This paper presents GeoTrend; a system for scalable support of spatial
\ntrend discovery on recent microblogs, e.g., tweets and online
\nreviews, that come in real time. GeoTrend is distinguished from
\nexisting techniques in three aspects: (1) It discovers trends in arbitrary
\nspatial regions, e.g., city blocks. (2) It supports trending
\nmeasures that effectively capture trending items under a variety of
\ndefinitions that suit different applications. (3) It promotes recent
\nmicroblogs as first-class citizens and optimizes its system components
\nto digest a continuous flow of fast data in main-memory while
\nremoving old data efficiently. GeoTrend queries are top-k queries
\nthat discover the most trending k keywords that are posted within
\nan arbitrary spatial region and during the last T time units. To support
\nits queries efficiently, GeoTrend employs an in-memory spatial
\nindex that is able to efficiently digest incoming data and expire
\ndata that is beyond the last T time units. The index also materializes
\ntop-k keywords in different spatial regions so that incoming
\nqueries can be processed with low latency. In case of peak times,
\na main-memory optimization technique is employed to shed less
\nimportant data, so that the system still sustains high query accuracy
\nwith limited memory resources. Experimental results based
\non real Twitter feed and Bing Mobile spatial search queries show
\nthe scalability of GeoTrend to support arrival rates of up to 50,000
\nmicroblog\/second, average query latency of 3 milli-seconds, and at
\nleast 90+% query accuracy even under limited memory resources.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper presents GeoTrend; a system for scalable support of spatial trend discovery on recent microblogs, e.g., tweets and online reviews, that come in real time. GeoTrend is distinguished from existing techniques in three aspects: (1) It discovers trends in arbitrary spatial regions, e.g., city blocks. 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