Abstract: A method for computationally scoring the trustworthiness of source data begins with a subset of the raw source data. Fields and related fields are identified, profiled, and results aggregated. In addition, the subset of raw source data is input to an entity resolution system, with results summarized and aggregated. The output of these two streams of processes are used to compute a scorecard. In addition, a sandbox of the entity resolution system's data graph is constructed, the sandbox is modified with the source data, and the difference between the baseline sandbox and modified sandbox is computed. Changes to entities are computed for all entities as well as for most sought-after entities, and the results are summarized and aggregated into the overall scorecard.

Abstract: A method for computationally scoring the trustworthiness of source data begins with a subset of the raw source data. Fields and related fields are identified, profiled, and results aggregated. In addition, the subset of raw source data is input to an entity resolution system, with results summarized and aggregated. The output of these two streams of processes are used to compute a scorecard. In addition, a sandbox of the entity resolution system's data graph is constructed, the sandbox is modified with the source data, and the difference between the baseline sandbox and modified sandbox is computed. Changes to entities are computed for all entities as well as for most sought-after entities, and the results are summarized and aggregated into the overall scorecard.

Abstract: An entity resolution data structure system compares two data graphs by creating a confusion matrix in a distributed processing environment. A benchmark file is created from one data graph for comparison to a reference data graph. Identifiers and metadata are appended to the benchmark file to allow the comparison to take place and the construction of a confusion matrix. The confusion matrix provides a high-level indication of the results of the comparison. When the data graphs contain personally identifiable information (Pll), the process does not require any Pll to be transmitted in either direction between the parties who maintain the data graphs to be compared.

Abstract: An entity resolution data structure system compares two data graphs by creating a confusion matrix in a distributed processing environment. A benchmark file is created from one data graph for comparison to a reference data graph. Identifiers and metadata are appended to the benchmark file to allow the comparison to take place and the construction of a confusion matrix. The confusion matrix provides a high-level indication of the results of the comparison. When the data graphs contain personally identifiable information (Pll), the process does not require any Pll to be transmitted in either direction between the parties who maintain the data graphs to be compared.

Abstract: A system and method for transforming input data in a data graph is structured in such a way that it does not destroy embedded contextual data yet also keeps the number of edges in the data graph sufficiently small in number that computation with respect to the data in the data graph is feasible with existing computational resources on extremely large graph sets. Incoming data is represented as a collection of “cliques” rather than placing each data object into its own node in the graph database. Maintaining the clique structure though the graph build pipeline dramatically reduces the exponential increase in the number of edges in the graph, while also maintaining all of the contextual data presented on the input record.

Abstract: A system and method for transforming input data in a data graph is structured in such a way that it does not destroy embedded contextual data yet also keeps the number of edges in the data graph sufficiently small in number that computation with respect to the data in the data graph is feasible with existing computational resources on extremely large graph sets. Incoming data is represented as a collection of “cliques” rather than placing each data object into its own node in the graph database. Maintaining the clique structure though the graph build pipeline dramatically reduces the exponential increase in the number of edges in the graph, while also maintaining all of the contextual data presented on the input record.