Efficiently answering top-k frequent term queries in temporal-categorical range

In the procedure of extracting hot topics and detecting emerging topic, counting term frequency is one of the most inevitable and time-consuming steps. For the purpose of text exploration, users may change the query range frequently, and the adjustment of ranges would cause recalculation of term frequency when finding hot terms, bringing unacceptable time cost. In addition, real-time update of dimensions is also a challenge. To address these problems, we first propose a novel data structure based on prefix cube to store terms and their frequencies, so that the time for counting term frequency gets a significant reduction. Based on the data structure, we propose an efficient range query algorithm that significantly decreases the number of input word lists involved in top-k queries. Considering the underlying dimension update, we also design an efficient maintenance mechanism to cope with different dimension updates. Finally, we conduct comprehensive experiments to validate the effectiveness of the proposed structure and the efficiency of the optimized query algorithm. We also prove that using the proposed data structure, the time cost of our algorithms in hot topic extraction and emerging topic detection can be reduced by about ten times compared with the previous algorithms.