Transferência de Aprendizado para Redes Bayesianas com Aplicação em Predição de Falha de Discos Rígidos

Predizer falhas em Discos Rígidos é muito importante para evitar perda de dados e custos adicionais. Logo, um esforço pode ser observado para encontrar métodos adequados de predição de falhas. Apesar dos resultados encorajantes alcançados por vários métodos, um aspecto notado é a falta de dados disponíveis para construir modelos confiáveis. Transferência de Aprendizado oferece uma alternativa válida, uma vez que pode ser usada para transferir conhecimento de modelos de Disco com muitos dados para Discos com menos dados. Neste trabalho, avaliamos estratégias de Transferência de Aprendizado para esta tarefa. Além disso propomos uma estratégia para construir fontes de informação baseadas no agrupamento de modelos de disco parecidos. Resultados mostraram que todos os cenários testados de transferência melhoram a performance dos métodos de predição, principalmente para Discos com muito poucos dados

DORS: Database Query Optimizer with Rule Based Search Engine

The database query optimizer is a very important and complex module in database management systems. It receives a query optimization request with a query tree as a parameter and return an optimized execution plan. The query optimization problem is NP-Hard; therefore, there are many proposals of heuristics and techniques for optimization strategies. There are also several data models (e.g objectoriented, relational, object-relational and semi-structured/XML) suitable to store information for different kinds of applications. Several optimization frameworks were proposed with the aim of making easier to build optimizers and reuse design decisions. However, they are tied to some specific language and hard to integrate with other database modules. We propose a design pattern to help the design and construction of a database optimizer. So far, we do not have knowledge about similar work. Context Different types of applications should use a suitable data model. For instance, commercial systems work well with relational data models, CAD/CAM systems need a more expressive data model as the objectoriented, and the Internet with XML [W3C] applications work well with semi-structured data models. Different data models and different kinds of applications require specific implementation of the quer

Applying Rules for Partitioned Parallelism in OODBMS within an Optimizer Generator Framework

This work presents a rule-based approach for declarative query optimizer generation considering parallel execution in object-oriented databases. The main goal of this work is to provide a framework that can capture relevant aspects of parallel query optimization in a declarative way, combining procedural techniques with the advantages of rule processing. One of those techniques was used for determining repartitioning and selecting the algorithms to evaluate operator in a query tree considering the trade-offs between processing costs and repartitioning costs. This technique was proposed as a procedural algorithm and it was adapted to be used as rules in the context of object-oriented database optimizers. Another algorithm, used for query operator reordering in object-oriented databases, was adapted in order to consider repartitioning cost and was added to the framework. Finally, a new module for processing rules for parallelism extraction was added to the framework, providing a better support for inter-operator parallelism optimization techniques.

R.M.C.: FramePersist: An object persistence framework for mobile device applications

Traditional requirements for persistence layers do not consider limitations of the development platforms available for mobile devices. In order to facilitate the development of applications, which need to store data, for mobile devices, these traditional requirements are reevaluated in this paper and a framework for object persistence is proposed. This framework, called FramePersist, is constructed based on the concept of object serialization and allows to persist and to search objects efficiently. Mapping rules of objects for persistence are presented together with a case study using the Symbian OS native file systems. 1

MAPSkew: Metaheuristic Approaches for Partitioning Skew in MapReduce

MapReduce is a parallel computing model in which a large dataset is split into smaller parts and executed on multiple machines. Due to its simplicity, MapReduce has been widely used in various applications domains. MapReduce can significantly reduce the processing time of a large amount of data by dividing the dataset into smaller parts and processing them in parallel in multiple machines. However, when data are not uniformly distributed, we have the so called partitioning skew, where the allocation of tasks to machines becomes unbalanced, either by the distribution function splitting the dataset unevenly or because a part of the data is more complex and requires greater computational effort. To solve this problem, we propose an approach based on metaheuristics. For evaluating purposes, three metaheuristics were implemented: Simulated Annealing, Local Beam Search and Stochastic Beam Search. Our experimental evaluation, using a MapReduce implementation of the Bron-Kerbosch Clique Algorithm, shows that the proposed method can find good partitionings while better balancing data among machines

Transferência de Aprendizado para Redes Bayesianas com Aplicação em Predição de Falha de Discos Rígidos

Predizer falhas em Discos Rígidos é muito importante para evitar perda de dados e custos adicionais. Logo, um esforço pode ser observado para encontrar métodos adequados de predição de falhas. Apesar dos resultados encorajantes alcançados por vários métodos, um aspecto notado é a falta de dados disponíveis para construir modelos confiáveis. Transferência de Aprendizado oferece uma alternativa válida, uma vez que pode ser usada para transferir conhecimento de modelos de Disco com muitos dados para Discos com menos dados. Neste trabalho, avaliamos estratégias de Transferência de Aprendizado para esta tarefa. Além disso propomos uma estratégia para construir fontes de informação baseadas no agrupamento de modelos de disco parecidos. Resultados mostraram que todos os cenários testados de transferência melhoram a performance dos métodos de predição, principalmente para Discos com muito poucos dados