TY - GEN
T1 - Swarm intelligence in big data analytics
AU - Cheng, Shi
AU - Shi, Yuhui
AU - Qin, Quande
AU - Bai, Ruibin
N1 - Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - This paper analyses the difficulty of big data analytics problems and the potential of swarm intelligence solving big data analytics problems. Nowadays, the big data analytics has attracted more and more attentions, which is required to manage immense amounts of data quickly. However, current researches mainly focus on the amount of data. In this paper, the other three properties of big data analytics, which include the high dimensionality of data, the dynamical change of data, and the multi-objective of problems, are discussed. Swarm intelligence, which works with a population of individuals, is a collection of nature-inspired searching techniques. It has effectively solved many large-scale, dynamical, and multi-objective problems. Based on the combination of swarm intelligence and data mining techniques, we can have better understanding of the big data analytics problems, and designing more effective algorithms to solve real-world big data analytics problems.
AB - This paper analyses the difficulty of big data analytics problems and the potential of swarm intelligence solving big data analytics problems. Nowadays, the big data analytics has attracted more and more attentions, which is required to manage immense amounts of data quickly. However, current researches mainly focus on the amount of data. In this paper, the other three properties of big data analytics, which include the high dimensionality of data, the dynamical change of data, and the multi-objective of problems, are discussed. Swarm intelligence, which works with a population of individuals, is a collection of nature-inspired searching techniques. It has effectively solved many large-scale, dynamical, and multi-objective problems. Based on the combination of swarm intelligence and data mining techniques, we can have better understanding of the big data analytics problems, and designing more effective algorithms to solve real-world big data analytics problems.
UR - http://www.scopus.com/inward/record.url?scp=84890891734&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-41278-3_51
DO - 10.1007/978-3-642-41278-3_51
M3 - Conference contribution
AN - SCOPUS:84890891734
SN - 9783642412776
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 417
EP - 426
BT - Intelligent Data Engineering and Automated Learning - 14th International Conference, IDEAL 2013, Proceedings
PB - Springer Verlag
T2 - 14th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2013
Y2 - 20 October 2013 through 23 October 2013
ER -