Distributed data service for data management in internet of things middleware

The development of the Internet of Things (IoT) is closely related to a considerable increase in the number and variety of devices connected to the Internet. Sensors have become a regular component of our environment, as well as smart phones and other devices that continuously collect data about our lives even without our intervention. With such connected devices, a broad range of applications has been developed and deployed, including those dealing with massive volumes of data. In this paper, we introduce a Distributed Data Service (DDS) to collect and process data for IoT environments. One central goal of this DDS is to enable multiple and distinct IoT middleware systems to share common data services from a loosely-coupled provider. In this context, we propose a new specification of functionalities for a DDS and the conception of the corresponding techniques for collecting, filtering and storing data conveniently and efficiently in this environment. Another contribution is a data aggregation component that is proposed to support efficient real-time data querying. To validate its data collecting and querying functionalities and performance, the proposed DDS is evaluated in two case studies regarding a simulated smart home system, the first case devoted to evaluating data collection and aggregation when the DDS is interacting with the UIoT middleware, and the second aimed at comparing the DDS data collection with this same functionality implemented within the Kaa middleware

Modelo de dados para um Pipeline de seqüenciamento de alto desempenho transcritômico

Dissertação (mestrado)—Universidade de Brasília, Instituto de CIências Exatas, Departamento de Ciência da Computação, 2012.O rápido avanço nas técnicas de sequenciamento de alto desempenho de fragmentos de DNA/RNA criou novos desa os computacionais na área de bioinformática. Um desses desa os é administrar o enorme volume de dados gerados pelos sequenciadores automáticos, particularmente o armazenamento e a análise desses dados processados em larga escala. A existência de diferentes formatos de representação, terminologia, estrutura de arquivos e semânticas, faz muito complexa a representação e administração desses dados. Neste contexto, um modelo de dados para representar, organizar e garantir o acesso aos dados biológicos é essencial para suportar o trabalho dos pesquisadores do campo da biologia, quando fazendo uso de pipelines de sequenciamento de alto desempenho. Este trabalho propõe tanto um modelo de dados conceitual, como também seu respectivo esquema relacional, permitindo a representação e o gerenciamento de um pipeline de sequenciamento de alto desempenho para projetos transcritômicos no intuito de organizar e armazenar de maneira simples e e ciente os dados gerados em cada fase da análise do pipeline. Nesta dissertação, trabalhamos com pipelines de sequenciamento de alto desempenho com três fases: ltragem, mapeamento e análise. Para validar nosso modelo, apresentamos dois estudos de casos para identi car a expressão diferencial de genes usando dados de sequenciamento de alto desempenho transcritômico. Estes estudos de caso mostraram que introduzir o modelo de dados, e o esquema correspondente, tornou o pipeline mais e ciente, organizado, para dar suporte ao trabalho dos biólogos envolvidos em um projeto de transcritoma. _________________________________________________________________________________________ ABSTRACTThe rapid advances in high-throughput sequencing techniques of DNA/RNA fragments created new computational challenges in bioinformatics. One of these challenges is to manage the enormous volume of data generated by automatic sequencers, specially storage and analysis of these data processed on large scale. The existence of representation format, terminology, _le structure and semantics, becomes very complex representation and management of such data. In this context, a data model to represent, organize and provide access to biological data is essential to support the researchers works into biology_eld when using high-throughput sequencing. This work proposes a conceptual model as well as its database schema to representand manage a high-throughput transcriptome pipeline in order to organize and store in a simple and efficient way data generated in each pipeline phase. In this dissertation, we work with three phases high-throughput sequencing pipeline: _ltering, mapping and analysis. In order to validate our model, we present two case studies both having the objective of identifying deferentially expressed genes using high-throughput sequencing transcriptome data. These case studies showed that uses a data model, and its database schema, became the pipeline more efficient, organized, and support the biologists works involved in a transcriptome project