improving query performance using distributed computing

Data warehouses are used to store large amounts of data. This data is often used for On-Line Analytical Processing (OLAP) where short response times are essential for on-line decision support. One of the most important requirements of a data warehouse server is the query performance. The principal aspect from the user perspective is how quickly the server processes a given query: “the data warehouse must be fast”. The main focus of our research is finding adequate solutions to improve query response time of typical OLAP queries and improve scalability using a distributed computation environment that takes advantage of characteristics specific to the OLAP context. Our proposal provides very good performance and scalability even on huge data warehouses

Life Cycle Assessment of PEEK for Additive Manufacturing

The lifecycle assessment process (LCA) is a method that allows one to determine the environ- mental impact and gives the information needed to improve the environmental performance of processes which allows for reducing the human footprint in the environment. This dissertation consists of a lifecycle assessment of the production of polyetheretherketone (PEEK) in both the additive manufacturing and injection molding processes with the aim of selecting the most environmentally friendly process. This assessment is made considering a cradle-to-grave approach from when the natural resources are extracted until the final piece is discarded. This analysis is supported using LCA databases and real experimental data collected at NOVA School of Science and Technology. This data will allow for a more conscious choice in selecting one manufacturing process over the other. OpenLCA was the selected software to perform the life cycle assessment with Delft's University of Technologies' IDEMAT database. It was determined that the additive manufacturing production of PEEK is the manufacturing method that contains the least environmental impact mainly because of the reduction of travel distances that are associated with more local production. This compensates for the slight in- crease in energy consumption of additive manufacturing compared to the injection molding process.O processo de avaliação do ciclo de vida (ACV) é um método que permite determinar o im- pacto ambiental e dá a informação necessária para melhorar o desempenho ambiental dos processos o que permite reduzir a pegada humana no ambiente. Esta dissertação consiste numa avaliação do ciclo de vida da produção da polieteretercetona (PEEK) tanto nos processos de fabrico aditivo como na moldagem por injeção, com o objetivo de selecionar o processo com o menor impacte ambiental. Esta avaliação é efetuada tendo em conta uma abordagem cradle-to-grave que compreende os processos desde a extração dos recursos naturais até que a peça final seja descartada. Esta análise é suportada através de bases de dados de ACV e dados experimentais reais reco- lhidos na NOVA School of Science and Technology. Estes dados permitirão uma escolha mais consciente na seleção de um processo de fabrico em detrimento do outro. O OpenLCA foi o software selecionado para realizar a avaliação do ciclo de vida com o suporte da base de dados IDEMAT produzida pela Universidade de Tecnologias de Delft. Considerou-se que a produção em fabrico aditivo do PEEK é o método de fabrico que contém o menor impacte ambiental, principalmente devido à redução das distâncias de viagem que estão associadas a uma produção local. Este fator compensa o ligeiro aumento do consumo de energia do fabrico aditivo em comparação com o processo de moldagem por injeção

OSSPal Assessment of Self-Service BI and Analytics Software

Business Intelligence (BI) and Data Analytics are among the top Data Science topics nowadays. They are available as Self-Service solutions of valuable utility when business professionals need to perform data visualization and/or analytics. In addition to that, a great opportunity for companies to start exploring their data with minimal or no assistance from IT technicians. In other words, a shortcut to business opportunities. In this paper, through the OSSPal methodology, we assess the free versions of three popular Self-Service BI and Analytics tools: Power BI, QlikView, and Tableau Public. In conclusion, we could see that Power BI offers more features at no cost, being so highly recommended for Small and Medium-Sized Enterprises (SMEs). On the other hand, QlikView and Tableau Public were considered almost as powerful as Power BI and might also naturally be a more suitable choice according to the requirements of a company

Big Data in the Cloud: A Survey

Big Data has become a hot topic across several business areas requiring the storage and processing of huge volumes of data. Cloud computing leverages Big Data by providing high storage and processing capabilities and enables corporations to consume resources in a pay-as-you-go model making clouds the optimal environment for storing and processing huge quantities of data. By using virtualized resources, Cloud can scale very easily, be highly available and provide massive storage capacity and processing power. This paper surveys existing databases models to store and process Big Data within a Cloud environment. Particularly, we detail the following traditional NoSQL databases: BigTable, Cassandra, DynamoDB, HBase, Hypertable, and MongoDB. The MapReduce framework and its developments Apache Spark, HaLoop, Twister, and other alternatives such as Apache Giraph, GraphLab, Pregel and MapD - a novel platform that uses GPU processing to accelerate Big Data processing - are also analyzed. Finally, we present two case studies that demonstrate the successful use of Big Data within Cloud environments and the challenges that must be addressed in the future

Which NoSQL Database? A Performance Overview

NoSQL data stores are widely used to store and retrieve possibly large amounts of data, typically in a key-value format. There are many NoSQL types with different performances, and thus it is important to compare them in terms of performance and verify how the performance is related to the database type. In this paper, we evaluate five most popular NoSQL databases: Cassandra, HBase, MongoDB, OrientDB and Redis. We compare those databases in terms of query performance, based on reads and updates, taking into consideration the typical workloads, as represented by the Yahoo! Cloud Serving Benchmark. This comparison allows users to choose the most appropriate database according to the specific mechanisms and application needs