KAUST DepartmentComputer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
InfoCloud Research Group
Online Publication Date2020-03-09
Print Publication Date2020-09
Embargo End Date2021-03-09
Permanent link to this recordhttp://hdl.handle.net/10754/662293
MetadataShow full item record
AbstractMassive amounts of data that contain spatial, textual, and temporal information are being generated at a rapid pace. With streams of such data, which includes check-ins and geo-tagged tweets, available, users may be interested in being kept up-to-date on which terms are popular in the streams in a particular region of space. To enable this functionality, we aim at efficiently processing two types of general top-k term subscriptions over streams of spatio-temporal documents: region-based top-k spatial-temporal term (RST) subscriptions and similarity-based top-k spatio-temporal term (SST) subscriptions. RST subscriptions continuously maintain the top-k most popular trending terms within a user-defined region. SST subscriptions free users from defining a region and maintain top-k locally popular terms based on a ranking function that combines term frequency, term recency, and term proximity. To solve the problem, we propose solutions that are capable of supporting real-life location-based publish/subscribe applications that process large numbers of SST and RST subscriptions over a realistic stream of spatio-temporal documents. The performance of our proposed solutions is studied in extensive experiments using two spatio-temporal datasets.
CitationChen, L., Shang, S., Jensen, C. S., Xu, J., Kalnis, P., Yao, B., & Shao, L. (2020). Top-k term publish/subscribe for geo-textual data streams. The VLDB Journal. doi:10.1007/s00778-020-00607-8
SponsorsThis work was supported by the National Natural Science Foundation of China (61932004, 61922054, 61872235, 61729202, 61832017, U1636210), the National Key Research and Development Program of China (2018YFC1504504, 2016YFB0700502), and Hong Kong RGC Grant 12201018.
PublisherSpringer Science and Business Media LLC