Acknowledgement
This research is supported by the U.S. Department of Energy, Office of Environmental Management (EM) MSIPP program under TOA #T0000456309.
References
- M.K. Saggi, S. Jain, A survey towards an integration of big data analytics to big insights for value-creation, Inf. Process. Manag. 54 (2018) 758-790, https://doi.org/10.1016/j.ipm.2018.01.010.
- C.W. Tsai, C.F. Lai, H.C. Chao, A.V. Vasilakos, Big data analytics: a survey, J. Big Data 2 (2015) 1-32, https://doi.org/10.1186/s40537-015-0030-3.
- J.S. Dhoble, N. Shelke, Investigative research on big data: an analysis, available at, Int. J. Innov. Res. Sci. Eng. Technol. 4 (2015) 4476-4482. :, 06/13/2022, http://www.ijirset.com/upload/2015/june/58_12_Investigative.pdf.
- C. Ma, H.H. Zhang, X. Wang, Machine learning for big data analytics in plants, Trends Plant Sci. 19 (2014) 798-808, https://doi.org/10.1016/j.tplants.2014.08.004.
- J. Han, M. Kamber, J. Pei, Data Mining: Concepts and Techniques, Morgan Kaufmann Waltham, MA, 2012.
- A.K. Jain, M.N. Murty, P.J. Flynn, Data clustering: a review, ACM Comput. Surv. 31 (1999) 264-323, https://doi.org/10.1145/331499.331504.
- R. Xu, D. Wunsch, Survey of clustering algorithms, IEEE Trans. Neural Network. 16 (2005) 645-678, https://doi.org/10.1109/TNN.2005.845141.
- K.E. Zeigler, B.A. Ferguson, Development of an in-situ decommissioning sensor network test bed for structural condition monitoring, in: Waste Management 2012 Conference, Phoenix, AZ, USA, Feb 26 - March 1, 2012.
- K. Zeigler, B. Ferguson, D. Karapatakis, C. Herbst, C. Stripling, Development of a Sensor Network Test Bed for ISD Materials and Structural Condition Monitoring, Savannah River National Laboratory (SRNL), 2011, https://doi.org/10.2172/1018717. SRNL-STI-2011-00193.
- Z. Sun, A. Duncan, Y. Kim, K. Zeigler, Applying temporal data mining (TDM) on the baseline data acquired by the in-situ decommissioning (ISD) sensor network test bed, in: Waste Management 2018 Conference, Phoenix, AZ, USA, Mar 18-22, 2018.
- X. Ao, P. Luo, C. Li, F. Zhuang and Q. He, Online frequent episode mining, in: 2015 IEEE 31st International Conference on Data Engineering, pp. 891-902, https://doi.org/10.1109/ICDE.2015.7113342.
- P.S. Sastry, S. Laxman, K.P. Unnikrishnan, System and Method for Mining of Temporal Data, 2010 patent 7644078.
- D. Patnaik, S. Laxman, B. Chandramouli, N. Ramakrishnan, Efficient episode mining of dynamic event streams, Data Mining (ICDM), in: IEEE 12th International Conference, 2012, pp. 605-614, https://doi.org/10.1109/ICDM.2012.84.
- D. Patnaik, P. Sastry, K. Unnikrishnan, Inferring neuronal network connectivity from spike data: a temporal data mining approach, Sci. Program. 16 (2018) 49-77, https://doi.org/10.3233/SPR-2008-0242.
- S. Laxman, P.S. Sastry, K.P. Unnikrishnan, A fast algorithm for finding frequent episodes in event streams, in: KDD '07: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017, pp. 410-419, https://doi.org/10.1145/1281192.1281238.
- X. Ao, H. Shi, J. Wang, L. Zuo, H. Li, Q. He, Large-scale frequent episode mining from complex event sequences with hierarchies, ACM Trans. Intell. Syst. Technol. 10 (2019) 1-26, https://doi.org/10.1145/3326163.
- P. Fournier-Viger, P. Yang, J. Lin, U. Yun, HUE-span: fast high utility episode mining, in: 15th International Conference on Advanced Data Mining and Applications (ADMA 2019), Dalian, China, Nov 21-23, 2019, https://doi.org/10.1007/978-3-030-35231-8_12.
- M. Amiri, L. Mohammad-Khanli, R. Mirandola, An online learning model based on episode mining for workload prediction in cloud, Future Generat. Comput. Syst. 87 (2018) 83-101, https://doi.org/10.1016/j.future.2018.04.044.
- T. You, Y. Li, B. Sun, C. Du, Multi-source data stream online frequent episode mining, IEEE Access 8 (2020) 107465-107478, https://doi.org/10.1109/ACCESS.2020.2997337.
- B. Biswal, A. Duncan, Z. Sun, Applying advanced data analytics methods to the baseline data of ISD sensor network testbed for system failure detection, in: Waste Management 2021 Conference, Phoenix, AZ, USA, Mar 8-12, 2021.
- Y. Zhou, Y. Zhu, Q. Ye, Q. Qiu, J. Jiao, Weakly supervised instance segmentation using class peak response, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 3791-3800, https://doi.org/10.1109/CVPR.2018.00399.
- B. Biswal, S. Shetty, T. Rogers, Enhanced learning classifier to locate data in cloud data centres, Int. J. Metaheuristics (IJMHeur) 4 (2015) 141-158, https://doi.org/10.1504/IJMHEUR.2015.074248.
- R. Suzuki, H. Shimodaira, Pvclust: an R package for assessing the uncertainty in hierarchical clustering, J. Bioinfo. 22 (2006) 1540-1542, https://doi.org/10.1093/bioinformatics/btl117.
- C. Negin, C. Urland, A. Szilagyi, DOE-EM'S in-situ decommissioning strategy, in: Waste Management 2021 Conference, Phoenix, AZ, USA, Feb 24-28, 2008.
- N. Carino, V. Li, G. Heath, G. Song, C. Wang, P. Ziehl, Development of a Remote Monitoring Sensor Network for in Situ Decommissioned Structures, Savannah River National Laboratory (SRNL), 2010. SRNL-RP-2010-01666.
- S. Hansun, A new approach of Brown's double exponential smoothing method in time series analysis, Balkan J. Electr. Comput. Eng. (2016) 75-78, https://doi.org/10.17694/bajece.14351.
- R. Benjamin, B. Pierre, T. Nicolas, G. Paulo, V. Pierre, Fourier could be a data scientist: from graph Fourier transform to signal processing on graphs, Compt. Rendus Phys. 20 (2019) 474-488, https://doi.org/10.1016/j.crhy.2019.08.003.
- S. Taixin, M. Yang, T. Jin, R.C.C. Flesch, Power harmonic and interharmonic detection method in renewable power based on Nuttall double-window all-phase FFT algorithm, IET Renew. Power Gener. 12 (2018) 953-961, https://doi.org/10.1049/iet-rpg.2017.0115.
- N. Samad, P. Hamid, F. Eshagh, Using sub-sampling and ensemble clustering techniques to improve performance of imbalanced classification, Neurocomputing 276 (2018) 55-66, https://doi.org/10.1016/j.neucom.2017.06.082.
- J. Zhang, I. Mani, kNN approach to unbalanced data distributions: a case study involving information extraction, available at, in: Proceeding of International Conference on Machine Learning (ICML 2003), Washington DC, Aug 21, 2003. :, 06/13/2022, https://www.site.uottawa.ca/~nat/Workshop2003/jzhang.pdf.