Abstract
The upcoming new horizons and recent research trends in Big Data Analytics frameworks, techniques and algorithms are as reflected in research papers recently published in conferences such as ACM International Conference on Knowledge Discovery and Data Mining (ACM SIG KDD), SIAM International Conference on Data Mining (SDM), IEEE International Conference on Data Engineering (ICDE) and ACM International Conference on Information and Knowledge Management (CIKM). In this chapter, we shall survey the research trends and the possible new horizons coming up in Big Data Analytics.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
C. Aggarwal, K. Subbian, Evolutionary network analysis: a survey. ACM Comput. Surv. 47(1):10:1–10:36 (2014)
M. Gupta, J. Gao, C.C. Aggarwal, J. Han, Outlier detection for temporal data: a survey. IEEE Trans. Knowl. Data Eng. 26(9), 2250–2267 (2014)
S. Ranshous, S. Shen, D. Koutra, S. Harenberg, C. Faloutsos, F.N. Samatova, Anomaly detection in dynamic networks: a survey. 7, 223–247 (2015)
I.J. Goodfellow, J. Shlens, C. Szegedy, Explaining and Harnessing Adversarial Examples (2014). ArXiv e-prints
W. Liu, S. Chawla, J. Bailey, C. Leckie, K. Ramamohanarao, AI 2012: advances in Artificial Intelligence: 25th Australasian Joint Conference, Sydney, Australia, 4–7 Dec, 2012, in Proceedings, Chapter An Efficient Adversarial Learning Strategy for Constructing Robust Classification Boundaries (Springer, Berlin, Heidelberg, 2012), pp. 649–660
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. Berkay Celik, A. Swami, Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples (2016). ArXiv e-prints
M. Vidyadhari, K. Kiranmai, K.R. Krishniah, D.S. Babu, Security evaluation of pattern classifiers under attack. Int. J. Res. 3(01), 1043–1048 (2016)
C.C. Aggarwal, Y. Zhao, P.S. Yu, Outlier detection in graph streams, in Proceedings of the 2011 IEEE 27th International Conference on Data Engineering, ICDE’11. IEEE Computer Society Washington, DC, USA, 2011, pp. 399–409
M. Jiang, A. Beutel, P. Cui, B. Hooi, S. Yang, C. Faloutsos, A general suspiciousness metric for dense blocks in multimodal data, in 2015 IEEE International Conference on Data Mining, ICDM 2015, Atlantic City, NJ, USA, 14–17 Nov 2015, pp. 781–786
J. Sun, C. Faloutsos, S. Papadimitriou, P.S. Yu, Graphscope: Parameter-free mining of large time-evolving graphs, in Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’07 (New York, NY, USA. ACM, 2007), pp. 687–696
M. Davis, W. Liu, P. Miller, G. Redpath, Detecting anomalies in graphs with numeric labels, in Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM’11 (ACM, New York, NY, USA, 2011) , pp. 1197–1202
M. Mongiov, P. Bogdanov, R. Ranca, E.E. Papalexakis, C. Faloutsos, A.K. Singh, NetSpot: Spotting Significant Anomalous Regions on Dynamic Networks, Chapter 3, pp. 28–36
M. Gupta, C.C. Aggarwal, J. Han, Y. Sun, Evolutionary clustering and analysis of bibliographic networks, in 2011 International Conference on Advances in Social Net-Works Analysis and Mining (ASONAM), pp. 63–70
J. Chan, N.X. Vinh, W. Liu, J. Bailey, C.A. Leckie, K. Ramamohanarao, J. Pei, Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, 13–16 May 2014, in Proceedings, Part I, chapter Structure-Aware Distance Measures for Comparing Clusterings in Graphs (Springer International Publishing, Cham, 2014) pp. 362–373
F. Wang, W. Liu, S. Chawla, On sparse feature attacks in adversarial learning, in 2014 IEEE International Conference on Data Mining, 2014, pp. 1013–1018
H. Xiao, B. Biggio, B. Nelson, H. Xiao, C. Eckert, F. Roli, Support vector machines under adversarial label contamination. J. Neuro Comput., Spec. Issue Adv. Learn. Label Noise (2014 in press)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Prabhu, C., Chivukula, A., Mogadala, A., Ghosh, R., Livingston, L. (2019). Emerging Research Trends and New Horizons. In: Big Data Analytics: Systems, Algorithms, Applications. Springer, Singapore. https://doi.org/10.1007/978-981-15-0094-7_17
Download citation
DOI: https://doi.org/10.1007/978-981-15-0094-7_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0093-0
Online ISBN: 978-981-15-0094-7
eBook Packages: Computer ScienceComputer Science (R0)