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US Patent 10360527 Casual modeling of multi-dimensional hierarchical metric cubes

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Is a
Patent
Patent

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10360527
Date of Patent
July 23, 2019
Patent Application Number
12943316
Date Filed
November 10, 2010
Patent Citations Received
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US Patent 12130839 System and method for supporting queries having sub-select constructs in a multidimensional database environment
0
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US Patent 11422881 System and method for automatic root cause analysis and automatic generation of key metrics in a multidimensional database environment
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US Patent 11914620 System and method for aggregating values through risk dimension hierarchies in a multidimensional database environment
0
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US Patent 11922221 System and method for automatic dependency analysis for use with a multidimensional database
0
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US Patent 11531662 System and method for use of a dynamic flow in a multidimensional database environment
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US Patent 10733155 System and method for extracting a star schema from tabular data for use in a multidimensional database environment
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US Patent 10846273 System and method for automatic inference of a cube schema from a tabular data for use in a multidimensional database environment
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US Patent 10909134 System and method for client-side calculation in a multidimensional database environment
...
Patent Primary Examiner
‌
Timothy Padot
Patent abstract

A computing system initializes a first frontier to be a root of a multi-dimensional hierarchical data structure representing an entity. The system acquires first data corresponding to the first frontier. The system performs modeling on the first data to obtain a first model and a corresponding first statistic. The system expands a dimension of the first frontier. The system gathers second data corresponding to the expanded frontier. The system applies the data modeling on the second data to obtain a second model and a corresponding second statistic. The system compares the first statistic of the first model and the second statistic of the second model. The system sets the second model to be the first model in response to determining that the second model statistic is better than the first model statistic. The system outputs the first model.

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