Big Data Challenges and Opportunities for Sustainable Industries and Societies

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 9817

Special Issue Editors


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Guest Editor
Jheronimus Academy of Data Science(JADS), Eindhoven University of Technology, Eindhoven, The Netherlands
Interests: big data engineering; data-intensive computing; social software engineering

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Guest Editor
Jheronimus Academy of Data Science (JADS), Tilburg University, Tilburg, The Netherlands
Interests: big data engineering; data-intensive computing; distributed systems; databases; data integration and governance

Special Issue Information

There is no doubt that data have become the new “oil” in today’s world. However, much like any other good, data can become a tool for the pursuit of both savory and unsavory objectives, and with the emergence of big data, s those objectives can now be of an even grander scale. This Special Issue aims at examining state-of-the-art practices regarding the use of data-intensive computing to make industries and societies more sustainable from many perspectives, including, but not limited to, societal, economical, entrepreneurial, and social. We particularly encourage submissions that refine or replicate the application of big data computing to known and established data-intensive computing problems, as well as of proven-true proof-of-concepts in the industry where data-intensive computing leads to more sustainable industrial or societal outcomes.

Assoc. Prof. Damian A. Tamburri
Prof. Dr. Willem-Jan van den Heuvel
Guest Editors

Manuscript Submission Information

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Keywords

  • Big data
  • Data-intensive computing
  • Social computing
  • Data-driven industrial engineering
  • Applied big data analytics
  • DataOps

Published Papers (1 paper)

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Research

21 pages, 2556 KiB  
Article
Performance Analysis of NoSQL and Relational Databases with CouchDB and MySQL for Application’s Data Storage
by Cornelia A. Győrödi, Diana V. Dumşe-Burescu, Doina R. Zmaranda, Robert Ş. Győrödi, Gianina A. Gabor and George D. Pecherle
Appl. Sci. 2020, 10(23), 8524; https://doi.org/10.3390/app10238524 - 28 Nov 2020
Cited by 20 | Viewed by 9383
Abstract
In the current context of emerging several types of database systems (relational and non-relational), choosing the type and database system for storing large amounts of data in today’s big data applications has become an important challenge. In this paper, we aimed to provide [...] Read more.
In the current context of emerging several types of database systems (relational and non-relational), choosing the type and database system for storing large amounts of data in today’s big data applications has become an important challenge. In this paper, we aimed to provide a comparative evaluation of two popular open-source database management systems (DBMSs): MySQL as a relational DBMS and, more recently, as a non-relational DBMS, and CouchDB as a non-relational DBMS. This comparison was based on performance evaluation of CRUD (CREATE, READ, UPDATE, DELETE) operations for different amounts of data to show how these two databases could be modeled and used in an application and highlight the differences in the response time and complexity. The main objective of the paper was to make a comparative analysis of the impact that each specific DBMS has on application performance when carrying out CRUD requests. To perform the analysis and to ensure the consistency of tests, two similar applications were developed in Java, one using MySQL and the other one using CouchDB database; these applications were further used to evaluate the time responses for each database technology on the same CRUD operations on the database. Finally, a comprehensive discussion based on the results of the analysis was performed that centered on the results obtained and several conclusions were revealed. Advantages and drawbacks for each DBMS are outlined to support a decision for choosing a specific type of DBMS that could be used in a big data application. Full article
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