The Application, Development and Learning of NoSQL

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 (10 June 2023) | Viewed by 6246

Special Issue Editors


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Guest Editor
IT, Multimedia and Telecommunications Department, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
Interests: NonSQL databases; analytics; e-learning; self-determined learning; eHealth; semantics
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Special Issue Information

Dear Colleagues,

The Big Data phenomenon has driven the necessity to manage massive data, which requires the development of new analytical and storage tools that go beyond relational models. The use of the relational model in databases has remained the status quo during recent decades, but massive data analysis and storage pose new requirements that are difficult to manage with relational databases, such as flexibility in data schemes, storage in distributed cluster environments, horizontal scalability or the need to enhance the availability of data against the consistency of information. That is why new models of databases emerged, besides relational models, and are better adapted to new storage needs. These databases mainly fall under the umbrella of NoSQL databases, but also include NewSQL, column-oriented and other kinds of databases. The objective of this Special Issue is to bring together research that shows the latest advances in these new kinds of databases (NoSQL, NewSQL and column-oriented), from both theoretical and practical perspectives. Therefore, this Special Issue not only addresses the theoretical aspects of NoSQL databases, but also their applications in real cases and the ways that they are learned. Therefore, papers that address theoretical advances, the application of databases for real problems, lessons learnt from case studies and educational experiences regarding these kinds of databases are welcome.

The objective of this Special Issue is to provide a forum for all researchers, practitioners and teachers who are working in NoSQL to present their innovative findings in this field. The topics of interest include (but are not limited to):

  • NoSQL databases;
  • New SQL;
  • Column-oriented databases;
  • New data warehouses;
  • Nonsql database applications;
  • Massive data processing;
  • Data analysis and NonSQL databases;
  • New nonsql database models.

State-of-the-art review articles, case studies, and experimental or theoretical articles are welcome.

Dr. Jordi Conesa Caralt
Prof. Dr. Antonio Sarasa Cabezuelo
Guest Editors

Manuscript Submission Information

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Keywords

  • big data
  • NoSQL databases
  • new SQL
  • column-store databases
  • Neo4j
  • MongoDB
  • Cassandra
  • Redis

Published Papers (2 papers)

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Research

16 pages, 5206 KiB  
Article
Experimental Evaluation of Graph Databases: JanusGraph, Nebula Graph, Neo4j, and TigerGraph
by Jéssica Monteiro, Filipe Sá and Jorge Bernardino
Appl. Sci. 2023, 13(9), 5770; https://doi.org/10.3390/app13095770 - 07 May 2023
Cited by 4 | Viewed by 4039
Abstract
NoSQL databases were created with the primary goal of addressing the shortcomings in the efficiency of relational databases, and can be of four types: document, column, key-value, and graph databases. Graph databases can store data and relationships efficiently, and have a flexible and [...] Read more.
NoSQL databases were created with the primary goal of addressing the shortcomings in the efficiency of relational databases, and can be of four types: document, column, key-value, and graph databases. Graph databases can store data and relationships efficiently, and have a flexible and easy-to-understand data schema. In this paper, we perform an experimental evaluation of the four most popular graph databases: JanusGraph, Nebula Graph, Neo4j, and TigerGraph. Database performance is evaluated using the Linked Data Benchmark Council’s Social Network Benchmark (LDBC SNB). In the experiments, we analyze the execution time of the queries, the loading time of the nodes and the RAM and CPU usage for each database. In our analysis, Neo4j was the graph database with the best performance across all metrics. Full article
(This article belongs to the Special Issue The Application, Development and Learning of NoSQL)
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18 pages, 7157 KiB  
Article
A Linked Data Application for Harmonizing Heterogeneous Biomedical Information
by Nicola Capuano, Pasquale Foggia, Luca Greco and Pierluigi Ritrovato
Appl. Sci. 2022, 12(18), 9317; https://doi.org/10.3390/app12189317 - 16 Sep 2022
Viewed by 1193
Abstract
In the biomedical field, there is an ever-increasing number of large, fragmented, and isolated data sources stored in databases and ontologies that use heterogeneous formats and poorly integrated schemes. Researchers and healthcare professionals find it extremely difficult to master this huge amount of [...] Read more.
In the biomedical field, there is an ever-increasing number of large, fragmented, and isolated data sources stored in databases and ontologies that use heterogeneous formats and poorly integrated schemes. Researchers and healthcare professionals find it extremely difficult to master this huge amount of data and extract relevant information. In this work, we propose a linked data approach, based on multilayer networks and semantic Web standards, capable of integrating and harmonizing several biomedical datasets with different schemas and semi-structured data through a multi-model database providing polyglot persistence. The domain chosen concerns the analysis and aggregation of available data on neuroendocrine neoplasms (NENs), a relatively rare type of neoplasm. Integrated information includes twelve public datasets available in heterogeneous schemas and formats including RDF, CSV, TSV, SQL, OWL, and OBO. The proposed integrated model consists of six interconnected layers representing, respectively, information on the disease, the related phenotypic alterations, the affected genes, the related biological processes, molecular functions, the involved human tissues, and drugs and compounds that show documented interactions with them. The defined scheme extends an existing three-layer model covering a subset of the mentioned aspects. A client–server application was also developed to browse and search for information on the integrated model. The main challenges of this work concern the complexity of the biomedical domain, the syntactic and semantic heterogeneity of the datasets, and the organization of the integrated model. Unlike related works, multilayer networks have been adopted to organize the model in a manageable and stratified structure, without the need to change the original datasets but by transforming their data “on the fly” to respond to user requests. Full article
(This article belongs to the Special Issue The Application, Development and Learning of NoSQL)
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