Efficient Range Query on Moving Object Trajectories
نویسندگان
چکیده
منابع مشابه
Efficient mutual nearest neighbor query processing for moving object trajectories
Given a set D of trajectories, a query object q, and a query time extent C, amutual (i.e., symmetric) nearest neighbor (MNN) query over trajectories finds from D, the set of trajectories that are among the k1 nearest neighbors (NNs) of q within C, and meanwhile, have q as one of their k2 NNs. This type of queries is useful inmany applications such as decisionmaking, data mining, and pattern rec...
متن کاملA Query Language for Moving Object Trajectories
Trajectory properties are spatio-temporal properties that describe the changes of spatial (topological) relationships of one moving object with respect to regions and trajectories of other moving objects. Trajectory properties can be viewed as continuous changes of an object’s location resulting in a continuous change in the topological relationship between this object and other entities of int...
متن کاملEfficient Trajectory Cover Search for Moving Object Trajectories
Given a set of query locations and a set of query keywords, a Trajectory Cover (CT) query over a repository of mobile trajectories returns a minimal set of trajectories that maximally covers the query keywords and are also spatially close to the query locations. Processing CT queries over mobile trajectories requires substantially different algorithms than those for location range queries. The ...
متن کاملNearest Neighbor Search on Moving Object Trajectories
With the increasing number of Mobile Location Services (MLS), the need for effective k-NN query processing over historical trajectory data has become the vehicle for data analysis, thus improving existing or even proposing new services. In this paper, we investigate mechanisms to perform NN search on R-tree-like structures storing historical information about moving object trajectories. The pro...
متن کاملDiscovering Chasing Behavior in Moving Object Trajectories
With the increasing use of mobile devices, a lot of tracks of movement of objects are being collected. The advanced trajectory data mining research has allowed the discovery of many types of patterns from these data, like flocks, leadership, avoidance, frequent sequences, and other types of patterns. In this paper we introduce a new kind of pattern: a chasing behavior between trajectories. We p...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of the Korea Institute of Information and Communication Engineering
سال: 2014
ISSN: 2234-4772
DOI: 10.6109/jkiice.2014.18.2.364