Liveramp, Inc.

Acxiom Corporation

Pavan Roy Marupally

Adam Zimmerman

W Dwayne Collins

Joseph Shannon Duncan

Amresh Mohanlal

Gon Yi

Dwayne Collins

Aaron Miyahira

Dwayne W Collins

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.

Fault tolerant big data processing

A system and method for efficiently matching into an extremely large data set uses two data stores. A search data store contains identifiers that correspond to localized portions of the main data store, along with matching values for that portion of the main data store. To perform a matching operation, the search data store is first searched for a matching value. Then the identifier for the localized portion of the main data store is retrieved from the search data store. Using this identifier, a search may be performed against only the corresponding localized portion of the main data store rather than the entire main data store.

System and method for matching into a complex data set

An identity graph is partitioned into records pertaining to entities with similar data. A single authentication string or asserted relationship (AR) that represents a person identifies the person in the graph whose composite information is closest to the input by using all of the available context provided by the input string. By providing more contextual data to the match service than simply a name and single touchpoint, the service is able to match to a more focused and unique set of persons. In certain embodiments, the invention allows for the ability to take a small set of input data and more accurately identify its location in an identity graph much faster in time and lower in computational cost.

Asserted relationships matching in an identity graph data structure

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.

Asserted relationship data structure

A distributed node cluster architecture within a parallel computing environment provides for the association of individuals and households accessing a communications network through multiple electronic communications devices with a particular digital touchpoint or touchpoints used when communicating on the network. The cluster facilitates the determination of a touchpoint when an individual or household name is determined, or, alternatively, the determination of an individual or household when a touchpoint is received. A persistent linkage of digital touchpoints to individuals or households communicates with an entity resolution system executing at the node cluster to track data pertaining to entities, allowing the coordination of touchpoints with such individual consumers or households over time.

Distributed node cluster for establishing a digital touchpoint across multiple devices on a digital communications network

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.

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Conway, AR, United States of America

Pavan Roy Marupally