Palantir Technologies Inc.

James Thompson

Joseph Rafidi

Patrick Szmucer

David Cohen

Parvathy Menon

Adam Borochoff

Michael Harris

Jack Grossman

Michael Kross

Jason Ma

Matthew Sprague

Ilya Nepomnyashchiy

Bing Jie Fu

Steven Berler

Julia Boortz

Alex Smaliy

Ammaar Reshi

Jeppe Hallgren

A resource dependency system may track data dependencies and data transformations for individual columns of the data sets over the span of the data pipeline (referred to as a provenance or lineage of a column). Column provenance/lineage can be logged using metadata or graph-like data structures, which the resource dependency system can generate, store, manage, and access. Column provenance/lineage can be used to generate user interfaces displaying visual node graphs with columns as nodes and the data dependencies and data transformations associated with the columns as edges between the nodes.

Column lineage for resource dependency system and graphical user interface

A resource dependency system and its associated user interfaces, used for tracking data dependencies and data transformations between resources, may display visual node graphs with resources as nodes and the data dependencies and data transformations associated with the columns as edges between the nodes. The nodes representing the resources may be displayed differently based on relevant differences in the resources they represent, which can be set through various selectable criteria and schemes. The user interfaces may include selectable options for visually arranging the nodes or grouping the nodes into superseding nodes according to how the nodes are displayed or the relevant differences in the resources they represent. Updated properties and data dependencies associated with superseding nodes can be presented to the user.

Resource grouping for resource dependency system and graphical user interface

A method of enabling propagated deletion in a distributed database system comprises receiving a request to delete data in a distributed database system; causing a display of a relevant dataset and a switch between applying propagated deletion or not; receiving a first selection of a subset of records from the relevant dataset using one or more filter functions and a second selection of applying propagated deletion to the subset of records; and applying propagated deletion to the subset of records to generate a new dataset.

Propagated deletion of database records and derived data

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

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London, United Kingdom

James Thompson