GeoTrend

Spatial trending queries on real-time Microblogs

Amr Magdy, Ahmed M. Aly, Mohamed F Mokbel, Sameh Elnikety, Yuxiong He, Suman Nath, Walid G. Aref

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

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. (2) It supports trending measures that effectively capture trending items under a variety of definitions that suit different applications. (3) It promotes recent microblogs as first-class citizens and optimizes its system components to digest a continuous flow of fast data in main-memory while removing old data efficiently. GeoTrend queries are top-k queries that discover the most trending k keywords that are posted within an arbitrary spatial region and during the last T time units. To support its queries efficiently, GeoTrend employs an in-memory spatial index that is able to efficiently digest incoming data and expire data that is beyond the last T time units. The index also materializes top-k keywords in different spatial regions so that incoming queries can be processed with low latency. In case of peak times, a main-memory optimization technique is employed to shed less important data, so that the system still sustains high query accuracy with limited memory resources. Experimental results based on real Twitter feed and Bing Mobile spatial search queries show the scalability of GeoTrend to support arrival rates of up to 50,000 microblog/second, average query latency of 3 milli-seconds, and at least 90+% query accuracy even under limited memory resources.

Original languageEnglish (US)
Title of host publication24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016
EditorsMatthias Renz, Mohamed Ali, Shawn Newsam, Matthias Renz, Siva Ravada, Goce Trajcevski
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450345897
DOIs
StatePublished - Oct 31 2016
Event24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016 - Burlingame, United States
Duration: Oct 31 2016Nov 3 2016

Publication series

NameGIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

Other

Other24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016
CountryUnited States
CityBurlingame
Period10/31/1611/3/16

Fingerprint

Query
Real-time
Data storage equipment
Latency
resource
Spatial Index
Scalability
Resources
Unit
Arbitrary
Optimization Techniques
Optimise
Experimental Results
index
trend
Trends

Keywords

  • Indexing
  • Microblogs
  • Query Processing
  • Real-time
  • Spatial
  • Trend

Cite this

Magdy, A., Aly, A. M., Mokbel, M. F., Elnikety, S., He, Y., Nath, S., & Aref, W. G. (2016). GeoTrend: Spatial trending queries on real-time Microblogs. In M. Renz, M. Ali, S. Newsam, M. Renz, S. Ravada, & G. Trajcevski (Eds.), 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016 [7] (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems). Association for Computing Machinery. https://doi.org/10.1145/2996913.2996986

GeoTrend : Spatial trending queries on real-time Microblogs. / Magdy, Amr; Aly, Ahmed M.; Mokbel, Mohamed F; Elnikety, Sameh; He, Yuxiong; Nath, Suman; Aref, Walid G.

24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016. ed. / Matthias Renz; Mohamed Ali; Shawn Newsam; Matthias Renz; Siva Ravada; Goce Trajcevski. Association for Computing Machinery, 2016. 7 (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Magdy, A, Aly, AM, Mokbel, MF, Elnikety, S, He, Y, Nath, S & Aref, WG 2016, GeoTrend: Spatial trending queries on real-time Microblogs. in M Renz, M Ali, S Newsam, M Renz, S Ravada & G Trajcevski (eds), 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016., 7, GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, Association for Computing Machinery, 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016, Burlingame, United States, 10/31/16. https://doi.org/10.1145/2996913.2996986
Magdy A, Aly AM, Mokbel MF, Elnikety S, He Y, Nath S et al. GeoTrend: Spatial trending queries on real-time Microblogs. In Renz M, Ali M, Newsam S, Renz M, Ravada S, Trajcevski G, editors, 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016. Association for Computing Machinery. 2016. 7. (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems). https://doi.org/10.1145/2996913.2996986
Magdy, Amr ; Aly, Ahmed M. ; Mokbel, Mohamed F ; Elnikety, Sameh ; He, Yuxiong ; Nath, Suman ; Aref, Walid G. / GeoTrend : Spatial trending queries on real-time Microblogs. 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016. editor / Matthias Renz ; Mohamed Ali ; Shawn Newsam ; Matthias Renz ; Siva Ravada ; Goce Trajcevski. Association for Computing Machinery, 2016. (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems).
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abstract = "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. (2) It supports trending measures that effectively capture trending items under a variety of definitions that suit different applications. (3) It promotes recent microblogs as first-class citizens and optimizes its system components to digest a continuous flow of fast data in main-memory while removing old data efficiently. GeoTrend queries are top-k queries that discover the most trending k keywords that are posted within an arbitrary spatial region and during the last T time units. To support its queries efficiently, GeoTrend employs an in-memory spatial index that is able to efficiently digest incoming data and expire data that is beyond the last T time units. The index also materializes top-k keywords in different spatial regions so that incoming queries can be processed with low latency. In case of peak times, a main-memory optimization technique is employed to shed less important data, so that the system still sustains high query accuracy with limited memory resources. Experimental results based on real Twitter feed and Bing Mobile spatial search queries show the scalability of GeoTrend to support arrival rates of up to 50,000 microblog/second, average query latency of 3 milli-seconds, and at least 90+{\%} query accuracy even under limited memory resources.",
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