Palantir Technologies Inc.

Roger Hu

Rahul Kumar Agarwal

Matthew Elkherj

Daniel Erenrich

Lawrence Manning

Xavier Falco

Eli Bingham

Yifei Huang

Daniel Fernandez

Guillem Palou Visa

Aleksandr Zamoshchin

Jeremy Elser

Rahul Mehta

Jason Prestinario

Clayton Sader

Rowan Gilmore

Nicholas Latourette

Mitchell Beard

Mark Schafer

Thomas Pearson

Joy Tao

Barry McCardel

Miklos Danka

Kelsey Falter

Laurynas Pliuskys

Boris Valensi

James Ong

Kai Shen

Sachi Shah

Howard Carter

Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.

Systems and methods for automatic clustering and canonical designation of related data in various data structures

Systems and techniques for indexing and/or querying a database are described herein. Multiple, large disparate data sources may be processed to cleanse and/or combine item data and/or item metadata. Further, attributes may be extracted from the item data sources. The interactive user interfaces allow a user to select one or more attributes and/or other parameters to present visualizations based on the processed data.

Systems and methods for attribute analysis of one or more databases

A system comprising a computer-readable storage medium storing at least one program and a method for determining, tracking, and anticipating risk in a manufacturing facility are presented. In example embodiments, the method includes generating a risk data model for the manufacturing facility based on correlations between historical staffing conditions of the manufacturing facility and deviations from existing manufacturing procedures. The method further includes receiving projected operational data that includes information related to anticipated future staffing conditions of the manufacturing facility. The method further includes calculating a risk score based on the projected operational data using the risk data model. The method further includes causing presentation of a user interface that includes a display of the risk score.

Quantifying, tracking, and anticipating risk at a manufacturing facility based on staffing conditions and textual descriptions of deviations

A system comprising a computer-readable storage medium storing at least one program and a method for determining, tracking, and anticipating risk in a manufacturing facility are presented. In example embodiments, the method includes obtaining operational data associated with a manufacturing facility. The method further includes accessing a risk data model corresponding to the manufacturing facility, and calculating a risk score based on the current operational data using the risk data model. The method further includes causing presentation of a user interface that includes a display of the risk score to provide a user with a measure of risk at the manufacturing facility.

Quantifying, tracking, and anticipating risk at a manufacturing facility, taking deviation type and staffing conditions into account

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New York, NY, United States of America

Roger Hu