Marcio Victorino

An evaluation of data replication for bioinformatics workflows on NoSQL systems

Many research projects in bioinformatics may be viewed as scientific workflows. Biologists often run multiple times the same workflow with different parameters in order to refine their data analysis. These executions generate a large volume of files with different formats, which need to be stored for future evaluations. New database models, like NoSQL systems, could be considered to deal with large volumes of data, particularly in distributed systems. This work presents a data replication impact assessment from the execution of scientific workflows for two NoSQL database management systems: Cassandra and MongoDB.

An Evaluation of Data Model for NoSQL Document-Based Databases

NoSQL databases offer flexibility in the data model. The document-based databases may have some data models built with embedded documents, and others made with referenced documents. The challenge lies in choosing the structure of the data. This paper proposes a study to analyze if different data models can have an impact on the performance of database queries. To this end, we created three data models: embedded, referenced, and hybrid. We ran experiments on each data model in a MongoDB cluster, comparing the response time of 3 different queries in each model. Results showed a disparity in performance between the data models. We also evaluated the use of indexes in each data model. Results showed that, depending on the type of query and field searched some types of indexes presented higher performance compared to others. Additionally, we carried out an analysis of the space occupied on the storage disk. This analysis shows that the choice of model also affects disk space for storing data and indexes.

Proposal of a Brazilian Database Government Open Linked Data: DBgoldbr: Invited Paper

The Brazilian Government has made available on the Web a massive volume of public data. This data may be structured, semi-structured or non-structured in order to turn the administration as transparent as possible. Thus, we notice the great challenge in providing applications capable enough to handle this Big Data environment, and to make information available for decision making. In this environment, data processing is done via new approaches from Information Science and Computer Science areas, by involving Technologies and processes for collecting, representing, storing and disseminating information. This paper presents a conceptual model, the technical architecture and the prototype implementation of a tool DBgoldbr, designed to classify government public information with the help of ontologies, by transforming open data into open linked data. To fulfill the purpose of the solution, we used Soft System Methodology to identify problems, to collect users needs and to design solutions that fit the purpose of specific groups. The DBgoldbr tool was designed to ease up the search for open data made available by many Brazilian Government institutions, so that this data can be reused to support the evaluation and monitoring of social programs, in order to support the design and management of public policies.

Geographic data in a graph oriented database: A study with Neo4j and PostgreSQL

Graph databases, similar to other NoSQL approaches, have emerged as an alternative to solve specific problems ill fitted to relational models. The huge amounts of data produced, and the high capability for horizontal scalability provided by NoSQL databases has led to the increasing adoption of these systems, which can now be adapted to support geographic data. This article presents an analysis on the behavior of a graph oriented geographic databases compared to those designed with a relational model, using the database management systems PostgreSQL/PostGIS and Neo4j.