期刊
VLDB JOURNAL
卷 29, 期 5, 页码 1101-1128出版社
SPRINGER
DOI: 10.1007/s00778-020-00607-8
关键词
Publish; Subscribe; Spatio-temporal; Keyword; Stream
Massive 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-kterm subscriptions over streams of spatio-temporal documents: region-based top-kspatial-temporal term (RST) subscriptions and similarity-based top-kspatio-temporal term (SST) subscriptions. RST subscriptions continuously maintain the top-kmost popular trending terms within a user-defined region. SST subscriptions free users from defining a region and maintain top-klocally 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.
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