Scalable, Continuous Tracking of Tag Co-occurrences Between Short Sets Using 
(Almost) Disjoint Tag Partitions

Alvanaki, Foteini; Michel, Sebastian

doi:10.1145/2484702.2484705

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Scalable, Continuous Tracking of Tag Co-occurrences Between Short Sets Using (Almost) Disjoint Tag Partitions

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Alvanaki, Foteini
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Michel, Sebastian
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Citation

Alvanaki, F., & Michel, S. (2013). Scalable, Continuous Tracking of Tag Co-occurrences Between Short Sets Using (Almost) Disjoint Tag Partitions. In K. LeFevre, A. Machanavajjhala, & A. Silberstein (Eds.), Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks (pp. 49-54). New York, NY: ACM. doi:10.1145/2484702.2484705.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0015-3A81-D

Abstract

In this work we consider the continuous computation of set correlations over a stream of set-valued attributes, such as Tweets and their hashtags, social annotations of blog posts obtained through RSS, or updates to set-valued attributes of databases. In order to compute tag correlations in a distributed fashion, all necessary information has to be present at the computing node(s). Our approach makes use of a partitioning scheme based on set covers for efficient and replication-lean information flow. We report on the results of a preliminary performance evaluation using Tweets obtained through Twitter's streaming API.