A Low Cost and Highly Accurate Technique for Big Data Spatial-Temporal Interpolation

Mohsen Esmaeilbeigi, Omid Chatrabgoun, Amin Hosseinian-Far, Alireza Daneshkhah, Reza Montasari

Research output: Contribution to JournalArticle

Abstract

The high velocity, variety and volume of data generation by today's systems have necessitated Big Data (BD) analytic techniques. This has penetrated a wide range of industries; BD as a notion has various types and characteristics, and therefore a variety of analytic techniques would be required. The traditional analysis methods are typically unable to analyse spatial-temporal BD. Interpolation is required to approximate the values between the already exiting data points, yet since there exist both location and time dimensions, only a multivariate interpolation would be appropriate. Nevertheless, existing software are unable to perform such complex interpolations. To overcome this challenge, this paper presents a layer by layer interpolation approach for spatial-temporal BD. Developing this layered structure provides the opportunity for working with much smaller linear system of equations. Consequently, this structure increases the accuracy and stability
of numerical structure of the considered BD interpolation. To construct this layer by layer interpolation, we have used the good properties of Radial Basis Functions (RBFs). The proposed new approach is applied to numerical examples in spatial-temporal big data and the obtained results conrm the high accuracy and low computational cost. Finally, our approach is applied to explore one of the air pollution indices, i.e. daily PM2:5 concentration, based on dierent stations in the contiguous United States, and it is evaluated by leave-one-out cross validation.
Original languageEnglish
JournalApplied Numerical Mathematics
Publication statusAccepted/In press - 11 Mar 2020

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Esmaeilbeigi, M., Chatrabgoun, O., Hosseinian-Far, A., Daneshkhah, A., & Montasari, R. (Accepted/In press). A Low Cost and Highly Accurate Technique for Big Data Spatial-Temporal Interpolation. Applied Numerical Mathematics.