Evaluating the Cassandra NoSQL Database Approach for Genomic Data Persistency

Aniceto, Rodrigo; Xavier, Rene; Guimarães, Valéria Monteze; Hondo, Fernanda; Holanda, Maristela; Walter, Maria Emília M. T.; Lifschitz, Sérgio

doi:10.1155/2015/502795

Cited by 18 publications

(10 citation statements)

References 19 publications

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“…We use Cassandra for two main reasons. First, Cassandra is one of the most used and best performing NoSQL databases today, with applications in several different domains ( Duarte & Bernardino, 2016 ; Daz, Martn & Rubio, 2016 ; Mahgoub et al, 2017a ; Le et al, 2014 ; Aniceto et al, 2015 ; Pinheiro et al, 2017 ). Second, the existing documentation is very complete, and it allows to easily replicate and generalize the experiments carried out in this work.…”

Section: Methodsmentioning

confidence: 99%

“…Our approach is completely general, and can be applied to different relational and NoSQL databases with little effort. In this work we choose to study the performance of irace on the Cassandra database, one of the most popular NoSQL databases, used in several real-world applications such as Internet of Things, genomics, or electric consumption data ( Cassandra, 2014 ; Duarte & Bernardino, 2016 ; Daz, Martn & Rubio, 2016 ; Mahgoub et al, 2017a ; Le et al, 2014 ; Aniceto et al, 2015 ; Pinheiro et al, 2017 ). We measure the performance in terms of throughput using the YCSB benchmark ( Cooper et al, 2010 ; Wang & Tang, 2012 ), observing a speedup of up to 30% over the default configuration.…”

Section: Introductionmentioning

confidence: 99%

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Automatic configuration of the Cassandra database using irace

Silva-Muñoz¹,

Franzin²,

Bersini³

2021

PeerJ Computer Science

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Database systems play a central role in modern data-centered applications. Their performance is thus a key factor in the efficiency of data processing pipelines. Modern database systems expose several parameters that users and database administrators can configure to tailor the database settings to the specific application considered. While this task has traditionally been performed manually, in the last years several methods have been proposed to automatically find the best parameter configuration for a database. Many of these methods, however, use statistical models that require high amounts of data and fail to represent all the factors that impact the performance of a database, or implement complex algorithmic solutions. In this work we study the potential of a simple model-free general-purpose configuration tool to automatically find the best parameter configuration of a database. We use the irace configurator to automatically find the best parameter configuration for the Cassandra NoSQL database using the YCBS benchmark under different scenarios. We establish a reliable experimental setup and obtain speedups of up to 30% over the default configuration in terms of throughput, and we provide an analysis of the configurations obtained.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic configuration of the Cassandra database using irace

Silva-Muñoz¹,

Franzin²,

Bersini³

2021

PeerJ Computer Science

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“…Ferreira et al 37 compared relational and NoSQL DBMS approaches by migrating source data from a PostgreSQL RDBMS to Cassandra, a column-based NoSQL system. Aniceto et al 38 extended this research by including MongoDB, a Document-Oriented NoSQL, comparing both Cassandra and MongoDB regarding PostgreSQL performance.…”

Section: Related Workmentioning

confidence: 99%

“…Two research studies were performed using the NoSQL Column-Oriented Cassandra database system for data provenance management using the PROV-Wf model. 37,38 The researchers analyzed the performance by running a Bioinformatics workflow. Ferreira et al 37 compared relational and NoSQL DBMS approaches by migrating source data from a PostgreSQL RDBMS to Cassandra, a column-based NoSQL system.…”

Section: Related Workmentioning

confidence: 99%

Bioinformatics Workflows With NoSQL Database in Cloud Computing

Wercelens

Silva

Hondo

et al. 2019

Evol Bioinform Online

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Scientific workflows can be understood as arrangements of managed activities executed by different processing entities. It is a regular Bioinformatics approach applying workflows to solve problems in Molecular Biology, notably those related to sequence analyses. Due to the nature of the raw data and the in silico environment of Molecular Biology experiments, apart from the research subject, 2 practical and closely related problems have been studied: reproducibility and computational environment. When aiming to enhance the reproducibility of Bioinformatics experiments, various aspects should be considered. The reproducibility requirements comprise the data provenance, which enables the acquisition of knowledge about the trajectory of data over a defined workflow, the settings of the programs, and the entire computational environment. Cloud computing is a booming alternative that can provide this computational environment, hiding technical details, and delivering a more affordable, accessible, and configurable on-demand environment for researchers. Considering this specific scenario, we proposed a solution to improve the reproducibility of Bioinformatics workflows in a cloud computing environment using both Infrastructure as a Service (IaaS) and Not only SQL (NoSQL) database systems. To meet the goal, we have built 3 typical Bioinformatics workflows and ran them on 1 private and 2 public clouds, using different types of NoSQL database systems to persist the provenance data according to the Provenance Data Model (PROV-DM). We present here the results and a guide for the deployment of a cloud environment for Bioinformatics exploring the characteristics of various NoSQL database systems to persist provenance data.

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“…Data are stored in the database as a hash Compression of such data can be done efficiently in column-oriented databases. Genomic data generated from sequencing experiments can be stored and analysed using these databases (Aniceto et al, 2015).…”

Section: Horizontal Scaling Techniquesmentioning

confidence: 99%

Emerging trend of big data analytics in bioinformatics: a literature review

Nagaraj¹,

Sharvani²,

Sridhar³

2018

IJBRA

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Advancement of unparalleled data in bioinformatics over the years is a major concern for storage and management. Such massive data must be handled efficiently to disseminate knowledge. Computational advancements in information technology present feasible analytical solutions to process such data. In this context, the paper is an attempt to highlight the influence of big data in bioinformatics. Some of the concepts emphasised are definition of big data; architectural platforms supporting data analytics; followed by the application of above-mentioned analytical techniques towards complex problems in bioinformatics. The challenges and future prospects of big data analytics in bioinformatics are briefly discussed. This paper provides a comprehensive summary of several data analytical techniques available for bioinformatics researchers and computer scientists.

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Evaluating the Cassandra NoSQL Database Approach for Genomic Data Persistency

Cited by 18 publications

References 19 publications

Automatic configuration of the Cassandra database using irace

Automatic configuration of the Cassandra database using irace

Bioinformatics Workflows With NoSQL Database in Cloud Computing

Emerging trend of big data analytics in bioinformatics: a literature review

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