metadata representation with the required explicit semantics, and constructs an enhanced relational schema representation (RSR). Based on the RSR, a CDM is generated which is enriched with the RDB's constraints and data semantics that may not have been explicitly expressed in the RDB metadata. The CDM so obtained facilitates both schema translation and data conversion. We design sets of rules for translating the CDM into each of the three target schemas, and provide algorithms for converting RDB data into the target formats based on the CDM. A prototype of the solution has been implemented, which generates the three target databases. Experimental study has been conducted to evaluate the prototype. The experimental results show that the target schemas resulting from the prototype and those generated by existing manual mapping techniques were comparable. We have also shown that the source and target databases were equivalent, and demonstrated that the solution, conceptually and practically, is feasible, efficient and correct

On Pattern Mining in Graph Data to Support Decision-Making

In recent years graph data models became increasingly important in both research and industry. Their core is a generic data structure of things (vertices) and connections among those things (edges). Rich graph models such as the property graph model promise an extraordinary analytical power because relationships can be evaluated without knowledge about a domain-specific database schema. This dissertation studies the usage of graph models for data integration and data mining of business data. Although a typical company's business data implicitly describes a graph it is usually stored in multiple relational databases. Therefore, we propose the first semi-automated approach to transform data from multiple relational databases into a single graph whose vertices represent domain objects and whose edges represent their mutual relationships. This transformation is the base of our conceptual framework BIIIG (Business Intelligence with Integrated Instance Graphs). We further proposed a graph-based approach to data integration. The process is executed after the transformation. In established data mining approaches interrelated input data is mostly represented by tuples of measure values and dimension values. In the context of graphs these values must be attached to the graph structure and aggregated measure values are graph attributes. Since the latter was not supported by any existing model, we proposed the use of collections of property graphs. They act as data structure of the novel Extended Property Graph Model (EPGM). The model supports vertices and edges that may appear in different graphs as well as graph properties. Further on, we proposed some operators that benefit from this data structure, for example, graph-based aggregation of measure values. A primitive operation of graph pattern mining is frequent subgraph mining (FSM). However, existing algorithms provided no support for directed multigraphs. We extended the popular gSpan algorithm to overcome this limitation. Some patterns might not be frequent while their generalizations are. Generalized graph patterns can be mined by attaching vertices to taxonomies. We proposed a novel approach to Generalized Multidimensional Frequent Subgraph Mining (GM-FSM), in particular the first solution to generalized FSM that supports not only directed multigraphs but also multiple dimensional taxonomies. In scenarios that compare patterns of different categories, e.g., fraud or not, FSM is not sufficient since pattern frequencies may differ by category. Further on, determining all pattern frequencies without frequency pruning is not an option due to the computational complexity of FSM. Thus, we developed an FSM extension to extract patterns that are characteristic for a specific category according to a user-defined interestingness function called Characteristic Subgraph Mining (CSM). Parts of this work were done in the context of GRADOOP, a framework for distributed graph analytics. To make the primitive operation of frequent subgraph mining available to this framework, we developed Distributed In-Memory gSpan (DIMSpan), a frequent subgraph miner that is tailored to the characteristics of shared-nothing clusters and distributed dataflow systems. Finally, the results of use case evaluations in cooperation with a large scale enterprise will be presented. This includes a report of practical experiences gained in implementation and application of the proposed algorithms

Migrating relational databases into object-based and XML databases

Rapid changes in information technology, the emergence of object-based and WWW applications, and the interest of organisations in securing benefits from new technologies have made information systems re-engineering in general and database migration in particular an active research area. In order to improve the functionality and performance of existing systems, the re-engineering process requires identifying and understanding all of the components of such systems. An underlying database is one of the most important component of information systems. A considerable body of data is stored in relational databases (RDBs), yet they have limitations to support complex structures and user-defined data types provided by relatively recent databases such as object-based and XML databases. Instead of throwing away the large amount of data stored in RDBs, it is more appropriate to enrich and convert such data to be used by new systems. Most researchers into the migration of RDBs into object-based/XML databases have concentrated on schema translation, accessing and publishing RDB data using newer technology, while few have paid attention to the conversion of data, and the preservation of data semantics, e.g., inheritance and integrity constraints. In addition, existing work does not appear to provide a solution for more than one target database. Thus, research on the migration of RDBs is not fully developed. We propose a solution that offers automatic migration of an RDB as a source into the recent database technologies as targets based on available standards such as ODMG 3.0, SQL4 and XML Schema. A canonical data model (CDM) is proposed to bridge the semantic gap between an RDB and the target databases. The CDM preserves and enhances the metadata of existing RDBs to fit in with the essential characteristics of the target databases. The adoption of standards is essential for increased portability, flexibility and constraints preservation. This thesis contributes a solution for migrating RDBs into object-based and XML databases. The solution takes an existing RDB as input, enriches its metadata representation with the required explicit semantics, and constructs an enhanced relational schema representation (RSR). Based on the RSR, a CDM is generated which is enriched with the RDB's constraints and data semantics that may not have been explicitly expressed in the RDB metadata. The CDM so obtained facilitates both schema translation and data conversion. We design sets of rules for translating the CDM into each of the three target schemas, and provide algorithms for converting RDB data into the target formats based on the CDM. A prototype of the solution has been implemented, which generates the three target databases. Experimental study has been conducted to evaluate the prototype. The experimental results show that the target schemas resulting from the prototype and those generated by existing manual mapping techniques were comparable. We have also shown that the source and target databases were equivalent, and demonstrated that the solution, conceptually and practically, is feasible, efficient and correct.EThOS - Electronic Theses Online ServiceGBUnited Kingdo