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Statistical Outlier Curation Kernel Software (SOCKS): A Modern, Efficient Outlier Detection and Curation Suite
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  • Prasanta Pal ,
  • Remko Van Lutterveld ,
  • Nancy Quirós ,
  • Veronique Taylor ,
  • Judson Brewer
Prasanta Pal
Brown University, Brown University

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Remko Van Lutterveld
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Nancy Quirós
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Veronique Taylor
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Judson Brewer
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Abstract

Real world signal acquisition through sensors, is at the heart of modern digital revolution. However, almost every signal acquisition systems are contaminated with noise and outliers. Precise detec- tion, and curation of data is an essential step to reveal the true-nature of the uncorrupted observations. With the exploding volumes of digital data sources, there is a critical need for a robust but easy-to-operate, low-latency, generic yet highly customizable, outlier- detection and curation tool, easily accessible, adaptable to diverse types of data sources. Existing methods often boil down to data smoothing that inherently cause valuable information loss. We have developed a C++ based, software tool to decontaminate time- series and matrix like data sources, with the goal of recovering the ground-truth. The SOCKS tool would be made available as an open-source software for broader adoption in the scientific community. Our work calls for a philosophical shift in the design pipelines of real- world data processing. We propose, raw data should be decontaminated first, through conditional flagging of outliers, curation of flagged points, followed by iterative, parametrically tuned, asymptotic converge to the ground-truth as accurately as possible, before performing traditional data processing tasks.