An adaptive spark-based framework for querying large-scale NoSQL and relational databases.

The growing popularity of big data analysis and cloud computing has created new big data management standards. Sometimes, programmers may interact with a number of heterogeneous data stores depending on the information they are responsible for: SQL and NoSQL data stores. Interacting with heterogeneo...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Eman Khashan, Ali Eldesouky, Sally Elghamrawy
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3cacfe4d48c44ca29d55082256e8a7ed
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:3cacfe4d48c44ca29d55082256e8a7ed
record_format	dspace
spelling	oai:doaj.org-article:3cacfe4d48c44ca29d55082256e8a7ed2021-12-02T20:17:46ZAn adaptive spark-based framework for querying large-scale NoSQL and relational databases.1932-620310.1371/journal.pone.0255562https://doaj.org/article/3cacfe4d48c44ca29d55082256e8a7ed2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255562https://doaj.org/toc/1932-6203The growing popularity of big data analysis and cloud computing has created new big data management standards. Sometimes, programmers may interact with a number of heterogeneous data stores depending on the information they are responsible for: SQL and NoSQL data stores. Interacting with heterogeneous data models via numerous APIs and query languages imposes challenging tasks on multi-data processing developers. Indeed, complex queries concerning homogenous data structures cannot currently be performed in a declarative manner when found in single data storage applications and therefore require additional development efforts. Many models were presented in order to address complex queries Via multistore applications. Some of these models implemented a complex unified and fast model, while others' efficiency is not good enough to solve this type of complex database queries. This paper provides an automated, fast and easy unified architecture to solve simple and complex SQL and NoSQL queries over heterogeneous data stores (CQNS). This proposed framework can be used in cloud environments or for any big data application to automatically help developers to manage basic and complicated database queries. CQNS consists of three layers: matching selector layer, processing layer, and query execution layer. The matching selector layer is the heart of this architecture in which five of the user queries are examined if they are matched with another five queries stored in a single engine stored in the architecture library. This is achieved through a proposed algorithm that directs the query to the right SQL or NoSQL database engine. Furthermore, CQNS deal with many NoSQL Databases like MongoDB, Cassandra, Riak, CouchDB, and NOE4J databases. This paper presents a spark framework that can handle both SQL and NoSQL Databases. Four scenarios' benchmarks datasets are used to evaluate the proposed CQNS for querying different NoSQL Databases in terms of optimization process performance and query execution time. The results show that, the CQNS achieves best latency and throughput in less time among the compared systems.Eman KhashanAli EldesoukySally ElghamrawyPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255562 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Eman Khashan Ali Eldesouky Sally Elghamrawy An adaptive spark-based framework for querying large-scale NoSQL and relational databases.
description	The growing popularity of big data analysis and cloud computing has created new big data management standards. Sometimes, programmers may interact with a number of heterogeneous data stores depending on the information they are responsible for: SQL and NoSQL data stores. Interacting with heterogeneous data models via numerous APIs and query languages imposes challenging tasks on multi-data processing developers. Indeed, complex queries concerning homogenous data structures cannot currently be performed in a declarative manner when found in single data storage applications and therefore require additional development efforts. Many models were presented in order to address complex queries Via multistore applications. Some of these models implemented a complex unified and fast model, while others' efficiency is not good enough to solve this type of complex database queries. This paper provides an automated, fast and easy unified architecture to solve simple and complex SQL and NoSQL queries over heterogeneous data stores (CQNS). This proposed framework can be used in cloud environments or for any big data application to automatically help developers to manage basic and complicated database queries. CQNS consists of three layers: matching selector layer, processing layer, and query execution layer. The matching selector layer is the heart of this architecture in which five of the user queries are examined if they are matched with another five queries stored in a single engine stored in the architecture library. This is achieved through a proposed algorithm that directs the query to the right SQL or NoSQL database engine. Furthermore, CQNS deal with many NoSQL Databases like MongoDB, Cassandra, Riak, CouchDB, and NOE4J databases. This paper presents a spark framework that can handle both SQL and NoSQL Databases. Four scenarios' benchmarks datasets are used to evaluate the proposed CQNS for querying different NoSQL Databases in terms of optimization process performance and query execution time. The results show that, the CQNS achieves best latency and throughput in less time among the compared systems.
format	article
author	Eman Khashan Ali Eldesouky Sally Elghamrawy
author_facet	Eman Khashan Ali Eldesouky Sally Elghamrawy
author_sort	Eman Khashan
title	An adaptive spark-based framework for querying large-scale NoSQL and relational databases.
title_short	An adaptive spark-based framework for querying large-scale NoSQL and relational databases.
title_full	An adaptive spark-based framework for querying large-scale NoSQL and relational databases.
title_fullStr	An adaptive spark-based framework for querying large-scale NoSQL and relational databases.
title_full_unstemmed	An adaptive spark-based framework for querying large-scale NoSQL and relational databases.
title_sort	adaptive spark-based framework for querying large-scale nosql and relational databases.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/3cacfe4d48c44ca29d55082256e8a7ed
work_keys_str_mv	AT emankhashan anadaptivesparkbasedframeworkforqueryinglargescalenosqlandrelationaldatabases AT alieldesouky anadaptivesparkbasedframeworkforqueryinglargescalenosqlandrelationaldatabases AT sallyelghamrawy anadaptivesparkbasedframeworkforqueryinglargescalenosqlandrelationaldatabases AT emankhashan adaptivesparkbasedframeworkforqueryinglargescalenosqlandrelationaldatabases AT alieldesouky adaptivesparkbasedframeworkforqueryinglargescalenosqlandrelationaldatabases AT sallyelghamrawy adaptivesparkbasedframeworkforqueryinglargescalenosqlandrelationaldatabases
_version_	1718374338807201792

An adaptive spark-based framework for querying large-scale NoSQL and relational databases.

Ejemplares similares