Abstract: Various embodiments pertain to techniques for predicting and isolating patterns or trends across datasets. In various embodiments, one or more Q-entities are extracted from a data seed, associated with one or more dimensions, and classified into one or more clusters for each dimension with which it is associated. In some embodiments, a Q-entity can exist in more than one dimension and/or more than one cluster within a dimension. Once information from the data seed is associated with a dimension and cluster, frequency analysis can be utilized to ascertain a pattern or trend in the data. In various embodiments, additional data can be processed, added to the dimensions and clusters, and frequency analysis can be performed on the updated dataset to provide additional information on the pattern or trend.

Abstract: Super-clusters of point-of-interest locations are created based on how close the point-of-interest locations are to one another. Additionally, one or more mini-clusters are created in each of the super-clusters based on how close the point-of-interest locations within each super-cluster are to one another. For each of one or more mini-clusters, some point-of-interest icons corresponding to the point-of-interest locations are placed based at least in part on a mean out positioning vector that is based at least in part on a center of the mini-cluster and a center of the super-cluster that includes the mini-cluster. Additionally, some point-of-interest icons are attempted to be placed as a group, while others are attempted to be placed individually.

Abstract: Super-clusters of point-of-interest locations are created based on how close the point-of-interest locations are to one another. Additionally, one or more mini-clusters are created in each of the super-clusters based on how close the point-of-interest locations within each super-cluster are to one another. For each of one or more mini-clusters, some point-of-interest icons corresponding to the point-of-interest locations are placed based at least in part on a mean out positioning vector that is based at least in part on a center of the mini-cluster and a center of the super-cluster that includes the mini-cluster. Additionally, some point-of-interest icons are attempted to be placed as a group, while others are attempted to be placed individually.

Abstract: The claimed subject matter provides a system and/or a method that facilitates providing an immerse view having at least one portion related to aerial view data and a disparate portion related to a first-person ground-level view. A receiver component can receive at least one of geographic data and an input. An interface component can generate an immersed view based on at least one of the geographic data and the input, the immersed view includes a first portion of aerial data and a second portion of a first-person perspective view corresponding to a location related to the aerial data.