Wading through the swamp: filter systems for geospatial data science

Adrian Tear; Richard Healey

Wading through the swamp: filter systems for geospatial data science

Research output: Contribution to conference › Paper › peer-review

Abstract

Data Scientists write code, understand statistics and derive insights from data. Commonly used technologies include Relational Database Management Systems and Structured Query Language, ‘Big Data’ ecosystems such as Hadoop, R, Java, Python or similar code bases, and; Natural Language Processing software used to text-mine large un-/semi-/structured datasets. Data Scientists must also deploy complex software environments built on (or across) various physical, virtual and/or cloud computing infrastructures. This paper outlines the development of one such system designed to comprehensively analyse >8 million Twitter/Facebook social media posts.

Original language	English
Publication status	Published - 18 Apr 2017
Event	The GIS Research UK 25th Annual Conference - Manchester, United Kingdom Duration: 18 Apr 2017 → 21 Apr 2017 http://manchester.gisruk.org/proceedings.php

Conference

Conference	The GIS Research UK 25th Annual Conference
Abbreviated title	GISRUK2017
Country/Territory	United Kingdom
City	Manchester
Period	18/04/17 → 21/04/17
Internet address	http://manchester.gisruk.org/proceedings.php

Keywords

data science
big data
social media
text/data-mining
analytics

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T1 - Wading through the swamp: filter systems for geospatial data science

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AU - Healey, Richard

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N2 - Data Scientists write code, understand statistics and derive insights from data. Commonly used technologies include Relational Database Management Systems and Structured Query Language, ‘Big Data’ ecosystems such as Hadoop, R, Java, Python or similar code bases, and; Natural Language Processing software used to text-mine large un-/semi-/structured datasets. Data Scientists must also deploy complex software environments built on (or across) various physical, virtual and/or cloud computing infrastructures. This paper outlines the development of one such system designed to comprehensively analyse >8 million Twitter/Facebook social media posts.

AB - Data Scientists write code, understand statistics and derive insights from data. Commonly used technologies include Relational Database Management Systems and Structured Query Language, ‘Big Data’ ecosystems such as Hadoop, R, Java, Python or similar code bases, and; Natural Language Processing software used to text-mine large un-/semi-/structured datasets. Data Scientists must also deploy complex software environments built on (or across) various physical, virtual and/or cloud computing infrastructures. This paper outlines the development of one such system designed to comprehensively analyse >8 million Twitter/Facebook social media posts.

KW - data science

KW - big data

KW - social media

KW - text/data-mining

KW - analytics

M3 - Paper

T2 - The GIS Research UK 25th Annual Conference

Y2 - 18 April 2017 through 21 April 2017

ER -

Wading through the swamp: filter systems for geospatial data science

Abstract

Conference

Keywords

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Cite this