Performance Evaluation of NoSQL and SQL Queries in Response Time for the E-government

Flores, A.; Ramirez, Stalin; Toasa, Renato M.; Vargas, Javier; Barrionuevo, Renato Urvina; Lavín, José M.

doi:10.1109/icedeg.2018.8372362

Cited by 17 publications

(10 citation statements)

References 8 publications

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“…Publications are grouped by research areas and their trends compared over time, making it possible to identify some dominant fields, including computer technology, international economics, computer sciences, and policy sciences. Data collected in the Ecuadorian Repository of Researchers (REDI in Spanish) suggest that, between 2016 and 2018, only 0.72% of investigations were related to data analysis and big data [54]. Simultaneously, there is evidence that contributions related to robotics, wireless, big data, computer networks, and digital television have decreased.…”

Section: Discussionmentioning

confidence: 99%

Exploring the Landscape of Data Analysis: A Review of Its Application and Impact in Ecuador

2023

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Data analysis is increasingly critical in aiding decision-making within public and private institutions. This paper scrutinizes the status quo of big data and data analysis and its applications within Ecuador, focusing on its societal, educational, and industrial impact. A detailed literature review was conducted from academic databases such as SpringerLink, Scopus, IEEE Xplore, Web of Science, and ACM, incorporating research from inception until May 2023. The search process adhered to the PRISMA statement, employing specific inclusion and exclusion criteria. The analysis revealed that data implementation in Ecuador, while recent, has found noteworthy applications in six principal areas, classified using ISCED: education, science, engineering, health, social, and services. In the scientific and engineering sectors, big data has notably contributed to disaster mitigation and optimizing resource allocation in smart cities. Its application in the social sector has fortified cybersecurity and election data integrity, while in services, it has enhanced residential ICT adoption and urban planning. Health sector applications are emerging, particularly in disease prediction and patient monitoring. Educational applications predominantly involve student performance analysis and curricular evaluation. This review emphasizes that while big data’s potential is being gradually realized in Ecuador, further research, data security measures, and institutional interoperability are required to fully leverage its benefits.

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Section: Discussionmentioning

confidence: 99%

Exploring the Landscape of Data Analysis: A Review of Its Application and Impact in Ecuador

2023

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“…For this analysis we implement semi-stream join process for TABLE 1. Structured vs Un-structured database terminologies [9].…”

Section: Structured (Sql) Vs Un-structured (Nosql) Database Terminology Comparison Is Shown In Tablementioning

confidence: 99%

“…Response time on relational and non-relational database models is focused in the work by [9], where several load tests are performed on SQL Server and MongoDB. MongoDB offers better performance over Linux operating systems, while SQL Server tends to increase response time when it comes to quantity.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Not Only SQL Semi-Stream Join Using MongoDB for Real-Time Data Warehousing

Mehmood

Anees

2019

IEEE Access

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Data warehousing has been indispensable to enterprises for decades. However, infrequently updated data warehouse environment does not support quicker business decisions and faster data recovery in case of transformation or load issue. Implementation of real-time data warehouse provides solution to update problems of enterprises. Efficient stream processing for un-structured(NoSQL) and structured(SQL) data from various sources is required for the successful implementation of real-time data warehousing. We have done an analysis between un-structured and structured semi-stream join processing, using efficient database engine MongoDB at Extraction-Transformation-Loading phase. Semi-stream tuples coming from different sources are joined with disk-based master data, based on keys in memory, for both un-structured and structured documents(tuples) using MongoDB server, where the I/O rates are different for both inputs. Through experiments, in this paper we have analyzed the CPU and memory usage for real-time semi-stream join processing through two types of tests, un-structured and structured data streams using synthetic and real datasets. The results show that, memory usage and execution time remains consistent for a given specification irrespective of the nature of data streams (un-structured or structured), even when incoming semi-streams are growing.INDEX TERMS NoSQL/SQL, semi-stream join, real-time data warehousing, MongoDB.

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“…NoSQL is highly scalable, allowing it to grow per the demands of the data already in existence. Though it should come as no surprise that this database management is thought to be the best option for processing large amounts of constantly changing data (Flores et al, 2018). The Document Base, Key Value Base, Graph Store, and Wide Column Store are the types of NOSQL databases (Kamal et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Complex SQL-NoSQL Query Translation for Data Lake Management

Nurhadi¹,

Kadir²,

Surin³

2022

Journal of Computer Science

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A data lake refers to an extremely large data resource or repository. Data lakes store large amounts of data and use advanced analytics to pair data from multiple sources with different types of structured, semistructured, and unstructured information. NoSQL databases such as Mongodb, Redis, Neo4j, and Cassandra are nontabular and they store data differently rather than use relational tables. NoSQL databases come in many forms, mostly documents, key values, wide columns, and graphs based on their data model. NoSQL gives less complicated scalability and higher overall performance as compared with traditional relational databases. NoSQL databases can store different types of data, but they cannot fully support Automation, Consistency, Isolation, and endurance (ACID) features, i.e., trigger functions in multi-transaction management, because a NoSQL database uses a non-relational database system. Thus, an interpreter is necessary for SQL-to-NoSQL queries. We used ANTLR (ANother Tool for Language Recognition), which has five main stages: The input SQL query, the tokenizer, the parser, the parser tree, and lastly the generation of the query results in NoSQL. The tool gave users to write a flexible multi-pass language parser that is expected to solve problems in querying complex ACID functions and other problems in complex queries in NoSQL databases. In the measurement, analysis, and evaluation of the translation results through the comparison of each NoSQL criterion against a Relational Database Management System (MySQL), the scores obtained were as follows. The performance criterion achieved the highest score (98.40%) by the MongoDB database, followed by scalability (97.40%) and accuracy (97.00%). The criterion with the lowest score was complexity (91.65%).

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Performance Evaluation of NoSQL and SQL Queries in Response Time for the E-government

Cited by 17 publications

References 8 publications

Exploring the Landscape of Data Analysis: A Review of Its Application and Impact in Ecuador

Exploring the Landscape of Data Analysis: A Review of Its Application and Impact in Ecuador

Performance Analysis of Not Only SQL Semi-Stream Join Using MongoDB for Real-Time Data Warehousing

Complex SQL-NoSQL Query Translation for Data Lake Management

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