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Big Data in Medical Image Processing
Published in R. Suganya, S. Rajaram, A. Sheik Abdullah, Big Data in Medical Image Processing, 2018
R. Suganya, S. Rajaram, A. Sheik Abdullah
A document store database (also known as a document-oriented database, aggregate database, or simply document store or document database) is a database that uses a document-oriented model to store data. Document store databases store each record and its associated data within a single document like a single patient history. It includes anthropometric data, clinical data, pharmacy data, scan reports, surgery reports, hospital admission reports, etc. as a single document. Each document contains semi-structured data that can be queried against using various query and analytics tools of the DBMS. Relational databases store data within multiple tables, each table containing columns, and each row represents each record. Information about any given entity could be spread out among many tables. Data from different tables can only be associated by establishing a relationship between the tables.
A Classification Perspective of Big Data Mining
Published in Ibrahiem M. M. El Emary, Anna Brzozowska, Shaping the Future of ICT, 2017
Manal Abdullah, Nojod M. Alotaibi
NoSQL (not only SQL) is a term used to designate database management systems that differ from classic RDBMS in some way (“NoSQL Databases Explained” 2015). These data stores may not require fixed table schemas, usually avoid join operations, do not attempt to provide ACID (atomicity, consistency, isolation, durability) properties, and typically scale horizontally. There are several types of NoSQL databases: Key–value stores—In key–value stores, each single item in the database is stored as an attribute name (or key), together with its value. Examples of key–value stores are Amazon’s Dynamo and Oracle’s BerkeleyDB.Document-oriented database—It is a database designed for storing, retrieving, and managing document-oriented or semistructured data. Examples of these databases are CouchDB and MongoDB.Column stores—It stores columns of data together, instead of rows. Examples include Cassandra and Apache HBase.Graph database—It contains nodes, edges, and properties to represent and store data. Examples of graph databases are Neo4j and HyperGraphDB.
NoSQL Databases and Big Data Strategies
Published in Unhelkar Bhuvan, Big Data Strategies for Agile Business, 2017
Document-centric (or document) databases expand on the concept of key–value stores. Documents inside a document-oriented database may be understood as similar to records in relational databases. The documents in these types of databases are the equivalent of value in the previously discussed KVPs. The document has a unique identifier (or key), which is used to retrieve the document. The document, however, is more than a value, as it can be a highly complex and searchable entity that can take in a variety of data types, including strings and arrays.
An Enhanced Entity Model for Converting Relational to Non-Relational Documents in Hospital Management System Based on Cloud Computing
Published in IETE Technical Review, 2022
A. Samydurai, K. Revathi, L. Karthikeyan, B. Vanathi, K. Devi
In the document-oriented database, the data are stored in the form of a document. Also, in this type of database, the schema values remain unfixed and each field comprises various documents. The stored documents can be in the form of arrays, integers, strings as well as documents. Mongo DB and Couch DB are the most common examples of document-oriented databases. In addition to this, the JSON format is employed in storing the data of the document-oriented database [38].
An open source analysis framework for large-scale building energy modeling
Published in Journal of Building Performance Simulation, 2020
Brian L. Ball, Nicholas Long, Katherine Fleming, Chris Balbach, Phylroy Lopez
The architecture of OSAF leverages Docker Swarm – a cluster of several Docker container images (also called services) that communicate with each other and various external problem-defining applications through a well-defined API. This setup is a significant improvement from OpenStudio Server, which leveraged virtual machines and a configuration management tool called Chef (Long et al. 2014). As depicted in Figure 2, a typical configuration consists of one computer or compute node that functions as the server node, and several other computers or compute nodes that function only as workers. The server node is responsible for analysis management, simulation queuing (either algorithmic or batch processing), and results management. It comprises four Docker container images assembled into a microservice architecture: A Mongo container providing the database MongoDB, which is a NoSQL-type database that uses a JavaScript Object Notation (JSON)-like schema. MongoDB is a free, open-source, cross-platform, and document-oriented database. This service is called ‘db.’A Redis container providing the Resque queuing system. Resque is an open-source Ruby library for creating background jobs, placing them on multiple queues, and processing them later. This service is called ‘queue.’An OpenStudio-Rserve container providing access to the R algorithms for optimization, calibration, and uncertainty quantification. These are discussed in more detail in Section 3.3.An OpenStudio-Server container – deployed as two separate services called ‘web’ and ‘web-background’ – manages the front-end graphical user interface and the back-end communication for the entire Docker Swarm cluster and communication with the workers. The worker services are also instances of the OpenStudio-Server container and are called ‘worker.’