TY - GEN
T1 - Will Deep Learning Change How Teams Execute Big Data Projects?
AU - Shamshurin, Ivan
AU - Saltz, Jeffrey
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - As data continues to be produced in ever increasing quantities, and technologies such as high performance computing continue to be enhanced, the number of big data projects using advanced neural network machine learning, often referred to as deep learning, continues to increase. Unfortunately, while much has been written on the use of deep learning algorithms in terms of generating insightful analysis, much less has been written about the project management process methodologies that could enable teams to more effectively and efficiently »do» big data deep learning projects. Specifically, the rapid growth in the use of deep learning techniques might introduce new challenges with respect to how to execute a big data deep learning project, due to how deep learning models can learn features automatically. For example, feature engineering and model evaluation phases of big data projects might grow in importance, while other areas, such as model selection, might decrease in importance. Hence, this paper discusses the key research questions relating the potential impact of the use of deep learning on how teams should execute big data projects.
AB - As data continues to be produced in ever increasing quantities, and technologies such as high performance computing continue to be enhanced, the number of big data projects using advanced neural network machine learning, often referred to as deep learning, continues to increase. Unfortunately, while much has been written on the use of deep learning algorithms in terms of generating insightful analysis, much less has been written about the project management process methodologies that could enable teams to more effectively and efficiently »do» big data deep learning projects. Specifically, the rapid growth in the use of deep learning techniques might introduce new challenges with respect to how to execute a big data deep learning project, due to how deep learning models can learn features automatically. For example, feature engineering and model evaluation phases of big data projects might grow in importance, while other areas, such as model selection, might decrease in importance. Hence, this paper discusses the key research questions relating the potential impact of the use of deep learning on how teams should execute big data projects.
KW - deep learning
KW - methodology
KW - neural networks
UR - http://www.scopus.com/inward/record.url?scp=85062643447&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062643447&partnerID=8YFLogxK
U2 - 10.1109/BigData.2018.8622337
DO - 10.1109/BigData.2018.8622337
M3 - Conference contribution
AN - SCOPUS:85062643447
T3 - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
SP - 2813
EP - 2817
BT - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
A2 - Abe, Naoki
A2 - Liu, Huan
A2 - Pu, Calton
A2 - Hu, Xiaohua
A2 - Ahmed, Nesreen
A2 - Qiao, Mu
A2 - Song, Yang
A2 - Kossmann, Donald
A2 - Liu, Bing
A2 - Lee, Kisung
A2 - Tang, Jiliang
A2 - He, Jingrui
A2 - Saltz, Jeffrey
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Big Data, Big Data 2018
Y2 - 10 December 2018 through 13 December 2018
ER -