@article{ART002376798},
author={YOONJIHAE and Kang Sun-Kyung},
title={Development of Apparel Service Distribution Pattern Prediction Application Service Using Big Data},
journal={Journal of Knowledge Information Technology and Systems},
issn={1975-7700},
year={2018},
volume={13},
number={4},
pages={451-458},
doi={10.34163/jkits.2018.13.4.005}
TY - JOUR
AU - YOONJIHAE
AU - Kang Sun-Kyung
TI - Development of Apparel Service Distribution Pattern Prediction Application Service Using Big Data
JO - Journal of Knowledge Information Technology and Systems
PY - 2018
VL - 13
IS - 4
PB - Korea Knowledge Information Technology Society
SP - 451
EP - 458
SN - 1975-7700
AB - There are more companies and start-ups providing recommended services to consumers by predicting and analyzing the distribution patterns under circumstances through big data of various distribution fields. This paper is to use the distribution big data of clothing to develop the distribution pattern prediction service. As the actual distribution data of clothing distributors are used for product data, import and export data, and import date data, the accuracy of distribution pattern of clothing may be enhanced. After filtering the actual distribution data of clothing distributors under items, months, and seasons by using the Hadoop-based R, the most distributed items ranked from one to ten are shown on the Android-based application. The application was developed to arrange and filter the data by category to indicate the useful data for retailers of clothing. Therefore, through the application developed, difficult data flows and distribution are easily accessible. The distribution pattern prediction application service developed in this paper shows the result of prediction of the clothing distribution patterns by circumstances (month or season) to wholesalers and retailers of clothing within the scope of data by current clothing distributors to assist the smooth distribution service.
KW - Big data;Recommendation system;Hadoop;Clothing distribution analysis;Clothing recommendation system;Android application
DO - 10.34163/jkits.2018.13.4.005
ER -
YOONJIHAE and Kang Sun-Kyung. (2018). Development of Apparel Service Distribution Pattern Prediction Application Service Using Big Data. Journal of Knowledge Information Technology and Systems, 13(4), 451-458.
YOONJIHAE and Kang Sun-Kyung. 2018, "Development of Apparel Service Distribution Pattern Prediction Application Service Using Big Data", Journal of Knowledge Information Technology and Systems, vol.13, no.4 pp.451-458. Available from: doi:10.34163/jkits.2018.13.4.005
YOONJIHAE, Kang Sun-Kyung "Development of Apparel Service Distribution Pattern Prediction Application Service Using Big Data" Journal of Knowledge Information Technology and Systems 13.4 pp.451-458 (2018) : 451.
YOONJIHAE, Kang Sun-Kyung. Development of Apparel Service Distribution Pattern Prediction Application Service Using Big Data. 2018; 13(4), 451-458. Available from: doi:10.34163/jkits.2018.13.4.005
YOONJIHAE and Kang Sun-Kyung. "Development of Apparel Service Distribution Pattern Prediction Application Service Using Big Data" Journal of Knowledge Information Technology and Systems 13, no.4 (2018) : 451-458.doi: 10.34163/jkits.2018.13.4.005
YOONJIHAE; Kang Sun-Kyung. Development of Apparel Service Distribution Pattern Prediction Application Service Using Big Data. Journal of Knowledge Information Technology and Systems, 13(4), 451-458. doi: 10.34163/jkits.2018.13.4.005
YOONJIHAE; Kang Sun-Kyung. Development of Apparel Service Distribution Pattern Prediction Application Service Using Big Data. Journal of Knowledge Information Technology and Systems. 2018; 13(4) 451-458. doi: 10.34163/jkits.2018.13.4.005
YOONJIHAE, Kang Sun-Kyung. Development of Apparel Service Distribution Pattern Prediction Application Service Using Big Data. 2018; 13(4), 451-458. Available from: doi:10.34163/jkits.2018.13.4.005
YOONJIHAE and Kang Sun-Kyung. "Development of Apparel Service Distribution Pattern Prediction Application Service Using Big Data" Journal of Knowledge Information Technology and Systems 13, no.4 (2018) : 451-458.doi: 10.34163/jkits.2018.13.4.005
