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Mixed-drove spatio-temporal co-occurrence patterns (MDCOPs) represent subsets of object-types that are located together in space and time. Discovering MDCOPs is an important problem with many applications such as identifying tactics in battlefields, games, and predator-prey interactions. However, mining MDCOPs is computationally very expensive because the(More)
Mean streets represent those connected subsets of a spatial network whose attribute values are significantly higher than expected. Discovering and quantifyingmean streets is an important problem with many applications such as detecting high-crime-density streets and high crash roads (or areas) for public safety, detecting urban cancer disease clusters for(More)
Spatial co-location patterns represent the subsets of features whose instances are frequently located together in geographic space. Co-location pattern discovery presents challenges since the instances of spatial features are embedded in a continuous space and share a variety of spatial relationships. A large fraction of the computation time is devoted to(More)
Zonal co-location patterns represent subsets of feature- types that are frequently located in a subset of space (i.e., zone). Discovering zonal spatial co-location patterns is an important problem with many applications in areas such as ecology, public health, and homeland defense. However, discovering these patterns with dynamic parameters (i.e., repeated(More)
Mixed-drove spatio-temporal co-occurrence patterns (MDCOPs) represent subsets of object-types that are located together in space and time. Discovering MDCOPs is an important problem with many applications such as identifying tactics in battlefields, games, and predator-prey interactions. However, mining MDCOPs is computationally very expensive because the(More)
Mixed-drove spatio-temporal co-occurrence patterns (MDCOPs) represent subsets of two or more different object-types whose instances are often located in spatial and temporal proximity. Discovering MDCOPs is an important problem with many applications such as identifying tactics in battlefields, games, and predator-prey interactions. However, mining MDCOPs(More)
Sustained emerging spatio-temporal co-occurrence patterns (SECOPs) represent subsets of object-types that are increasingly located together in space and time. Discovering SECOPs is important due to many applications, e.g., predicting emerging infectious diseases, predicting defensive and offensive intent from troop movement patterns, and novel predator-prey(More)
Mixed-drove spatio-temporal co-occurrence patterns (MDCOPs) represent subsets of object-types that are located together in space and time. Discovering MDCOPs is an important problem with many applications such as planning battlefield tactics, and tracking predator-prey interactions. However, determining suitable interest measure thresholds is a difficult(More)
Spatio-temporal co-occurrence patterns represent subsets of object-types that are often located together in space and time. The aim of the discovery of partial spatio-temporal co-occurrence patterns (PACOPs) is to find co-occurrences of the object-types that are partially present in the database. Discovering PACOPs is an important problem with many(More)
Given a spatial crime data warehouse, that is updated infrequently and a set of operations O as well as constraints of storage and update overheads, the index type selection problem is to find a set of index types that can reduce the I/O cost of the set of operations. The index type selection problem is important to improve user experience and system(More)