Retaining data from streams of social platforms with minimal regret

Tam, Nguyen Thanh, Weidlich, Matthias, Thang, Duong Chi, Yin, Hongzhi and Hung, Nguyen Quoc Viet (2017). Retaining data from streams of social platforms with minimal regret. In: Carles Sierra, The Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017. International Joint Conference on Artificial Intelligence, IJCAI, Melbourne, Australia, (2850-2856). 19-25 August 2017.

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Author Tam, Nguyen Thanh
Weidlich, Matthias
Thang, Duong Chi
Yin, Hongzhi
Hung, Nguyen Quoc Viet
Title of paper Retaining data from streams of social platforms with minimal regret
Conference name International Joint Conference on Artificial Intelligence, IJCAI
Conference location Melbourne, Australia
Conference dates 19-25 August 2017
Proceedings title The Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017   Check publisher's open access policy
Journal name IJCAI International Joint Conference on Artificial Intelligence   Check publisher's open access policy
Series IJCAI International Joint Conference on Artificial Intelligence
Place of Publication Melbourne, Australia
Publisher International Joint Conferences on Artificial Intelligence
Publication Year 2017
Sub-type Fully published paper
Open Access Status Not yet assessed
ISBN 9780999241103
ISSN 1045-0823
Editor Carles Sierra
Start page 2850
End page 2856
Total pages 7
Language eng
Abstract/Summary Today's social platforms, such as Twitter and Facebook, continuously generate massive volumes of data. The resulting data streams exceed any reasonable limit for permanent storage, especially since data is often redundant, overlapping, sparse, and generally of low value. This calls for means to retain solely a small fraction of the data in an online manner. In this paper, we propose techniques to effectively decide which data to retain, such that the induced loss of information, the regret of neglecting certain data, is minimized. These techniques enable not only efficient processing of massive streaming data, but are also adaptive and address the dynamic nature of social media. Experiments on large-scale real-world datasets illustrate the feasibility of our approach in terms of both, runtime and information quality.
Subjects 1702 Artificial Intelligence
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ
Additional Notes https://www.ijcai.org/proceedings/2017/

Document type: Conference Paper
Sub-type: Fully published paper
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
 
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Created: Thu, 14 Sep 2017, 22:56:59 EST by Hongzhi Yin on behalf of School of Information Technol and Elec Engineering