Palantir Technologies Inc.

Daniel Campos

Parvathy Menon

Adam Borochoff

Michael Harris

Michael Kross

Matthew Sprague

Christopher I Walker

Alexander Visbal

Jacob Albertson

Brandon Krieger

Christopher Rogers

Anuraag Bahl

Elisa Castaner

Samuel Tarng

Charles Guetta

Trevor Austin

Methods, systems and apparatus, including computer programs encoded on computer storage media for a data record resolution and correlation system. One of the methods includes obtaining data sets each including records associated with entities, the records each specifying information for a plurality of aspects of an entity, the information being encoded in the records, for each of the data sets, according to a respective format. One or more bundles are determined from the data sets, with each bundle indicating records determined to be associated with a same entity and determined using similarity rules that compare aspects specified in the records. Information describing a particular bundle is presented to a reviewing user, and the reviewing user can specify one or more records included in the particular bundle that are properly matched. The particular bundle can be updated based on the reviewing user's specification.

Data record resolution and correlation system

In various embodiments, systems, methods, and techniques are disclosed for generating a collection of clusters of related data from a seed. Seeds may be generated based on seed generation strategies or rules. Clusters may be generated by, for example, retrieving a seed, adding the seed to a first cluster, retrieving a clustering strategy or rules, and adding related data and/or data entities to the cluster based on the clustering strategy. Various cluster scores may be generated based on attributes of data in a given cluster. Further, cluster metascores may be generated based on various cluster scores associated with a cluster. Clusters may be ranked based on cluster metascores. Various embodiments may enable an analyst to discover various insights related to data clusters, and may be applicable to various tasks including, for example, tax fraud detection, beaconing malware detection, malware user-agent detection, and/or activity trend detection, among various others.

Tax data clustering

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Washington, DC, United States of America

Daniel Campos