Abstract: A system for selecting a best email address aggregates all evidence about the emails associated with a consumer utilizing an identity graph. This evidence is used to make a choice concerning the best email address by applying processing engines. The evidence may include point-in-time (PIT) signals; temporal date signals; recency; source contribution; the URL provider; overlapping of the local email portion with a consumer's name; and the number of people in the household of that consumer who share the same local email address portion. The presence of profanity in the local portion of an email address may also be used by a profanity engine in determining whether an email address is or is not the best email address, and tiebreakers may be used by a tiebreaking engine when there are two or more email addresses that are scored closely.

Abstract: A method for improving fault tolerance of MapReduce computing systems processing extremely large data sets employs a custom mapper script that outputs the results to the shuffle and reduce processes. The Reducer writes its results into multiple files that are then compressed. The resulting files are processed by a map-only Reducer job that aggregates and reduces the results of a single file into a single output. The non-block compression prevents the file from being split across multiple mappers. The job can recover if a single mapper fails without requiring that each Map node be rerun, thus improving fault tolerance.

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.

Abstract: A method and apparatus for matching equivalent words across languages takes advantage of a set of rules that are built from a user-defined language specification (UDLS), which may be open source and customizable by a language expert. The UDLS is used to build a customer language library (CLL) that includes a list of consonants, a list of vowels, and rules defining phoneme equivalencies across two languages. The CLL is used to match equivalent words by both two-set and three-set matching to not only increase the number of true matches (i.e., overall accuracy), but also improve recognition of variations in a manner that is not language specific.

Abstract: A method and apparatus for matching equivalent words across languages takes advantage of a set of rules that are built from a user-defined language specification (UDLS), which may be open source and customizable by a language expert. The UDLS is used to build a customer language library (CLL) that includes a list of consonants, a list of vowels, and rules defining phoneme equivalencies across two languages. The CLL is used to match equivalent words by both two-set and three-set matching to not only increase the number of true matches (i.e., overall accuracy), but also improve recognition of variations in a manner that is not language specific.