Palantir Technologies Inc.

Michael Harris

Parvathy Menon

Adam Borochoff

Michael Kross

Matthew Sprague

David Cohen

Harkirat Singh

Jack Grossman

Jason Ma

Ilya Nepomnyashchiy

Bing Jie Fu

Steven Berler

Alex Smaliy

Julia Boortz

James Thompson

Mark Elliot

Hannah Korus

Lynn Cuthriell

Brian Schimpf

Gustav Brodman

Brian Toth

Jacob Meacham

Brendan Weickert

Jonathan Hsiao

Evelyn Nguyen

Michael Garland

John Antonio Carrino

Geoff Stowe

Geoffrey Stowe

Eric Wong

Stefan Bach

Allen Chang

John Kenneth Garrod

Stephen Freiberg

Ryan Zheng

Bobby Prochnow

Danielle Kramer

Jeff Wang

Christopher Male

Dragos-Florian Ristache

Alexander Visbal

Jacob Albertson

Christopher Rogers

Daniel Campos

Trevor Austin

Systems and methods including a framework for migration of live data. The method may comprised, by one or more hardware processors executing program instructions, receiving, at a migration proxy of the framework, code for reading data and writing data compatible with each of a plurality of states of a migration of data in a data store, wherein a service is at least intermittently reading data from and writing data to the data store; determining, by a migration runner of the framework, to perform the migration of the data; initiating, by the migration runner, the migration of the data, wherein the migration comprises a plurality of stages; storing, as the migration progresses through the plurality of stages, and at a migration data store of the framework, a current stage of the migration; and during the migration, using the migration proxy to read data from and write data to the data store.

Framework for live data migration

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

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

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.

A history preserving data pipeline computer system and method. In one aspect, the history preserving data pipeline system provides immutable and versioned datasets. Because datasets are immutable and versioned, the system makes it possible to determine the data in a dataset at a point in time in the past, even if that data is no longer in the current version of the dataset.

Universal data pipeline

Techniques are disclosed for prioritizing a plurality of clusters. Prioritizing clusters may generally include identifying a scoring strategy for prioritizing the plurality of clusters. Each cluster is generated from a seed and stores a collection of data retrieved using the seed. For each cluster, elements of the collection of data stored by the cluster are evaluated according to the scoring strategy and a score is assigned to the cluster based on the evaluation. The clusters may be ranked according to the respective scores assigned to the plurality of clusters. The collection of data stored by each cluster may include financial data evaluated by the scoring strategy for a risk of fraud. The score assigned to each cluster may correspond to an amount at risk.

Generating data clusters

A fair scheduling system with methodology for scheduling queries for execution by a database management system in a fair manner. The system obtains query jobs for execution by the database management system and cost estimates to execute the query jobs. Based on the cost estimates, the system causes the database management system to execute the query jobs as separate sub-query tasks in a round-robin fashion. By doing so, the execution latency of low cost query jobs that return few results is reduced when the query jobs are concurrently executed with high cost query jobs that return many results.

Fair scheduling for mixed-query loads

External malware data item clustering and analysis

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

A method and apparatus for a data analysis system for analyzing data object collections that include geo-temporal data is provided. One or more temporal granularities are specified for the purpose of generating a geo-temporal data index. The time granularities correspond to temporal ranges expected to correspond to temporal ranges specified in user queries against the data. One or more temporal index bucket groups are generated based on to the specified time granularities. Geo-temporal input data is indexed based on the generated temporal index bucket groups. The system allows a data analyst to specify geo-temporal queries that include both geospatial component and a temporal component. The system transforms geo-temporal queries into one or more second queries that retrieve data items based on the temporal index bucket groups.

Geo-temporal indexing and searching

Techniques are disclosed for for prioritizing a plurality of clusters. Prioritizing clusters may generally include identifying a scoring strategy for prioritizing the plurality of clusters. Each cluster is generated from a seed and stores a collection of data retrieved using the seed. For each cluster, elements of the collection of data stored by the cluster are evaluated according to the scoring strategy and a score is assigned to the cluster based on the evaluation. The clusters may be ranked according to the respective scores assigned to the plurality of clusters. The collection of data stored by each cluster may include financial data evaluated by the scoring strategy for a risk of fraud. The score assigned to each cluster may correspond to an amount at risk.

Growing community of inventors

Palo Alto, CA, United States of America

Michael Harris