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A method and system for organizing and storing map data that facilitates use of the map data by navigation application programs such as those in navigation systems. The map data represent geographic features located in a region. The map data are organized into parcels such that each parcel contains

A method and system for organizing and storing map data that facilitates use of the map data by navigation application programs such as those in navigation systems. The map data represent geographic features located in a region. The map data are organized into parcels such that each parcel contains a portion of the map data. The map data contained in each parcel represent those geographic features contained in a corresponding separate one of a plurality of rectangular areas located in the region. Each rectangular area has a uniform dimension in a first coordinate direction but has a dimension in the other coordinate direction such that the map data that represent the geographic features contained in the rectangular area are close to, but do not exceed, a maximum data size for a parcel.

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A method and system for organizing and storing map data that facilitates use of the map data by navigation application programs such as those in navigation systems. The map data represent geographic features located in a region. The map data are organized into parcels such that each parcel contains

A method and system for organizing and storing map data that facilitates use of the map data by navigation application programs such as those in navigation systems. The map data represent geographic features located in a region. The map data are organized into parcels such that each parcel contains a portion of the map data. The map data contained in each parcel represent those geographic features contained in a corresponding separate one of a plurality of rectangular areas located in the region. Each rectangular area has a uniform dimension in a first coordinate direction but has a dimension in the other coordinate direction such that the map data that represent the geographic features contained in the rectangular area are close to, but do not exceed, a maximum data size for a parcel. to claim 10, further comprising the step of computing a log likelihood of the high dimensional data, prior to said transforming step. 12. The method according to claim 11, wherein said EM method comprises an expectation step and a maximization step, the expectation step comprising the step of: computing an auxiliary function Q of the EM method, based upon the log likelihood of the high dimensional data; the maximization step comprising the steps of: updating the univariate Gaussian priors, to maximize the auxiliary function Q; and respectively updating the univariate Gaussian variances, the linear transform row by row, and the univariate Gaussian means, to maximize the auxiliary function Q; wherein said second updating step is repeated, until the auxiliary function Q converges to a local maximum, and wherein said computing step and said second updating step are repeated, until the log likelihood of the high dimensional data converges to a local maximum. 13. The method according to claim 12, wherein the linear transform is fixed, when the univariate Gaussian variances are updated. 14. The method according to claim 13, wherein the univariate Gaussian variances are fixed, when the linear transform is updated. 15. The method according to claim 13, wherein the linear transform is fixed, when the univariate Gaussian means are updated. 16. The method according to claim 9, wherein said arranging step hierarchically arranges the coordinates of all the iterations in a tree structure. 17. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for arranging high dimensional data for data mining, said method steps comprising: linearly transforming the high dimensional data into less dependent coordinates, by applying a linear transform of n rows by n columns to the high dimensional data; marginally Gaussianizing each of the coordinates, said Gaussianizing being characterized by univariate Gaussian means, priors, and variances; iteratively repeating said transforming and Gaussianizing steps until the coordinates converge to a standard Gaussian distribution; and arranging the coordinates of all iterations hierarchically to facilitate data mining. 18. The program storage device according to claim 17, wherein said transforming step further comprises the step of applying an iterative maximum likelihood expectation maximization (EM) method to the high dimensional data. 19. The program storage device according to claim 18, further comprising the step of computing a log likelihood of the high dimensional data, prior to said transforming step. 20. The program storage device according to claim 19, wherein said EM method comprises the steps of: computing an auxiliary function Q of the EM method based upon the log likelihood of the high dimensional data; updating the univariate Gaussian priors, to maximize the auxiliary function Q; respectively updating the univariate Gaussian variances, the linear transform row by row, and the univariate Gaussian means, to maximize the auxiliary function Q; repeating said second updating step, until the auxiliary function Q converges to a local maximum; and repeating said computing step and said second updating step, until the log likelihood of the high dimensional data converges to a local maximum. 21. The program storage device according to claim 17, wherein said arranging step hierarchically arranges the coordinates of all the iterations in a tree structure. 22. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for arranging high dimensional data for visualization, said method steps comprising: linearly transforming the high dimensional data into less dependent coordinates, by applying a linear transform of n rows by n columns to the high dimensional data; marginally Gaussianizing each of the coordinates , said Gaussianizing being characterized by univariate Gaussian means, priors, and variances; iteratively repeating said transforming and Gaussianizing steps until the coordinates converge to a standard Gaussian distribution; and arranging the coordinates of all iterations hierarchically into high dimensional data sets to facilitate data visualization. 23. The program storage device according to claim 22 wherein said transforming step further comprises the step of applying an iterative expectation maximization (EM) method to the high dimensional data. 24. The program storage device according to claim 23, further comprising the step of computing a log likelihood of the high dimensional data, prior to said transforming step. 25. The program storage device according to claim 24, wherein said EM method comprises an expectation step and a maximization step, the expectation step comprising the step of: computing an auxiliary function Q of the EM method, based upon the log likelihood of the high dimensional data; the maximization step comprising the steps of: updating the univariate Gaussian priors, to maximize the auxiliary function Q; and respectively updating the univariate Gaussian variances, the linear transform row by row, and the univariate Gaussian means, to maximize the auxiliary function Q; wherein said second updating step is repeated, until the auxiliary function Q converges to a local maximum, and wherein said computing step and said second updating step are repeated, until the log likelihood of the high dimensional data converges to a local maximum. 26. The program storage device according to claim 22, wherein said arranging step hierarchically arranges the coordinates of all the iterations in a tree structure.