{"type":"ScholarlyArticle","title":"Retaining Data from Streams of Social Platforms with Minimal Regret","authors":[{"name":"Nguyen Thanh Tam","slug":"nguyen-thanh-tam","given":"Nguyen Thanh","family":"Tam","affiliation":"École Polytechnique Fédérale de Lausanne ,"},{"name":"Matthias Weidlich","slug":"matthias-weidlich","given":"Matthias","family":"Weidlich","affiliation":"Humboldt-Universität zu Berlin ,"},{"name":"Duong Chi Thang","slug":"duong-chi-thang","given":"Duong Chi","family":"Thang","affiliation":"École Polytechnique Fédérale de Lausanne ,"},{"name":"Hongzhi Yin","slug":"hongzhi-yin","given":"Hongzhi","family":"Yin","affiliation":"The University of Queensland ,"},{"name":"Nguyen Quoc Viet Hung","slug":"nguyen-quoc-viet-hung","given":"Nguyen Quoc Viet","family":"Hung","affiliation":"Griffith University"}],"abstract":"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.","doi":"10.24963/ijcai.2017/397","url":"https://ijcai.org/proceedings/2017/397","pdf":"https://ijcai.org/proceedings/2017/0397.pdf","markdown":"https://ijcai.org/proceedings/2017/397.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/397","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":397,"pages":{"first":2850,"last":2856},"keywords":["Multidisciplinary Topics and Applications: Multidisciplinary Topics and Applications","Machine Learning: Time-series/Data Streams","Multidisciplinary Topics and Applications: Intelligent Database Systems"],"videos":[],"references":[{"position":1,"raw":"[Alsaedi et al., 2016] Nasser Alsaedi, Peter Burnap, and Omer Fa- rooq Rana. 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