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CEWE : A Python Summary Statistics Tool for Massive Data Analysis. / Oude Nijhuis, Albert; Krasnov, Oleg; Unal, Christine; Russchenberg, Herman; Yarovoy, Alexander.

2017. 1 Abstract from 97th American Meteorological Society Annual Meeting, Seattle, United States.

Research output: Contribution to conferenceAbstractScientific

Harvard

Oude Nijhuis, A, Krasnov, O, Unal, C, Russchenberg, H & Yarovoy, A 2017, 'CEWE: A Python Summary Statistics Tool for Massive Data Analysis' 97th American Meteorological Society Annual Meeting, Seattle, United States, 21/01/17 - 26/01/17, pp. 1.

APA

Oude Nijhuis, A., Krasnov, O., Unal, C., Russchenberg, H., & Yarovoy, A. (2017). CEWE: A Python Summary Statistics Tool for Massive Data Analysis. 1. Abstract from 97th American Meteorological Society Annual Meeting, Seattle, United States.

Vancouver

Oude Nijhuis A, Krasnov O, Unal C, Russchenberg H, Yarovoy A. CEWE: A Python Summary Statistics Tool for Massive Data Analysis. 2017. Abstract from 97th American Meteorological Society Annual Meeting, Seattle, United States.

Author

BibTeX

@conference{a65a2f0e674149cca08dac649cd4e3a4,
title = "CEWE: A Python Summary Statistics Tool for Massive Data Analysis",
abstract = "In this presentation CEWE is introduced, which is a novel open source Python module with the aim of processing summary statistics for massive data sets. It is based on the addition of raw moments and a mixed moment from which regular statistics can be calculated. The novelties of the CEWE tool in comparison with other massive data analysis tools are (1) that it is compatible with circular data; (2) there is a minimalistic worked example available that can be used as a starting for a new project and (3) there is an on-line worked example, TARA CEWE. The tool provides a fast way to explore and validate the data and gives the opportunity to discover correlations between variables. In the on-line example TARA CEWE, observables from a precipitation profiling radar are compared to an extensive list of meteorological parameters from the meteorological supersite in Cabauw, the Netherlands.",
author = "{Oude Nijhuis}, Albert and Oleg Krasnov and Christine Unal and Herman Russchenberg and Alexander Yarovoy",
year = "2017",
month = "1",
language = "English",
pages = "1",
note = "97th American Meteorological Society Annual Meeting : At-A-Glance ; Conference date: 21-01-2017 Through 26-01-2017",
url = "https://ams.confex.com/ams/97Annual/webprogram/",

}

RIS

TY - CONF

T1 - CEWE

T2 - A Python Summary Statistics Tool for Massive Data Analysis

AU - Oude Nijhuis, Albert

AU - Krasnov, Oleg

AU - Unal, Christine

AU - Russchenberg, Herman

AU - Yarovoy, Alexander

PY - 2017/1

Y1 - 2017/1

N2 - In this presentation CEWE is introduced, which is a novel open source Python module with the aim of processing summary statistics for massive data sets. It is based on the addition of raw moments and a mixed moment from which regular statistics can be calculated. The novelties of the CEWE tool in comparison with other massive data analysis tools are (1) that it is compatible with circular data; (2) there is a minimalistic worked example available that can be used as a starting for a new project and (3) there is an on-line worked example, TARA CEWE. The tool provides a fast way to explore and validate the data and gives the opportunity to discover correlations between variables. In the on-line example TARA CEWE, observables from a precipitation profiling radar are compared to an extensive list of meteorological parameters from the meteorological supersite in Cabauw, the Netherlands.

AB - In this presentation CEWE is introduced, which is a novel open source Python module with the aim of processing summary statistics for massive data sets. It is based on the addition of raw moments and a mixed moment from which regular statistics can be calculated. The novelties of the CEWE tool in comparison with other massive data analysis tools are (1) that it is compatible with circular data; (2) there is a minimalistic worked example available that can be used as a starting for a new project and (3) there is an on-line worked example, TARA CEWE. The tool provides a fast way to explore and validate the data and gives the opportunity to discover correlations between variables. In the on-line example TARA CEWE, observables from a precipitation profiling radar are compared to an extensive list of meteorological parameters from the meteorological supersite in Cabauw, the Netherlands.

UR - https://ams.confex.com/ams/97Annual/webprogram/Paper311283.html

M3 - Abstract

SP - 1

ER -

ID: 31196492