Palantir Technologies Inc.

Joanna Peller

Anshuman Prasad

Peter Maag

Thomas Powell

Spencer Tank

Ezra Spiro

Andre Frederico Cavalheiro Menck

Arthur Thouzeau

Charles Shepherd

Caroline Henry

Jennifer Yip

Marco Diciolla

Matthew Todd

Ankit Shankar

Casey Patton

Rahij Ramsharan

Andrew Ash

Arjun Mathur

Thomas Mathew

Andy Chen

Mitchell Skiles

Jonathan Victor

Sri Krishna Vempati

Samuel Sinensky

Nikhil Taneja

Christopher Wynnyk

Christopher Rogers

Scott Adams

Ryan Norris

Anuraag Bahl

Vinoo Ganesh

Aydin Keskin

Mackenzie Bohannon

Viktor Nordling

Kelvin Lau

Anton Apostolatos

Francisco Ferreira

Adam Lieskovský

Florian Diegruber

Joseph Kane

Mikael Ibrahim Mofarrej

Philipp Hoefer

Spencer McCollester

Max-Philipp Schrader

Maciej Laska

A computer-implemented method enforces data security constraints in a data pipeline. The data pipeline takes one or more source datasets as input and performs one or more data transformations on them. The method includes using data defining one or more data security constraints to configure the data pipeline to perform a data transformation on a restricted subset of entries of the source datasets. The restriction is defined by the data defining one or more data security constraints. The method further includes performing the data transformation according to the configuration to produce one or more transformed datasets. The method further includes using the data defining one or more data security constraints to perform a verification on one or more of the transformed datasets to ensure that entries in the one or more of the transformed datasets are restricted as defined by the one or more data security constraints.

Enforcing data security constraints in a data pipeline

A system for troubleshooting network problems is disclosed. A model can use demographic information, network usage information, and network membership information to determine an importance of a problem. The importance of the problem for the user who reported the problem, a number of other users affected by the problem, and the importance of the problem to the other users can be used to determine a priority for resolving the problem. Before and after a work order is executed to resolve the problem, network metrics can be gathered, including aggregate network metrics, and automatically presented in various user interfaces. The analysis of the metrics can be used to update a database of which work orders are assigned in response to which problems.

Computer network troubleshooting

Systems, methods, non-transitory computer readable media can be configured to access a plurality of sensor logs corresponding to a first machine, each sensor log spanning at least a first period; access first computer readable logs corresponding to the first machine, each computer readable log spanning at least the first period, the computer readable logs comprising a maintenance log comprising a plurality of maintenance task objects, each maintenance task object comprising a time and a maintenance task type; determine a set of statistical metrics derived from the sensor logs; determine a set of log metrics derived from the computer readable logs; and determine, using a risk model that receives the statistical metrics and log metrics as inputs, fault probabilities or risk scores indicative of one or more fault types occurring in the first machine within a second period.

Machine fault modelling

Systems, methods, non-transitory computer readable media can be configured to accessing a target sensor log corresponding to a first machine; accessing one or more prior sensor logs corresponding to the first machine and one or more prior sensor logs corresponding to a plurality of second machines which are of the same type as the first machine; accessing a plurality of computer readable logs corresponding to the first machine and the second machines, the computer readable logs for each second machine comprising a maintenance log comprising a plurality of maintenance task objects, each maintenance task object comprising a time and a maintenance task type; determining a set of statistical metrics characterising a difference between the target sensor log and each prior sensor log; selecting a sub-set of the prior sensor logs in dependence upon the statistical metrics; analysing the maintenance logs to correlate each prior sensor log included in the subset to one or more correlated maintenance tasks; selecting a priority maintenance task based on the sub-set of prior sensor logs, t and the correlated maintenance tasks; and outputting the priority maintenance task.

Determining maintenance for a machine

Systems, methods, non-transitory computer readable media can be configured to access a plurality of sensor logs corresponding to a first machine, each sensor log spanning at least a first period. Access first computer readable logs corresponding to the first machine, each computer readable log spanning at least the first period, the computer readable logs comprising a maintenance log comprising a plurality of maintenance task objects, each maintenance task object comprising a time and a maintenance task type. Determine a set of statistical metrics derived from the sensor logs; determine a set of log metrics derived from the computer readable logs. Determine, using a risk model that receives the statistical metrics and log metrics as inputs, fault probabilities or risk scores indicative of one or more fault types occurring in the first machine within a second period.

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London, United Kingdom

Joanna Peller