Top-k Temporal Keyword Query over Social Media Data

摘要：

　　Analytic jobs over social media data typically need to explore data of different periods.However,most existing keyword search work merely use creation time of items as the measurement of their recency.In this paper we propose top-k temporal keyword query that ranks data by their aggregate sum of shared times during the given time window.A query algorithm that can be executed over a general temporal inverted index is provided.The complexity analysis based on the power law distribution reveals the upper bound of accessed items.Furthermore,twotiers structure and piecewise maximum approximation sketch are proposed as refinements.Extensive empirical studies on a reallife dataset show the combination of two refinements achieves remarkable performance improvement under different query settings.

关键词： Social media Temporal keyword query Top-k query

作者: Fan Xia Chengcheng Yu Weining Qian Aoying Zhou

作者单位: ECNU-RMU-Infosys Data Science Joint Lab,Institute for Data Science and Engineering,East China Normal University,Shanghai,China

会议类型: 国际会议

会议名称: International Asia-Pacific Web Conference(第18届国际亚太互联网大会)

会议地点: 苏州

会议语种:英文

页码: 183-195

在线出版日期: 2016-09-23（万方平台首次上网日期，不代表论文的发表时间）