Efficiency of JSON for Data Retrieval in Big Data

2017 ◽  
Vol 7 (1) ◽  
pp. 250
Author(s):  
Mohd Kamir Yusof
Keyword(s):  
Big Data ◽  
Large Volume ◽  
Data Retrieval ◽  
Markup Language ◽  
Publication Data ◽  
Huge Data ◽  
Powerful Approach ◽  
Extensible Markup ◽  
Database Technology ◽  
And Storage

Big data is the latest industry buzzword to describe large volume of structured and unstructured data that can be difficult to process and analyze. Most of organization looking for the best approach to manage and analyze the large volume of data especially in making a decision. XML is chosen by many organization because of powerful approach during retrieval and storage processes. However, XML approach, the execution time for retrieving large volume of data are still considerably inefficient due to several factors. In this contribution, two databases approaches namely Extensible Markup Language (XML) and Java Object Notation (JSON) were investigated to evaluate their suitability for handling thousands records of publication data. The results showed JSON is the best choice for query retrieving speed and CPU usage. These are essential to cope with the characteristics of publication’s data. Whilst, XML and JSON technologies are relatively new to date in comparison to the relational database. Indeed, JSON technology demonstrates greater potential to become a key database technology for handling huge data due to increase of data annually.

scholarly journals Efficiency of Flat File Database Approach in Data Storage and Data Extraction for Big Data

2018 ◽  
Vol 9 (2) ◽  
pp. 460
Author(s):  
Mohd Kamir Yusof ◽  
Mustafa Man
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Large Volume ◽  
Data Extraction ◽  
Flat File ◽  
Huge Data ◽  
Powerful Approach ◽  
Extensible Markup ◽  
Database Technology ◽  
And Storage

Big data is the latest industry buzzword to describe large volume of structured and unstructured data that can be difficult to process and analyze. Most of organization looking for the best approach to manage and analyze the large volume of data especially in making a decision. XML and JSON are chosen by many organization because of powerful approach during retrieval and storage processes. However, these approaches, the execution time for retrieving large volume of data are still considerably inefficient due to several factors. In this contribution, three databases approaches namely Extensible Markup Language (XML), Java Object Notation (JSON) and Flat File database approach were investigated to evaluate their suitability for handling thousands records of publication data. The results showed flat file is the best choice for query retrieving speed and CPU usage. These are essential to cope with the characteristics of publication’s data. Whilst, XML, JSON and Flat File database approach technologies are relatively new to date in comparison to the relational database. Indeed, Text File Format technology demonstrates greater potential to become a key database technology for handling huge data due to increase of data annually.

Efficiency of JSON approach for Data Extraction and Query Retrieval

2016 ◽  
Vol 4 (1) ◽  
pp. 203 ◽  
Author(s):  
Mohd Kamir Yusof ◽  
Mustafa Man
Keyword(s):  
Information System ◽  
Relational Database ◽  
Execution Time ◽  
Database Management ◽  
Data Extraction ◽  
Extensible Markup Language ◽  
Markup Language ◽  
Huge Data ◽  
Extensible Markup ◽  
Database Technology

<p>Students’ Information System (SIS) in Universiti Sultan Zainal Abidin (UniSZA) handles thousands of records on the information of students, subject registration, etc. Efficiency of storage and query retrieval of these records is the matter of database management especially involving with huge data. However, the execution time for storing and retrieving these data are still considerably inefficient due to several factors. In this contribution, two database approaches namely Extensible Markup Language (XML) and JavaScript Object Notation (JSON) were investigated to evaluate their suitability for handling thousands records in SIS. The results showed JSON is the best choice for storage and query speed. These are essential to cope with the characteristics of students’ data. Whilst, XML and JSON technologies are relatively new to date in comparison to the relational database. Indeed, JSON technology demonstrates greater potential to become a key database technology for handling huge data due to an increase of data annually.</p>

scholarly journals Storing XML Documents in Databases

2005 ◽  
pp. 658-664
Author(s):  
Albrecht Schmidt ◽  
Stefan Manegold ◽  
Martin Kersten
Keyword(s):  
Data Management ◽  
Relational Database ◽  
Query Language ◽  
Transaction Management ◽  
Markup Language ◽  
Xml Documents ◽  
Management Technology ◽  
Extensible Markup ◽  
Database Technology ◽  
Exchange Data

Ever since the Extensible Markup Language (XML) (W3C, 1998b) began to be used to exchange data between diverse sources, interest has grown in deploying data management technology to store and query XML documents. A number of approaches propose to adapt relational database technology to store and maintain XML documents (Deutsch, Fernandez & Suciu, 1999; Florescu & Kossmann, 1999; Klettke & Meyer, 2000; Shanmugasundaram et al., 1999; Tatarinov et al., 2002; O’Neil et al., 2004). The advantage is that the XML repository inherits all the power of mature relational technology like indexes and transaction management. For XML-enabled querying, a declarative query language (Chamberlin et al., 2001) is available.

scholarly journals Study of Analysing and Processing of Big Data by Organizations in Indian Context

2020 ◽  
pp. 413-417
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Business Organization ◽  
Indian Context ◽  
Huge Data ◽  
Enormous Amount ◽  
Data Elements ◽  
And Storage

Every business organization needs valuable data and insights for understanding audience intent and consumer’s likings. Big data in this acts as a significant part as it supports in precede the needs of customers for which the data needs to be well presented and appropriately analyzed. Big Data permits organization to segregate customers in broad way which permits a business to hold consumers in a real-time, as in this tough competitive time you need to treat customers how they want. In simplified term “Big Data is a mix of processes and tools by which huge data grid through various form with each other and enormous amount of heterogeneous and rationalized information is created which in addition, used to figure out the utmost valuable customers. It also provide assistance for businesses to innovate, create and pitch new experiences, services, and products. The availability of such information creates opportunities for organizations. The paper here discusses about big data elements, its maintenance, handling and storage of varieties of big data by organizations and gains of big data analytics to organizations. The paper also analyze about the velocity of data generated and analysis of big data by organizations to understand its impact on organization working and consumer decision respectively. The paper also gives avenues for future research by explaining the application and practices of organization in the era of big data analytics.

Visualization and Storage of Big Data for Linguistic Applications

2018 ◽  
pp. 723-748
Author(s):  
Dan Ophir
Keyword(s):  
Big Data ◽  
Computational Power ◽  
Know How ◽  
Huge Data ◽  
The World ◽  
The Media ◽  
Operating Instructions ◽  
And Storage ◽  
Human Sense ◽  
Voice Recording

The following two main tendencies occur: 1) increase in the amount of the computational power around the world and increasing its sensitivity and functionality (communication, imaging, voice recording, position retrieval, etc.) causing growth of data; 2) decrease in the qualifications which are required to operate that computational power are two phenomena feeding themselves mutually: increasing the amount of PC's, Laptops, iPads, iPhones available with simpler and more intuitive operating instructions. This situation requires simplifying the data perception by the more developed human sense – the vision by untrained person even by a kid who doesn't know how to read and write. Such approach may easily make the media accessible to more and more people. Therefore, so called human interfaces are mainly supported by the vision, the most accurate human sense which demands developing the present methodology of visualizing huge data.

Mining Association Rules from XML Documents

2007 ◽  
pp. 79-103 ◽  
Author(s):  
Laura Irina Rusu ◽  
Wenny Rahayu ◽  
David Taniar
Keyword(s):  
Knowledge Discovery ◽  
Association Rules ◽  
Large Volume ◽  
Web Application ◽  
Markup Language ◽  
Xml Documents ◽  
Rapid Changes ◽  
Extensible Markup ◽  
Hidden Knowledge ◽  
The Web

This chapter presents some of the existing mining techniques for extracting association rules out of XML documents, in the context of rapid changes in the Web knowledge discovery area. The initiative of this study was driven by the fast emergence of XML (eXtensible Markup Language) as a standard language for representing semi-structured data and as a new standard of exchanging information between different applications. The data exchanged as XML documents becomes every day richer and richer, so the necessity to not only store these large volume of XML data for later use, but to mine them as well, to discover interesting information, has became obvious. The hidden knowledge can be used in various ways, for example to decide on a business issue or to make predictions about future e-customer behaviour in a web-application. One type of knowledge which can be discovered in a collection of XML documents relates to association rules between parts of the document, and this chapter presents some of the top techniques for extracting them.

An Electronic Study Form to Support Collaborating Agents in the Management of Clinical Knowledge

1999 ◽  
Vol 38 (03) ◽  
pp. 154-157
Author(s):  
W. Fierz ◽  
R. Grütter
Keyword(s):  
Procedural Knowledge ◽  
Health Care Provision ◽  
Markup Language ◽  
Artificial Agents ◽  
Care Provision ◽  
Clinical Knowledge ◽  
Extensible Markup ◽  
Implementation Level ◽  
Biological Organisms ◽  
Representation Of Knowledge

AbstractWhen dealing with biological organisms, one has to take into account some peculiarities which significantly affect the representation of knowledge about them. These are complemented by the limitations in the representation of propositional knowledge, i. e. the majority of clinical knowledge, by artificial agents. Thus, the opportunities to automate the management of clinical knowledge are widely restricted to closed contexts and to procedural knowledge. Therefore, in dynamic and complex real-world settings such as health care provision to HIV-infected patients human and artificial agents must collaborate in order to optimize the time/quality antinomy of services provided. If applied to the implementation level, the overall requirement ensues that the language used to model clinical contexts should be both human- and machine-interpretable. The eXtensible Markup Language (XML), which is used to develop an electronic study form, is evaluated against this requirement, and its contribution to collaboration of human and artificial agents in the management of clinical knowledge is analyzed.

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