Palantir Technologies Inc.

Geoff Stowe

Ankit Shankar

Parvathy Menon

Andrew Ash

Adam Anderson

Drew Dennison

Spencer Tank

Casey Patton

Arthur Thouzeau

Di Wu

Adam Borochoff

John Kenneth Garrod

Michael Harris

Benjamin Duffield

Michael Kross

Daniel Erenrich

Allen Cai

Matthew Sprague

Harkirat Singh

Bianca Rahill-Marier

Eli Bingham

Jacob Scott

Anirvan Mukherjee

Feridun Arda Kara

Brian Ngo

Zachary Bush

Ben Duffield

Greg Martin

Thomas Petracca

Diane Wu

William Dwyer

Winnie Chai

Engin Ural

Sean Kelley

Ted Mabrey

Drausin Wulsin

Gilad Gray

Greg Cohan

Chris Dorsey

Dennis Kwon

Chris Lewis

Asli Ozyar

Mike Reilly

Matt Sills

Spencer Stamats

Samir Talwar

Holt Wilkins

John McRaven

Stefan Bach

Joyce Yu-Hsin Chen

Bar Kaya

Lucas Lemanowicz

Mikhail Proniushkin

Andrew Pettit

Benedict Cappellacci

Sarah Gershkon

Brendan Weickert

Patricio Jones Velez

Bianca Rahil-Marier

Yu-Hsin Joyce Chen

Baris Kaya

P J Valez

Pj Valez

Chris Rogers

Kimberly Sparling

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

A computer system identifies malicious Uniform Resource Locator (URL) data items from a plurality of unscreened data items that have not been previously identified as associated with malicious URLs. The system can execute a number of pre-filters to identify a subset of URLs in the plurality of data items that are likely to be malicious. A scoring processor can score the subset of URLs based on a plurality of input vectors using a suitable machine learning model. Optionally, the system can execute one or more post-filters on the score data to identify data items of interest. Such data items can be fed back into the system to improve machine learning or can be used to provide a notification that a particular resource within a local network is infected with malicious software.

Malicious software detection in a computing system

An interactive, graph-based user interaction data analysis system is disclosed. The system is configured to provide analysis and graphical visualizations of user interaction data to a system operator. In various embodiments, interactive visualizations and analyses provided by the system may be based on user interaction data aggregated across particular groups of users, across particular time frames, and/or from particular computer-based platforms and/or applications. According to various embodiments, the system may enable insights into, for example, user interaction patterns and/or ways to optimize for desired user interactions, among others. In an embodiment, the system allows an operator to analyze and investigate user interactions with content provided via one or more computer-based platforms, software applications, and/or software application editions.

Presentation and analysis of user interaction data

Embodiments of the present disclosure relate to a computer system and interactive user interfaces configured to enable efficient and rapid access to multiple different data sources simultaneously, and by an unskilled user. The unskilled user may provide simple and intuitive search terms to the system, and the system may thereby automatically query multiple related data sources of different types and present results to the user. Data sources in the system may be efficiently interrelated with one another by way of a mathematical graph in which nodes represent data sources and/or portions of data sources (for example, database tables), and edges represent relationships among the data sources and/or portions of data sources. For example, edges may indicate relationships between particular rows and/or columns of various tables. The table graph enables a compact and memory efficient storage of relationships among various disparate data sources.

Systems and user interfaces for dynamic and interactive simultaneous querying of multiple data stores

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.

Systems and user interfaces for dynamic and interactive investigation based on automatic clustering of related data in various data structures

Systems and methods are provided for analyzing entity performance. In one implementation, a method is provided that includes recognizing an identifier associated with an entity and accessing a data structure comprising information associated with a plurality of interactions. The method also comprises identifying one or more interactions of the plurality of interactions based on the recognized identifier. The method further comprises processing the information of the identified interactions to analyze a performance of the entity and providing the processed information to display the performance of the entity on a user interface.

Methods and systems for analyzing entity performance

An interactive, graph-based user interaction data analysis system is disclosed. The system is configured to provide analysis and graphical visualizations of user interaction data to a system operator. In various embodiments, interactive visualizations and analyzes provided by the system may be based on user interaction data aggregated across particular groups of users, across particular time frames, and/or from particular computer-based platforms and/or applications. According to various embodiments, the system may enable insights into, for example, user interaction patterns and/or ways to optimize for desired user interactions, among others. In an embodiment, the system allows an operator to analyze and investigate user interactions with content provided via one or more computer-based platforms, software applications, and/or software application editions.

Systems and methods are provided for analyzing entity performance. In one implementation, a method is provided that includes receiving a request with one or more filter selections and accessing a data structure comprising a plurality of categories of information showing interactions associated with multiple entities. The method also comprises identifying a set of categories of the plurality of categories within the data structure based on the one or more filter selections. The method further comprises processing the information of the identified categories to analyze a performance of one or more entities of the multiple entities in accordance with the one or more filter selections and providing the processed information to display the performance of the one or more entities on a user interface.

Systems and methods are provided for analyzing entity performance. In one implementation, a method is provided that includes accessing a data structure comprising a plurality of interactions associated with multiple entities. The method also includes evaluating one or more interactions of the plurality of interactions associated with a consuming entity of the multiple entities. The method further includes determining whether the one or more interactions associated with the consuming entity comprise an identified location information of the consuming entity.

Aspects of the present disclosure relate to graphical representation of an output. A server provides for presentation, at a display device, of a first graphical representation of an output for a class. The server receives a selection of a time range for the class. The server determines, based on the output for the class during the selected time range, a baseline output. The server provides for presentation, in response to the selection of the time range, of a second graphical representation of the output, during the time range, for a family from the class, the second graphical representation indicating a difference between the output for the family and an output value for the family predicted based on the baseline output.

Graphical representation of an output

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San Francisco, CA, United States of America

Geoff Stowe