@inproceedings{0a0d2ae0a68145f68ff4d5c5c88ef3ff,
title = "Top-k temporal keyword query over social media data",
abstract = "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.",
keywords = "Social media, Temporal query, Top-k query",
author = "Fan Xia and Chengcheng Yu and Weining Qian and Aoying Zhou",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 18th Asia-Pacific Web Conference on Web Technologies and Applications, APWeb 2016 ; Conference date: 23-09-2016 Through 25-09-2016",
year = "2016",
doi = "10.1007/978-3-319-45814-4\_15",
language = "英语",
isbn = "9783319458137",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "183--195",
editor = "Guanfeng Liu and Feifei Li and Kyuseok Shim and Kai Zheng",
booktitle = "Web Technologies and Applications - 18th Asia-Pacific Web Conference, APWeb 2016, Proceedings",
address = "德国",
}