Abstract: The present invention relates to a method and system for generating client preference recommendations in a high performance computing regime. Accordingly, one embodiment of the present invention comprises: providing a sparse ratings matrix, forming a plurality of data structures representing the sparse ratings matrix, forming a runtime recommendation model from the plurality of data structures, determining a recommendation from the runtime recommendation model in response to a request from a user, and providing the recommendation to the user.

Abstract: The present invention relates to a method and system for generating client preference recommendations in a high performance computing regime. Accordingly, one embodiment of the present invention comprises: providing a sparse ratings matrix, forming a plurality of data structures representing the sparse ratings matrix, forming a runtime recommendation model from the plurality of data structures, determining a recommendation from the runtime recommendation model in response to a request from a user, and providing the recommendation to the user.

Abstract: The present invention relates to a method and system for generating client preference recommendations in a high performance computing regime. Accordingly, one embodiment of the present invention comprises: providing a sparse ratings matrix, forming a plurality of data structures representing the sparse ratings matrix, forming a runtime recommendation model from the plurality of data structures, determining a recommendation from the runtime recommendation model in response to a request from a user, and providing the recommendation to the user.

Abstract: A computerized method for analyzing data files finds association relationships between attributes recorded in the data files. The computer receives one or more data file records, each record containing two or more of attributes of interest. Software defines at least two attribute k-patterns by reference to a subset of the attributes, where k is an integer and represents the number of attributes that are included in a k-pattern. Using an invertible mapping algorithm, the software maps each of the at least two attribute k-patterns into an associated key and determines a distribution pattern for each of the at least two attribute k-patterns defined in step. The software then inverts the mapping to decode from the keys to the associated attribute k-pattern; and, using the keys, associates the distribution patterns determined with each of the at least two attribute k-patterns. Output is in the form of reports, detection signals and/or control signals.

Abstract: A computerized method for analyzing data files finds association relationships between attributes recorded in the data files. The computer receives one or more data file records, each record containing two or more of attributes of interest. Software defines at least two attribute k-patterns by reference to a subset of the attributes, where k is an integer and represents the number of attributes that are included in a k-pattern. Using an invertible mapping algorithm, the software maps each of the at least two attribute k-patterns into an associated key and determines a distribution pattern for each of the at least two attribute k-patterns defined in step. The software then inverts the mapping to decode from the keys to the associated attribute k-pattern; and, using the keys, associates the distribution patterns determined with each of the at least two attribute k-patterns. Output is in the form of reports, detection signals and/or control signals.