Palantir Technologies Inc.

Nicolas Prettejohn

Harkirat Singh

Matthew Julius Wilson

Ben Regev

Richard Deitch

Alexander Maass

Eugene Mak

Juan Jimenez Puig

Jorien Van Den Bergh

Stephen Heitkamp

Maciej Biskupiak

Rootul Patel

Duncan Hoffman

Marissa Miracolo

Mihai Condur

Katherine Ketsdever

Johannes Beil

Paul Baseotto

Yevhen Shevchuk

Joshua Aschheim

Kai Kamberger

Elliot Hirsch

Elise Norman

Lauren Brown

Patrick Walter

Elizabeth Patitucci

Craig Massie

Simon Slowik

Sam Stoll

Pieris Christofi

Alexander Bell-Thomas

Basil Jennings

Renee Leatherman

Louis Mosley

Alexandra Greehy

Helena Kertesz

A computer system is disclosed that provides purpose-based access to electronic data assets. For example, the computer system may perform operations including: receiving, from a first user, a request to access data assets associated with a purpose object; in response to receiving the request from the first user: generating a purpose access request object including at least an identification of the first user and an identification of the purpose object; and providing an indication of the purpose access request object to a second user associated with the purpose object; receiving, from the second user, an approval of the request; and in response to receiving the approval of the request from the second user: updating the purpose access request object to include at least an indication of the approval of the request; and granting the first user access to data assets associated with the purpose object.

Controlling access to electronic data assets

A computer-implemented method for efficiently performing a database join in a distributed data processing system comprising multiple computational nodes, the method comprising determining a first set of one or more columns of a first database table and a second set of one or more columns of a second database table on which the join is to be performed; estimating a size of the rows of the first table which have a particular combination of values in the first set of columns; computing a salt factor n based on the estimated size of rows and further based on a processing capacity of a computational node of the distributed data processing system; assigning one of n different salt values to each row of the first table having the particular combination of values in the first set of columns; for each row of the second table having the particular combination of values in the second set of columns into n rows, expanding the row into n row, and assigning to each expanded row a different one of the n salt values; and performing a join operation on the modified first and second tables, wherein the rows of the first and second tables have the same combination of values in the first and second sets of columns and the same salt value are joined on the same computational node.

Performing database joins in distributed data processing systems

A fuzzy matching system matching data records in one or more data sets based on user-customized selection of multiple fuzzy matching algorithms. Possible matches may be displayed to a user, who provides feedback on the accuracy of the matches, which may then be used by a machine learning algorithm to update weightings and parameters of the multiple fuzzy matching algorithms, such as based on machine learning analysis of the matching results and the user feedback.

Entity matching with machine learning fuzzy logic

A computer system provides shared access to electronic data assets. The system may perform operations including: receiving, from a first user, a request to access a shared data asset, wherein: the shared data asset is associated with a shared data asset object, and the shared data asset object identifies at least a second user authorized to approve sharing of the shared data asset; in response to receiving the request from the first user: generating a data access request object including at least an identification of the first user and an identification of the shared data asset object; and providing an indication of the data access request object to the second user associated with the shared data asset object; receiving, from the second user, an approval of the request; and in response to receiving the approval of the request from the second user: granting the first user access to the shared data asset associated with the shared data asset object.

Data asset sharing

A customer risk trigger associated with a customer may be identified. A response to the customer risk trigger may be detected. First risk analysis data related to the customer risk trigger may be gathered, based on the response, from a first datastore. Second risk analysis data related to the customer risk trigger may be gathered, based on the response, from a second datastore. A customer risk profile to model risk attribute(s) of the customer may be gathered. The risk attributes may represent a risk correlation between the customer and a prohibited act. Customer risk visualization tool(s) configured to facilitate visual user interaction with the customer risk profile may be gathered. The customer risk visualization tools may be rendered in a display of the computing system. The customer risk visualization tools provide a customer-centric view of risk for various applications, including anti-money laundering applications.

Automated risk visualization using customer-centric data analysis

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Bath, United Kingdom

Nicolas Prettejohn