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Integration of AI and Database Technologies, Its Applications

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 (1 August 2022) | Viewed by 2955

Special Issue Editors


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Guest Editor
School of Information and Communication Engineering, Chungbuk National University, Cheongju Chungbuk 28644, Korea
Interests: big data; intelligent database; storage management system; distributed computing; social network service

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Guest Editor
Department of SW Convergence Technology, Wonkwang University, Iksan-daero 460, Korea
Interests: database; mobile ad-hoc network; big data; social network service

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Guest Editor
School of Computer Engineering and Information Technology, Korea National University of Transportation, Chungcheongbuk-do 370914, Korea
Interests: database; senor network; storage system; big data

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Guest Editor
Department of Computer Science, California State University, Fullerton, CA 92831, USA
Interests: data mining; computational finance; databases; evolutionary computation

Special Issue Information

Dear Colleagues,

With the advent of big data, database technologies have evolved over the past 15 years to satisfy the characteristics of big data, such as volume, velocity, and diversity. To support volume characteristics, NoSQL technologies have been introduced that enable the distributed parallel management and processing of data. Since then, some technologies have evolved towards processing data streams in order to satisfy velocity characteristics. In addition, multi-model technologies are emerging to support various data models in consideration of variety characteristics.

Database techniques for big data are being applied to AI data pipelines to support learning data, as large amounts of learning data become important in artificial intelligence (AI), such as deep learning (DL). In AI data pipelines, database technologies serve data delivery and data life cycle management as an effective search and learning framework for large amounts of learning data. Furthermore, research is being conducted to apply AI data pipelines built on big data-related database technologies in various domains. On the other hand, research has been actively conducted to apply AI technologies to database technologies. In particular, studies have been conducted to improve performance by redesigning components to apply AI technologies on their own, to link AI functions (intelligent recommendation engines) to the existing DBMS, or to develop applications using them.

This Special Issue, “Integration of AI and Database technologies, Its Applications”, will publish original full papers including survey, theory, practice and applications of the integration of AI and database technologies. Papers that have been presented in conferences would also be welcomed.

Dr. Jae Soo Yoo
Dr. Kyoungsoo Bok
Dr. Seokil Song
Dr. Christopher Ryu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent database
  • intelligent data processing
  • database functions or components for AI
  • database functions or components for big data
  • integration of AI and database technologies
  • intelligent inference engines and databases
  • intelligent data applications. Big data
  • integration
  • inference engine
  • intelligent application

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Published Papers (1 paper)

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Research

18 pages, 6057 KiB  
Article
An Efficient Distributed SPARQL Query Processing Scheme Considering Communication Costs in Spark Environments
by Jongtae Lim, Byounghoon Kim, Hyeonbyeong Lee, Dojin Choi, Kyoungsoo Bok and Jaesoo Yoo
Appl. Sci. 2022, 12(1), 122; https://doi.org/10.3390/app12010122 - 23 Dec 2021
Cited by 2 | Viewed by 2256
Abstract
Various distributed processing schemes were studied to efficiently utilize a large scale of RDF graph in semantic web services. This paper proposes a new distributed SPARQL query processing scheme considering communication costs in Spark environments to reduce I/O costs during SPARQL query processing. [...] Read more.
Various distributed processing schemes were studied to efficiently utilize a large scale of RDF graph in semantic web services. This paper proposes a new distributed SPARQL query processing scheme considering communication costs in Spark environments to reduce I/O costs during SPARQL query processing. We divide a SPARQL query into several subqueries using a WHERE clause to process a query of an RDF graph stored in a distributed environment. The proposed scheme reduces data communication costs by grouping the divided subqueries in related nodes through the index and processing them, and the grouped subqueries calculate the cost of all possible query execution paths to select an efficient query execution path. The efficient query execution path is selected through the algorithm considering the data parsing cost of all possible query execution paths, amount of data communication, and queue time per node. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes. Full article
(This article belongs to the Special Issue Integration of AI and Database Technologies, Its Applications)
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