The Dun Bradstreet Corporation

Robin Fry Davies

Anthony J Scriffignano

Michael Klein

Sandra Stoker

Warwick Matthews

Thang Q Hoang

John Mark Nicodemo

Barry Rizzolo

Yiem Sunbhanich

Michael R Young

Vindra Rampaul

Laurie Shimer

Anjali Reddi

David A Spingarn

There is provided a method that includes (a) receiving data from a data source, (b) attributing the data source in accordance with rules, thus yielding an attribute, (c) analyzing the data to identify a confounding characteristic in the data, (d) calculating a qualitative measure of the attribute, thus yielding a weighted attribute, (e) calculating a qualitative measure of the confounding characteristic, thus yielding a weighted confounding characteristic, (f) analyzing the weighted attribute and the weighted confounding characteristic, to produce a disposition, (g) filtering the data in accordance with the disposition, thus yielding extracted data, and (h) transmitting the extracted data to a downstream process. There is also provided a system that executes the method, and a storage device that contains instructions for controlling a processor to perform the method.

System and process for analyzing, qualifying and ingesting sources of unstructured data via empirical attribution

A multidimensional recursive and self-perfecting process used to discover dyadic or multi-counterparty relationships between parties, the process comprising: (a) collecting information from a plurality of data sources; (b) discovering dyadic or multi-counterparty relationships between the parties from the collected information; (c) clustering the parties to infer the dyadic or multi-counterparty relationships between the parties based on common or partially intersecting attributes between the parties, thereby forming clustered parties; (d) evaluating the clustered parties for business linkage potential by integrating the collected information and contextually assessing indicia from the data sources to detect and measure consistency and inconsistency for a given party or dyadic or multi-counterparty relationship; (e) positing and evaluating relationship type and role said party plays in each relationship; and (f) assessing the confidence level regarding the likelihood that the dyadic or multi-counterparty relationship exists between the parties.

Multidimensional recursive learning process and system used to discover complex dyadic or multiple counterparty relationships

A system and a method used for data discovery in accordance with an inquiry in which multiple sources, which may be web sites or other data sources, are examined for data relevant to the inquiry. The process and method is performed recursively an indeterminate number of iterations, using data and metadata from multiple sources to corroborate discovered data and metadata from other sources, until no further relevant data or sources are found, or adjudication or exception rules have been met. Discovered data and metadata are curated, adjudicated to assess reliability, synthesized, and clustered into composite records using precedence rules and provenance to determine the most reliable data sources as well as terms of use for each source. Data, metadata, and information about each search are retained and can be used for subsequent purposes, such as subsequent searches or other downstream activities.

System and method for recursively traversing the internet and other sources to identify, gather, curate, adjudicate, and qualify business identity and related data

A corporate linkage system and method provides customers with complete, accurate, and timely lists of corporate linkage locations, including monitoring company lists for changes to location lists, matching stored locations to a company or third party list of locations, providing detailed coding on matches, and batch updates to global files.

Method and system for linking business entities using unique identifiers

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Plymouth, MI, United States of America

Robin Fry Davies