Scalable Top-k Spatio-Temporal Term Querying

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

  • Anders Skovsgaard
  • Darius Sidlauskas, Denmark
  • Christian S. Jensen, Aalborg University, Denmark
With the rapidly increasing deployment of Internet-connected, location-aware mobile devices, very large and increasing amounts of geo-tagged and timestamped user-generated content, such as microblog posts, are being generated. We present indexing, update, and query processing techniques that are capable of providing the top-k terms seen in posts in a user-specified spatio-temporal range. The techniques enable interactive response times in the millisecond range in a realistic setting where the arrival rate of posts exceeds today's average tweet arrival rate by a factor of 4-10. The techniques adaptively maintain the most frequent items at various spatial and temporal granularities. They extend existing frequent item counting techniques to maintain exact counts rather than approximations. An extensive empirical study with a large collection of geo-tagged tweets shows that the proposed techniques enable online aggregation and query processing at scale in realistic settings.
Original languageEnglish
Pages (from-to)148-159
Number of pages12
Publication statusPublished - 2014
EventICDE - Chicago, United States
Duration: 31 Mar 20144 Apr 2014


CountryUnited States

See relations at Aarhus University Citationformats

ID: 70867120