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@InProceedings{TanakaVieiKast:2015:EfAlDi,
               author = "Tanaka, Pedro Sena and Vieira, Marcos R. and Kaster, Daniel S.",
          affiliation = "{Universidade Estadual de Londrina (UEL)} and {Big Data Research 
                         Lab} and {Universidade Estadual de Londrina (UEL)}",
                title = "Efficient algorithms to discover flock patterns in trajectories",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Fileto, Renato and Korting, Thales Sehn",
         organization = "Simp{\'o}sio Brasileiro de Geoinform{\'a}tica, 16. (GEOINFO)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "With the ubiquitous use of location enabled devices, pattern 
                         discovery in trajectories has been receiving increasing interest. 
                         Among such patterns, we have queries related to how groups of 
                         moving objects behave over time such as discovering flocks. A 
                         flock pattern is defined as a set of moving objects that move 
                         within a predefined distance to each other for a given continuous 
                         period of time. A typical application example is surveillance, 
                         where relies on discovering flocks on very large streaming 
                         spatiotemporal data efficiently. Previous work presented a 
                         polynomial solution to the problem of finding flocks with fixed 
                         time duration. And presented as well a set of algorithms based on 
                         this solution, which are the state-of-the-art algorithms regarding 
                         this problem. In this paper, we improve those algorithms by 
                         applying the plane sweeping technique in conjunction to an 
                         inverted index. The plane sweeping accelerates the detection of 
                         groups of objects that are candidates to be a flock in a time 
                         instant and the inverted index is used to compare candidate disks 
                         across time instants quickly. Using an assortment of real-world 
                         trajectory datasets, we show that our proposed methods are very 
                         efficient. When compared with the baseline flock algorithm, our 
                         proposed methods achieved up to 46x speedup reducing the elapsed 
                         time from thousands of seconds to milliseconds.",
  conference-location = "Campos do Jord{\~a}o, SP",
      conference-year = "27 nov. a 02 dez.",
             language = "en",
                  ibi = "8JMKD3MGP3W34R/42GRPC2",
                  url = "http://urlib.net/rep/8JMKD3MGP3W34R/42GRPC2",
           targetfile = "tanaka_efficient1.pdf",
        urlaccessdate = "17 jan. 2021"
}


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