Abstract
In this study, we propose a novel data analysis approach that can be used for multi-view analysis and integration of heterogeneous temporal data originating from multiple sources. The proposed approach consists of several distinctive layers: (i) select a suitable set (view) of parameters in order to identify characteristic behaviour within each individual source (ii) exploit an alternative set (view) of raw parameters (or high-level features) to derive some complementary representations (e.g. related to source performance) of the results obtained in the first layer with the aim to facilitate comparison and mediation across the different sources (iii) integrate those representations in an appropriate way, allowing to trace back similar cross-source performance to certain characteristic behaviour of the individual sources.
The validity and the potential of the proposed approach has been demonstrated on a real-world dataset of a fleet of wind turbines.
This research was subsidised by the Brussels-Capital Region - Innoviris, received funding from the Flemish Government (AI Research Program) and was supported by the Energy Transition Fund of the FPS Economy through the project BitWind.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Supervisory control and data acquisition (SCADA) is an architecture to control industrial systems by use of both external and internal sensors (sources).
References
Bickel, S., Scheffer, T.: Multi-view clustering. In: ICDMM, vol. 4, pp. 19–26 (2004)
Boeva, V., Tsiporkova, E., Kostadinova, E.: Analysis of multiple DNA microarray datasets. In: Kasabov, N. (ed.) Springer Handbook of Bio-/Neuroinformatics, pp. 223–234. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-30574-0_14
Caliński, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. Theory Methods 3(1), 1–27 (1974)
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 2, 224–227 (1979)
Deepak, P., Jurek-Loughrey, A. (eds.): Linking and Mining Heterogeneous and Multi-view Data. USL. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-01872-6
Dong, X.L., Srivastava, D.: Big data integration. In: 2013 IEEE 29th International Conference on Data Engineering (ICDE), pp. 1245–1248. IEEE (2013)
Gamberger, D., Mihelčić, M., Lavrač, N.: Multilayer clustering: a discovery experiment on country level trading data. In: Džeroski, S., Panov, P., Kocev, D., Todorovski, L. (eds.) DS 2014. LNCS (LNAI), vol. 8777, pp. 87–98. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11812-3_8
Handl, J., Knowles, J., Kell, D.B.: Computational cluster validation in post-genomic data analysis. Bioinformatics 21(15), 3201–3212 (2005)
Liu, Z., et al.: A method of SVM with normalization in intrusion detection. Procedia Environ. Sci. 11, 256–262 (2011)
MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Statistics and Probability (1967)
Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)
Sheather, S.J.: Density estimation. Stat. Sci. 19, 588–597 (2004)
Silverman, B.W.: Density Estimation for Statistics and Data Analysis, vol. 26. CRC Press, Boca Raton (1986)
Xu, C., Tao, D., Xu, C.: A survey on multi-view learning. arXiv preprint arXiv:1304.5634 (2013)
Zhang, J.: Multi-source remote sensing data fusion: status and trends. Int. J. Image Data Fusion 1(1), 5–24 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Dhont, M., Tsiporkova, E., Boeva, V. (2020). Layered Integration Approach for Multi-view Analysis of Temporal Data. In: Lemaire, V., Malinowski, S., Bagnall, A., Guyet, T., Tavenard, R., Ifrim, G. (eds) Advanced Analytics and Learning on Temporal Data. AALTD 2020. Lecture Notes in Computer Science(), vol 12588. Springer, Cham. https://doi.org/10.1007/978-3-030-65742-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-65742-0_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-65741-3
Online ISBN: 978-3-030-65742-0
eBook Packages: Computer ScienceComputer Science (R0)
