Quasi-Relational Query Language Interface for Persistent Standardized EHRs: Using NoSQL Databases

2013 ◽  
pp. 182-196 ◽  
Author(s):  
Aastha Madaan ◽  
Wanming Chu ◽  
Yaginuma Daigo ◽  
Subhash Bhalla
Keyword(s):  
Query Language ◽  
Nosql Databases

scholarly journals Performance Benchmarking of Key-Value Store NoSQL Databases

2018 ◽  
Vol 8 (6) ◽  
pp. 5333
Author(s):  
Omoruyi Osemwegie ◽  
Kennedy Okokpujie ◽  
Nsikan Nkordeh ◽  
Charles Ndujiuba ◽  
Samuel John ◽  
...  
Keyword(s):  
Data Storage ◽  
Web Applications ◽  
Query Language ◽  
Research Work ◽  
Database Systems ◽  
Nosql Databases ◽  
Performance Benchmarking ◽  
Nosql Database ◽  
Structured Query Language ◽  
Web Developers

<p>Increasing requirements for scalability and elasticity of data storage for web applications has made Not Structured Query Language NoSQL databases more invaluable to web developers. One of such NoSQL Database solutions is Redis. A budding alternative to Redis database is the SSDB database, which is also a key-value store but is disk-based. The aim of this research work is to benchmark both databases (Redis and SSDB) using the Yahoo Cloud Serving Benchmark (YCSB). YCSB is a platform that has been used to compare and benchmark similar NoSQL database systems. Both databases were given variable workloads to identify the throughput of all given operations. The results obtained shows that SSDB gives a better throughput for majority of operations to Redis’s performance.</p>

MonogDB

2017 ◽  
pp. 64-91
Author(s):  
Sonali Tidke
Keyword(s):  
Management System ◽  
Database Management ◽  
Query Language ◽  
Database Management System ◽  
Management Model ◽  
Nosql Databases ◽  
Performance Indexes ◽  
And Behavior ◽  
Relational Database Management ◽  
Partitioning Technique

MongoDB is a NoSQL type of database management system which does not adhere to the commonly used relational database management model. MongoDB is used for horizontal scaling across a large number of servers which may have tens, hundreds or even thousands of servers. This horizontal scaling is performed using sharding. Sharding is a database partitioning technique which partitions large database into smaller parts which are easy to manage and faster to access. There are hundreds of NoSQL databases available in the market. But each NoSQL product is different in terms of features, implementations and behavior. NoSQL and RDBMS solve different set of problems and have different requirements. MongoDB has a powerful query language which extends SQL to JSON enabling developers to take benefit of power of SQL and flexibility of JSON. Along with support for select/from/where type of queries, MongoDB supports aggregation, sorting, joins as well as nested array and collections. To improve query performance, indexes and many more features are also available.

No-FSQL

2016 ◽  
Vol 5 (2) ◽  
pp. 54-63 ◽  
Author(s):  
Ines BenAli-Sougui ◽  
Minyar Sassi Hidri ◽  
Amel Grissa-Touzi
Keyword(s):  
Query Language ◽  
Nosql Databases ◽  
Database Model ◽  
Precise Data ◽  
Hands On ◽  
Ambiguous Data

NoSQL (Not only SQL) is an efficient database model for storing and manipulating huge quantities of precise data. However, most NoSQL databases scale well as data grows and often are flexible enough to accommodate imprecise and ambiguous data. This comprehensive hands-on guide presents fundamental concepts and practical solutions for using fuzziness with NoSQL to deals with fuzzy databases (FDB). In this paper, the authors present a graph-based fuzzy NoSQL model to deal with large fuzzy databases while extending the NoSQL one. The authors consider the cypher declarative query language proposed for Neo4j which is the current leader on this market to querying fuzzy databases.

Query Languages for Graph Databases

2019 ◽  
pp. 645-659
Author(s):  
Kornelije Rabuzin
Keyword(s):  
Relational Databases ◽  
Query Language ◽  
Main Idea ◽  
Query Languages ◽  
End Users ◽  
The Other ◽  
Graph Database ◽  
Graph Databases ◽  
Nosql Databases ◽  
The Past

In the past few years, many NoSQL databases have emerged, including graph databases. NoSQL databases have certain advantages and they can be used in certain domains as an alternative to relational databases. In order to use graph databases, one needs to be familiar with specific languages like Cypher Query Language (CQL) or Gremlin. However, some statements in CQL can be considered too complex for end users as it is shown later on. Because of that, the main idea of this chapter is to explore two other languages for graph databases. One of them is new and it is used to pose queries visually. Since CQL does not support recursion, views, etc., the other language is used to show how to use recursion and views on a graph database.

Query Languages in NoSQL Databases

2015 ◽  
pp. 415-437 ◽  
Author(s):  
Maristela Holanda ◽  
Jane Adriana Souza
Keyword(s):  
Query Language ◽  
Data Retrieval ◽  
Query Languages ◽  
Nosql Databases ◽  
Different Types ◽  
Application Codes

This chapter aims to investigate how NoSQL (Not Only SQL) databases provide query language and data retrieval mechanisms. Users attest to many advantages in using the NoSQL databases for specific applications, however, they also report that querying and retrieving data easily continues to be a problem. The NoSQL operations require that, during the project, the queries must be thought of as built-in application codes. The authors intend to contribute to the investigation of querying, considering different types of NoSQL databases.

scholarly journals Benchmarking geospatial database on Kubernetes cluster

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Bharti Sharma ◽  
Poonam Bansal ◽  
Mohak Chugh ◽  
Adisakshya Chauhan ◽  
Prateek Anand ◽  
...  
Keyword(s):  
Spatial Data ◽  
Query Language ◽  
Research Work ◽  
Nosql Databases ◽  
Geospatial Database ◽  
Computationally Expensive ◽  
Execution Times ◽  
Container Orchestration ◽  
Geospatial Databases ◽  
Geo Database

AbstractKubernetes is an open-source container orchestration system for automating container application operations and has been considered to deploy various kinds of container workloads. Traditional geo-databases face frequent scalability issues while dealing with dense and complex spatial data. Despite plenty of research work in the comparison of relational and NoSQL databases in handling geospatial data, there is a shortage of existing knowledge about the performance of geo-database in a clustered environment like Kubernetes. This paper presents benchmarking of PostgreSQL/PostGIS geospatial databases operating on a clustered environment against non-clustered environments. The benchmarking process considers the average execution times of geospatial structured query language (SQL) queries on multiple hardware configurations to compare the environments based on handling computationally expensive queries involving SQL operations and PostGIS functions. The geospatial queries operate on data imported from OpenStreetMap into PostgreSQL/PostGIS. The clustered environment powered by Kubernetes demonstrated promising improvements in the average execution times of computationally expensive geospatial SQL queries on all considered hardware configurations compared to their average execution times in non-clustered environments.

Query Languages for Graph Databases

2018 ◽  
pp. 2031-2042
Author(s):  
Kornelije Rabuzin
Keyword(s):  
Relational Databases ◽  
Query Language ◽  
Main Idea ◽  
Query Languages ◽  
End Users ◽  
The Other ◽  
Graph Database ◽  
Graph Databases ◽  
Nosql Databases ◽  
The Past

In the past few years many NoSQL databases have emerged, including graph databases. NoSQL databases have certain advantages and they can be used in certain domains as an alternative to relational databases. In order to use graph databases, one needs to be familiar with specific languages like Cypher Query Language (CQL) or Gremlin. However, some statements in CQL can be considered too complex for end users as it is shown later on. Because of that the main idea of this paper is to explore two other languages for graph databases. One of them is new and it is used to pose queries visually. Since CQL does not support recursion, views, etc., the other language is used to show how to use recursion and views on a graph database.

scholarly journals A Comparative study of SQL Databases and NoSQL Databases for E-Commerce

2021 ◽  
Vol 9 (12) ◽  
pp. 409-412
Author(s):  
Disha Nakhare
Keyword(s):  
Comparative Study ◽  
Relational Databases ◽  
Query Language ◽  
Economic Status ◽  
Web Engineering ◽  
Nosql Databases ◽  
Structured Query Language

Abstract: With the advent of E-Commerce, businesses persistently examine various ways to improvise and accomplish their demands with web engineering that provide notable resolution. The progress in economic status demands colossal databases that store the data efficiently. The databases currently used are relational or non-relational. Both these types have their benefits and limitations that influence the overall processing of data. Non-relational databases are referred to as NoSQL-not only SQL, and Relational databases are known as SQL-Structured Query Language. It has been suggested in many studies that NoSQL databases surpass SQL databases. Our paper aims to evaluate these claims by analyzing the CRUD [Create, Read, Update, Delete] operations executed by both database types. Keywords: NoSQL, SQL, Non-relational Databases, MySQL, E-Commerce, MongoDb , Relational Databases

The End of Relational Databases Domination

2018 ◽  
pp. 1-33
Keyword(s):  
Relational Databases ◽  
Query Language ◽  
Nosql Databases ◽  
Database Development ◽  
Relational Data Model ◽  
Durability Properties ◽  
Nosql Database ◽  
Soft State ◽  
Database Models ◽  
Consistent Approach

The chapter gives an overview of the three main stages of database development: hierarchical and network database models, relational database model, and NoSQL databases. It gives a short overview of the pillars of relational databases: relational data model, ACID (atomicity, consistency, isolation, and durability) properties of a transaction, and SQL (structured query language). Also, the concepts that make the base for NoSQL database development are explained, including the CAP (Consistency, Availability, Partitioning) theorem, the BASE (Basically Available, Soft-state, Eventually consistent) approach, and the sharding phenomenon. At last, the limitations of relational databases which led to the development of NoSQL databases are discussed.

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