@inproceedings{c3cfb09dde2140279a8dddc7a81ca38f,
title = "Location-aware top-κ term publish/subscribe",
abstract = "Massive amount of data that contain spatial, textual, and temporal information are being generated at a high scale. These spatio-Temporal documents cover a wide range of topics in local area. Users are interested in receiving local popular terms from spatio-Temporal documents published with a specified region. We consider the Top-k Spatial-Temporal Term (ST2) Subscription. Given an ST2 subscription, we continuously maintain up-To-date top-k most popular terms over a stream of spatio-Temporal documents. The ST2 subscription takes into account both frequency and recency of a term generated from spatio-Temporal document streams in evaluating its popularity. We propose an efficient solution to process a large number of ST2 subscriptions over a stream of spatio-Temporal documents. The performance of processing ST2 subscriptions is studied in extensive experiments based on two real spatio-Temporal datasets.",
keywords = "Spatial, Subscribe, Temporal, publish, stream",
author = "Lisi Chen and Shuo Shang and Zhiwei Zhang and Xin Cao and Jensen, \{Christian S.\} and Panos Kalnis",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 34th IEEE International Conference on Data Engineering, ICDE 2018 ; Conference date: 16-04-2018 Through 19-04-2018",
year = "2018",
month = oct,
day = "24",
doi = "10.1109/ICDE.2018.00073",
language = "English",
series = "Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "749--760",
booktitle = "Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018",
address = "United States",
}