ENHANCED NEAREST GROUP QUERY OPTIMIZATION WITH DATA ANALYTICS IN GEOLOCATION DATA

Spatial data is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial dataset Extracting interesting and useful patterns from Google spatial datasets is extracting the corresponding patterns from traditional numeric and categorical data thanks to the complexity of spatial data types, spatial relationships, and spatial autocorrelation. Spatial data is about instances located during a physical space. When spatial information becomes dominant interest, spatial data processing should be applied. Spatial data structures can facilitate spatial mining. Standard data mining algorithms are often modified for spatial data mining, with a considerable part of pre-processing to require under consideration of spatial information. Initially the set of knowledge points, containing the keyword information of the query object and therefore the query keyword should tend by the User. By Group Nearest query, each nearest point matches a minimum of one among the query keywords of the User. Next, the user wants to rank the selected locations with respect to the sum of distances to nearest interested facilities. As a result, the best location can be obtained from the minimized summed Distance calculation.