DISTRIBUTED DATA WAREHOUSE
First Claim
1. One or more computer-storage media storing computer-useable instructions that, when executed by a computing device, perform a method for mining data, comprising:
- aggregating a plurality of definitions for auxiliary data structures, the definitions including a plurality of measures and one or more dimensions, the definition of each dimension being associated with at least one measure from the plurality of measures;
constructing a common fact table including the plurality of measures and dimension keys for the one or more dimensions, the common fact table being constructed based on using the aggregated definitions of the plurality of auxiliary data structures to process one or more initial data files;
constructing one or more dimension tables corresponding to the one or more dimensions, the one or more dimension tables being stored separately from the common fact table;
forming a plurality of auxiliary data structures, each auxiliary data structure including one or more measures and one or more dimensions, a first user being associated with a first subset of the auxiliary data structures;
receiving a user data query from the first user, the user data query comprising a combination of a measure and a dimension;
identifying an auxiliary data structure from the plurality of auxiliary data structures that includes the combination of the measure and the dimension, the identified auxiliary data structure being different from the first subset of auxiliary data structures;
generating a responsive result to the user data query based on the identified auxiliary data structure; and
providing the generated responsive result to the first user.
1 Assignment
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Accused Products
Abstract
Methods and data structures are provided for allowing data mining with improved efficiency. During processing of a usage log (or multiple logs) for an activity, such as a usage logfile of network search activity, a common fact table is generated. The common fact table allows a plurality of auxiliary data structures to be formed from the common fact table. These auxiliary data structures are designed to allow users to submit queries against the contents of the data structure in order to investigate the data. The efficiency of access of the common fact table is improved by allowing users to access auxiliary data structures other than the auxiliary data structures that are associated with a user. Optionally, the common fact table and/or the auxiliary data structures can include dimension values that correspond to both pre-identified dimension values as well as dimension values that are identified during processing of the activity logfiles.
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Citations
20 Claims
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1. One or more computer-storage media storing computer-useable instructions that, when executed by a computing device, perform a method for mining data, comprising:
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aggregating a plurality of definitions for auxiliary data structures, the definitions including a plurality of measures and one or more dimensions, the definition of each dimension being associated with at least one measure from the plurality of measures; constructing a common fact table including the plurality of measures and dimension keys for the one or more dimensions, the common fact table being constructed based on using the aggregated definitions of the plurality of auxiliary data structures to process one or more initial data files; constructing one or more dimension tables corresponding to the one or more dimensions, the one or more dimension tables being stored separately from the common fact table; forming a plurality of auxiliary data structures, each auxiliary data structure including one or more measures and one or more dimensions, a first user being associated with a first subset of the auxiliary data structures; receiving a user data query from the first user, the user data query comprising a combination of a measure and a dimension; identifying an auxiliary data structure from the plurality of auxiliary data structures that includes the combination of the measure and the dimension, the identified auxiliary data structure being different from the first subset of auxiliary data structures; generating a responsive result to the user data query based on the identified auxiliary data structure; and providing the generated responsive result to the first user. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 20)
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11. A computer-implemented method for mining data, comprising:
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aggregating a plurality of definitions for auxiliary data structures, the definitions including a plurality of measures and a plurality of dimensions, the definitions comprising a plurality of managed dimension values for at least one dimension; processing one or more initial data files based on the aggregated definitions to extract values for the plurality of measures and the plurality of dimensions, the extracted values including one or more unmanaged dimension values for the at least one dimension; validating the one or more unmanaged dimension values; constructing a common fact table including the plurality of measures and dimension keys for the one or more dimensions, the common fact table being constructed based on the extracted values; constructing one or more dimension tables corresponding to the plurality of dimensions based on the extracted values, the one or more dimension tables being stored separately from the common fact table; forming a plurality of auxiliary data structures, each auxiliary data structure including one or more measures and one or more dimensions, at least one auxiliary dimension table including a dimension having validated unmanaged dimension values; receiving a user data query, the user data query comprising one or more combinations of measures and dimensions; generating a responsive result to the user data query based on at least one of the plurality of auxiliary data structures; and providing the generated responsive result. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18)
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19. A computer-implemented method for mining data, comprising:
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aggregating a plurality of definitions for auxiliary data structures, the definitions including a plurality of measures and a plurality of dimensions, the definitions comprising a plurality of managed dimension values for at least one dimension, the definition of each dimension being associated with at least one measure from the plurality of measures; processing one or more initial data files based on the aggregated definitions to extract values for the plurality of measures and the plurality of dimensions, the extracted values including one or more unmanaged dimension values for the at least one dimension; validating the one or more unmanaged dimension values; constructing a common fact table including the plurality of measures and dimension keys for the plurality of dimensions, the common fact table being constructed based on the extracted values; constructing one or more dimension tables corresponding to the plurality of dimensions based on the extracted values, the one or more dimension tables being stored separately from the common fact table; forming a plurality of auxiliary data structures, each auxiliary data structure including one or more measures and one or more dimensions, at least one auxiliary dimension table including a dimension having validated unmanaged dimension values, a first user being associated with a first subset of the auxiliary data structures; receiving a user data query from the first user, the user data query including a combination of a measure and a dimension; identifying an auxiliary data structure from the plurality of auxiliary data structures that includes the combination of the measure and the dimension, the identified auxiliary data structure being different from the first subset of auxiliary data structures; and generating a responsive result to the user data query based on the identified auxiliary data structure.
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Specification