@inproceedings{1baf4d42ecf54e688df5002aeefa3aa6,
title = "Distributed PARAFAC Decomposition Method Based on In-memory Big Data System",
abstract = "We propose IM-PARAFAC, a PARAFAC tensor decomposition method that enables rapid processing of large scalable tensors in Apache Spark for distributed in-memory big data management systems. We consider the memory overflow that occurs when processing large amounts of data because of running on in-memory. Therefore, the proposed method, IM-PARAFAC, is capable of dividing and decomposing large input tensors. It can handle large tensors even in small, distributed environments. The experimental results indicate that the proposed IM-PARAFAC enables handling of large tensors and reduces the execution time.",
keywords = "Apache Spark, Distributed PARAFAC decomposition",
author = "Yang, {Hye Kyung} and Yong, {Hwan Seung}",
note = "Funding Information: Acknowledgement. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1B03931529). Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; null ; Conference date: 22-04-2019 Through 25-04-2019",
year = "2019",
doi = "10.1007/978-3-030-18590-9_31",
language = "English",
isbn = "9783030185893",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "292--295",
editor = "Guoliang Li and Yongxin Tong and Juggapong Natwichai and Joao Gama and Jun Yang",
booktitle = "Database Systems for Advanced Applications - DASFAA 2019 International Workshops",
}