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We study the problem of efficient discovery of trending phrases from high-volume text streams -be they sequences of Twitter messages, email messages, news articles, or other timestamped text documents. Most exisiting approaches return top-k trending phrases. But, this approach neither guarantees that the top-k phrases returned are all trending, nor that all trending phrases are returned. In addition, the value of k is difficult to set and is indifferent to stream dynamics. Hence, we propose andoi:10.1145/2063576.2063937 dblp:conf/cikm/KamathC11 fatcat:opaazarlrje3rbqjuxhr2dsj3e