Cited 6 time in
Human-intelligence workflow management for the big data of augmented reality on cloud infrastructure
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Kim, Hyun-Woo | - |
| dc.contributor.author | Park, Jong Hyuk | - |
| dc.contributor.author | Jeong, Young-Sik | - |
| dc.date.accessioned | 2024-08-08T03:30:49Z | - |
| dc.date.available | 2024-08-08T03:30:49Z | - |
| dc.date.issued | 2018-03-01 | - |
| dc.identifier.issn | 0925-2312 | - |
| dc.identifier.issn | 1872-8286 | - |
| dc.identifier.uri | https://scholarworks.dongguk.edu/handle/sw.dongguk/16986 | - |
| dc.description.abstract | Human-intelligence workflow management (HIWM) is proposed as a means of dynamically distributing and processing storage work and calculating operations for fast augmented reality (AR) service provision on diverse smart mobile devices based on human behavior to apply the next generation web environments. In HIWM, pre-processing is performed to minimize service response time according to the definition of metadata and user requests for AR services. Basically, to process big data for AR services, a dynamic job distribution scheme is proposed based on the computing capacity of desktops constituting the cloud infrastructures. For final AR services by HIWM, the results of the evaluation of the performance of HIWM in relation to big data processing time are presented. The results show that processing time is 40.56% less than that of the existed methods in proportion to AR service requests. (c) 2017 Elsevier B.V. All rights reserved. | - |
| dc.format.extent | 8 | - |
| dc.language | 영어 | - |
| dc.language.iso | ENG | - |
| dc.publisher | ELSEVIER SCIENCE BV | - |
| dc.title | Human-intelligence workflow management for the big data of augmented reality on cloud infrastructure | - |
| dc.type | Article | - |
| dc.publisher.location | 네델란드 | - |
| dc.identifier.doi | 10.1016/j.neucom.2017.04.082 | - |
| dc.identifier.scopusid | 2-s2.0-85036526636 | - |
| dc.identifier.wosid | 000424888300004 | - |
| dc.identifier.bibliographicCitation | NEUROCOMPUTING, v.279, pp 19 - 26 | - |
| dc.citation.title | NEUROCOMPUTING | - |
| dc.citation.volume | 279 | - |
| dc.citation.startPage | 19 | - |
| dc.citation.endPage | 26 | - |
| dc.type.docType | Article | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | scie | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.relation.journalResearchArea | Computer Science | - |
| dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
| dc.subject.keywordPlus | VIRTUALIZATION | - |
| dc.subject.keywordAuthor | Human-intelligence workflow management | - |
| dc.subject.keywordAuthor | Big data | - |
| dc.subject.keywordAuthor | Cloud infrastructure | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
30, Pildong-ro 1-gil, Jung-gu, Seoul, 04620, Republic of Korea+82-2-2260-3114
Copyright(c) 2023 DONGGUK UNIVERSITY. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.
