TY - GEN
T1 - Location-Aware Top-k Term Publish/Subscribe
AU - chen, Lisi
AU - Shang, Shuo
AU - Zhang, Zhiwei
AU - Cao, Xin
AU - Jensen, Christian S.
AU - Kalnis, Panos
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported by the grant of the Hong Kong Research Grants Council, Hong Kong SAR, China, No. 12258116 and the Nation Nature Science Foundation of China, China, No. 61602395.
PY - 2018/10/25
Y1 - 2018/10/25
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/631621
UR - https://ieeexplore.ieee.org/document/8509294
UR - http://www.scopus.com/inward/record.url?scp=85051537124&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2018.00073
DO - 10.1109/ICDE.2018.00073
M3 - Conference contribution
SN - 9781538655207
SP - 749
EP - 760
BT - 2018 IEEE 34th International Conference on Data Engineering (ICDE)
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -