Palantir Technologies Inc.

Spencer Tank

John McRaven

Arthur Thouzeau

Shuyang Li

Ezra Spiro

Xavier Falco

Francis Chen

Ankit Shankar

Peter Maag

Thomas Powell

Geoff Stowe

Andre Frederico Cavalheiro Menck

Anshuman Prasad

Joanna Peller

Charles Shepherd

Matthew Todd

Caroline Henry

Jennifer Yip

Marco Diciolla

Bianca Rahill-Marier

Casey Patton

Juan Ricafort

Michael Glazer

Aakash Goenka

Glenn Sheasby

John Boreiko

Lucas Ray

Eliot Ball

Timothy Yousaf

Mikhail Proniushkin

Drew Dennison

Paul Thoren

Khoa Pham

Jeffrey Tsui

Xinyi Wang

Michal Duczynski

Benedict Cappellacci

Sebastiaan Visser

Issei Nino

Matthew Rauen

Sorin-Alexandru Nutu

Bianca Rahil-Marier

John Mcraven

Methods and systems for generating and analyzing visualizations based on a group of sets of data objects. One system includes processors executing instructions to present the sets of data objects in a selectable format on a display device, receive a user selection of a first set of data objects, generate a user interface comprising an indication of the first set of data objects and a plurality of selectable tools to generate a first data visualization of the first set of objects from one or more operations to the first set of objects, receive a user selection of a second set of data objects, receive a user selection to cause the application of the one or more operations to the second set of data objects, and update the user interface to comprise a second visualization based on the one or more operations performed on the second set of data objects.

Object-centric data analysis system and associated graphical user interfaces

Methods and systems for presenting time series for analysis. A method may receive a first input defining a metric that indicates a relationship between a first and a second time series that are each associated with at least a first data object of a plurality of data objects, generate a first plot depicting the metric as determined from the first and the second time series, receive, via the user interface, a second input of a selection of a second data object of the plurality of data objects, determine, via an ontology, a relationship of the second data object with a third and a fourth time series that, respectively, are associated with series types that match series types associated with the first and the second time series, and generate and display, in the user interface, a second plot depicting the metric as determined from the third and the fourth time series.

Ontology-based time series visualization and analysis

Methods and systems for structuring, storing and displaying time series data in a user interface. One system includes processors executing instructions to determine, from time series data from a first sensor, a first subset of time series data for the first batch from the first start time and the first end time, determine, from the time series data from the first sensor, a second subset of time series data for the second batch from the second start time and the second end time, generate a time series user interface comprising a chart, the chart including a first plot for the first subset of time series data and a second plot for the second subset of time series data, the first plot being aligned to the second plot, and cause presentation of the time series user interface.

Object time series system

Systems and methods are provided for storing data representing respective sub-elements of a complex task. Data representing one or more links between two or more sub-elements is stored, the links indicating a dependency between said sub-elements. A work order is calculated based on the identified links. A graphical representation of the calculated work order which indicates said sub-elements and their dependencies is provided. The links may indicate a temporal dependency of a second sub-element on a first sub-element and in which the provided graphical representation presents the temporal relationship of the sub-elements. Historical data may be received for association with one or more selected links or sub-elements, the historical data related to a prior event and which affects the temporal relationship between the sub-elements. An updated work order modified by the historical data may be calculated. An updated graphical representation of the work order may be provided.

Graphical representation of a complex task

Embodiments of the present disclosure relate to a data analysis system that may receive data comprising a plurality of raw data items from one or more data sources, such as a monitoring agent located in a monitored network. The received data may be scored using one or more scoring rules and/or algorithms, with raw data items satisfying a score threshold designated as 'data item leads.' Raw data items associated with a data item lead may be searched and displayed to the user via an interactive user interface. The data analysis system may be used to execute searches and additional enrichments against the received raw data items. The data analysis system may group received raw data items based upon shared attribute values. The data analysis system may be used to categorize received data and construct timelines, histograms, and/or other visualizations based upon the various attributes of the raw data items.

Systems and interactive user interfaces for dynamic retrieval, analysis, and triage of data items

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 and techniques for searching multiple data sources are described herein. Users may specify searches of multiple data sources to occur on a periodic basis. The searches may be configured to search time or date ranges that have not previously been searched. A user may select the data sources of interest and specify search terms, review and edit previously created searches, and review results of searches. The system automatically performs the specified searches, and notifies the user and/or a team of the user each time new results are found. The system may efficiently search the data sources by storing previous search results and comparing the previous results to current search results to identify new search results.

Periodic database search manager for multiple data sources

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

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New York, NY, United States of America

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