Facilitating Twitter data analytics: Platform, language and functionality

Ke Tao; Claudia Hauff; Geert Jan Houben; Fabian Abel; Guido Wachsmuth

doi:10.1109/BigData.2014.7004259

Facilitating Twitter data analytics: Platform, language and functionality

Ke Tao^*, Claudia Hauff, Geert Jan Houben, Fabian Abel, Guido Wachsmuth

^*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

9 Citations (Scopus)

Abstract

Conducting analytics over data generated by Social Web portals such as Twitter is challenging, due to the volume, variety and velocity of the data. Commonly, adhoc pipelines are used that solve a particular use case. In this paper, we generalize across a range of typical Twitter-data use cases and determine a set of common characteristics. Based on this investigation, we present our Twitter Analytical Platform (TAP), a generic platform for conducting analytical tasks with Twitter data. The platform provides a domain-specific Twitter Analysis Language (TAL) as the interface to its functionality stack. TAL includes a set of analysis tools ranging from data collection and semantic enrichment, to machine learning. With these tools, it becomes possible to create and customize analytical workflows in TAL and build applications that make use of the analytics results. We showcase the applicability of our platform by building Twinder-a search engine for Twitter streams.

Original language	English
Title of host publication	Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	421-430
Number of pages	10
ISBN (Electronic)	9781479956654
DOIs	https://doi.org/10.1109/BigData.2014.7004259
Publication status	Published - 7 Jan 2015
Event	2nd IEEE International Conference on Big Data, IEEE Big Data 2014 - Washington, United States Duration: 27 Oct 2014 → 30 Oct 2014

Conference

Conference	2nd IEEE International Conference on Big Data, IEEE Big Data 2014
Country/Territory	United States
City	Washington
Period	27/10/14 → 30/10/14

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10.1109/BigData.2014.7004259

Cite this

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title = "Facilitating Twitter data analytics: Platform, language and functionality",

abstract = "Conducting analytics over data generated by Social Web portals such as Twitter is challenging, due to the volume, variety and velocity of the data. Commonly, adhoc pipelines are used that solve a particular use case. In this paper, we generalize across a range of typical Twitter-data use cases and determine a set of common characteristics. Based on this investigation, we present our Twitter Analytical Platform (TAP), a generic platform for conducting analytical tasks with Twitter data. The platform provides a domain-specific Twitter Analysis Language (TAL) as the interface to its functionality stack. TAL includes a set of analysis tools ranging from data collection and semantic enrichment, to machine learning. With these tools, it becomes possible to create and customize analytical workflows in TAL and build applications that make use of the analytics results. We showcase the applicability of our platform by building Twinder-a search engine for Twitter streams.",

author = "Ke Tao and Claudia Hauff and Houben, {Geert Jan} and Fabian Abel and Guido Wachsmuth",

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booktitle = "Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "2nd IEEE International Conference on Big Data, IEEE Big Data 2014 ; Conference date: 27-10-2014 Through 30-10-2014",

}

Tao, K, Hauff, C, Houben, GJ, Abel, F & Wachsmuth, G 2015, Facilitating Twitter data analytics: Platform, language and functionality. in Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014., 7004259, Institute of Electrical and Electronics Engineers (IEEE), pp. 421-430, 2nd IEEE International Conference on Big Data, IEEE Big Data 2014, Washington, United States, 27/10/14. https://doi.org/10.1109/BigData.2014.7004259

Facilitating Twitter data analytics: Platform, language and functionality. / Tao, Ke; Hauff, Claudia; Houben, Geert Jan et al.
Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014. Institute of Electrical and Electronics Engineers (IEEE), 2015. p. 421-430 7004259.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - Tao, Ke

AU - Hauff, Claudia

AU - Houben, Geert Jan

AU - Abel, Fabian

AU - Wachsmuth, Guido

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AB - Conducting analytics over data generated by Social Web portals such as Twitter is challenging, due to the volume, variety and velocity of the data. Commonly, adhoc pipelines are used that solve a particular use case. In this paper, we generalize across a range of typical Twitter-data use cases and determine a set of common characteristics. Based on this investigation, we present our Twitter Analytical Platform (TAP), a generic platform for conducting analytical tasks with Twitter data. The platform provides a domain-specific Twitter Analysis Language (TAL) as the interface to its functionality stack. TAL includes a set of analysis tools ranging from data collection and semantic enrichment, to machine learning. With these tools, it becomes possible to create and customize analytical workflows in TAL and build applications that make use of the analytics results. We showcase the applicability of our platform by building Twinder-a search engine for Twitter streams.

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Facilitating Twitter data analytics: Platform, language and functionality

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