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Open Access Publications from the University of California

Understanding Data Similarity in Large-Scale Scientific Datasets

  • Author(s): Linton, P;
  • Melodia, W;
  • Lazar, A;
  • Agarwal, D;
  • Bianchi, L;
  • Ghoshal, D;
  • Pastorello, G;
  • Ramakrishnan, L;
  • Wu, K
  • Editor(s): Baru, Chaitanya;
  • Huan, Jun;
  • Khan, Latifur;
  • Hu, Xiaohua;
  • Ak, Ronay;
  • Tian, Yuanyuan;
  • Barga, Roger S;
  • Zaniolo, Carlo;
  • Lee, Kisung;
  • Ye, Yanfang Fanny
  • et al.
Abstract

Today, scientific experiments and simulations produce massive amounts of heterogeneous data that need to be stored and analyzed. Given that these large datasets are stored in many files, formats and locations, how can scientists find relevant data, duplicates or similarities? In this context, we concentrate on developing algorithms to compare similarity of time series for the purpose of search, classification and clustering. For example, generating accurate patterns from climate related time series is important not only for building models for weather forecasting and climate prediction, but also for modeling and predicting the cycle of carbon, water, and energy. We developed the methodology and ran an exploratory analysis of climatic and ecosystem variables from the FLUXNET2015 dataset. The proposed combination of similarity metrics, nonlinear dimension reduction, clustering methods and validity measures for time series data has never been applied to unlabeled datasets before, and provides a process that can be easily extended to other scientific time series data. The dimensionality reduction step provides a good way to identify the optimum number of clusters, detect outliers and assign initial labels to the time series data. We evaluated multiple similarity metrics, in terms of the internal cluster validity for driver as well as response variables. While the best metric often depends on a number of factor, the Euclidean distance seems to perform well for most variables and also in terms of computational expense.

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