Palantir Technologies Inc.

Adam Borochoff

Parvathy Menon

Michael Harris

Michael Kross

Matthew Sprague

Joseph Rafidi

Ryan Rowe

Matthew MacLean

Mark Elliot

Harkirat Singh

David Cohen

James Thompson

Jack Grossman

Jason Ma

Ilya Nepomnyashchiy

Matthew Jenny

Bing Jie Fu

Steven Berler

Julia Boortz

Alex Smaliy

Benjamin Duffield

Salar Al Khafaji

Tyler Uhlenkamp

Brendan Weickert

Hao Zuo

Robert Fink

Jared Newman

Punyashloka Biswal

Adam Anderson

Lynn Cuthriell

Catherine Lu

Karanveer Mohan

Michelle Guo

Ankit Shankar

Geoff Stowe

John Chakerian

Geoffrey Stowe

Ben Duffield

Arthur Wu

Omar Ali

Stefan Bach

Aditya Dahiya

Kamran Khan

Jenny Fan

Andrew Lee

Robert Kruszewski

Mark Schafer

Matthew Lynch

Walker Burgin

Alexander Visbal

Gregory O'Connor

Jacob Albertson

Caitlin Colgrove

Sander Kromwijk

Rahij Ramsharan

Matthew Sills

Stephanie Yu

Gabrielle Javitt

Brandon Krieger

Nir Ackner

Alexandru Antihi

Patrick Szmucer

Samuel Szuflita

Christopher Rogers

Phillip Chen

Diran Li

Helen Kaltegaertner

John Mathews

Katherine Ketsdever

Shannon Scott

Daniel Campos

Morten Telling

Trevor Austin

Maria Franco

Computing systems methods, and non-transitory storage media are provided for retrieving information regarding an operation to be performed by a platform, performing a preliminary validation of the operation, generating details regarding the preliminary validation, transmitting at least a subset of the details of the preliminary validation to the platform, and populating the generated details on an interface. If the preliminary validation fails, the platform refrains from performing the operation. Furthermore, the logic describing the operation can be executed on different platforms and is not bound or limited to one platform.

Approaches of augmenting and streamlining task execution

A method of determining and displaying metadata that represents provenance of columns in a data store comprises receiving query data expressed in a human-readable language and describing one or more transformations of data tables of a distributed database system or columns of the data tables; parsing the query data to create a data structure for unresolved transforms in which the data tables are not matched with the columns; based in part upon table schema metadata describing the columns of the data tables, creating an additional data structure for resolved transforms, the additional data structure matching the data tables with the columns and specifying inputs and outputs of each transformation of the one or more transformations; based on information in the additional data structure, creating and causing display of a visual graph of the columns of the data tables and relationships between the columns, relating to the one or more transformations.

Generation and graphical display of data transform provenance metadata

The systems and methods described herein provide highly dynamic and interactive data analysis user interfaces which enable data analysts to quickly and efficiently explore large volume data sources. The data analysis system enables the user to investigate large volumes of data over many different paths of analysis while maintaining detailed and retraceable steps taken by the user over the course of an investigation, as captured via the user's queries and user interaction with the user interfaces provided by the data analysis system. Data analysis paths may involve exploration of high volume data sets, such as Internet proxy data, which may include trillions of rows of data. The data analyst may pursue a data analysis path that involves, among other things, applying filters, joining to other tables in a database, viewing interactive data visualizations, and so on. The data analysis system enables a user to refresh or insert a node in a workflow. The data analysis system further provides data editing, reporting, enrichment, expression language, and charting functionality.

Interactive user interface for dynamically updating data and data analysis and query processing

A method comprises storing first transaction data for a first transaction producing a first version of a first dataset, second transaction data for a second transaction transforming a first version of the first dataset to a second version of the first dataset, and third transaction data for a third transaction transforming the second version of the first dataset to a third version of the first dataset; storing dependency information indicating a first dependency of the third transaction on the second transaction and a second dependency of the second transaction on the first transaction; receiving a first instruction to revoke a first permission of a first user to access the second version of the first dataset; automatically revoking a certain permission of the first user to access the third version of the first dataset based on the dependency information.

Data revision control in large-scale data analytic systems

Display screen or portion thereof with graphical user interface

A resource dependency system displays two dynamically interactive interfaces in a resource dependency user interface, a hierarchical resource repository and a dependency graph user interface. User interactions on each interface can dynamically update either interface. For example, a selection of a particular resource in the dependency graph user interface causes the system to update the dependency graph user interface to indicate the selection and also updates the hierarchical resource repository to navigate to the appropriate folder corresponding to the stored location of the selected resource. In another example, a selection of a particular resource in the hierarchical resource repository causes the system to update the hierarchical resource repository to indicate the selection and also updates the dependency graph user interface to display an updated graph, indicate the selection and, in some embodiments, focus on the selected resource by zooming into a portion of the graph.

Resource dependency system and graphical user interface

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.

Malware data clustering

Systems and methods are provided for creating and managing a data integration workspace. The workspace may comprise one or more views of data (or datasets) stored in or accessible by the system. Models may be generated and updated based on the plurality of datasets and presented via a graphical user interface. Feedback received via a graphical user interface presenting a model may be used to annotate an underlying dataset associated with the model. Responsive to a modification of the underlying dataset or the rules for using the underlying dataset to generate the model, other related datasets and/or models may be automatically updated accordingly. Templates associated with one or more types of users may be defined. Each template may comprise one or more specific models related to a specific type of user.

Systems and methods for creating and managing a data integration workspace

A method comprises creating metadata identifying columns of tables and column operations of one or more data transforms of the columns in a data pipeline and including links to code segments in human-readable form corresponding to the one or more data transforms; executing a build job that effects the one or more data transforms on one or more datasets to generate one or more derived datasets; causing, after the executing, a presentation of a graphical user interface (GUI) including a graphical representation of the one or more data transforms based on the metadata, wherein the method is performed by one or more processors.

Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, and provide results of the automated analysis in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a compact, human-readable analysis of the data clusters. The human-readable analyses (also referred to herein as 'summaries' or “conclusions”) of the data clusters may be organized into an interactive user interface so as to enable an analyst to quickly navigate among information associated with various data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation. Embodiments of the present disclosure also relate to automated scoring of the clustered data structures.

Phishing data item clustering and analysis

A computer-implemented system and method for data revision control in a large-scale data analytic systems. In one embodiment, for example, a computer-implemented method comprises the operations of storing a first version of a dataset that is derived by executing a first version of driver program associated with the dataset; and storing a first build catalog entry comprising an identifier of the first version of the dataset and comprising an identifier of the first version of the driver program.

Techniques for propagation of deletion operations among a plurality of related datasets are described herein. In an embodiment, a data processing method comprises, using a distributed database system that is programmed to manage a plurality of different raw datasets and a plurality of derived datasets that have been derived from the raw datasets based on a plurality of derivation relationships that link the raw datasets to the derived datasets: from a first dataset that is stored in the distributed database system, determining a subset of records that are candidates for propagated deletion of specified data values; determining one or more particular raw datasets that contain the subset of records; deleting the specified data values from the particular raw datasets; based on the plurality of derivation relationships and the particular raw datasets, identifying one or more particular derived datasets that have been derived from the particular raw datasets; generating and executing a build of the one or more particular derived datasets to result in creating and storing the one or more particular derived datasets without the specified data values that were deleted from the particular raw datasets; repeating the generating and executing for all derived datasets that have derivation relationships to the particular raw datasets; wherein the method is performed using one or more processors.

Growing community of inventors

New York, NY, United States of America

Adam Borochoff