Efficiently identify local frequent keyword co-occurrence patterns in geo-tagged Twitter stream

Publication Type:
Conference Proceeding
Citation:
SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2014, pp. 1215 - 1218
Issue Date:
2014-01-01
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With the prevalence of the geo-position enabled devices and services, a rapidly growing amount of tweets are associated with geo-tags. Consequently, the real time search on geotagged Twitter streams has attracted great attentions. In this paper, we advocate the significance of the co-occurrence of keywords for the geo-tagged tweets data analytics, which is overlooked by existing studies. Particularly, we formally introduce the problem of identifying local frequent keyword co-occurrence patterns over the geo-tagged Twitter streams, namely LFP query. To accommodate the high volume and the rapid updates of the Twitter stream, we develop an inverted KMV sketch (IK sketch for short) structure to capture the co-occurrence of keywords in limited space. Then efficient algorithms are developed based on IK sketch to support LFP queries as well as its variant. The extensive empirical study on real Twitter dataset confirms the effectiveness and efficiency of our approaches. Copyright 2014 ACM.
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