Palantir Technologies Inc.

Benjamin Duffield

Mark Elliot

Adam Borochoff

Samantha Woodward

Jared Newman

David Tobin

Robert Fink

Ankit Shankar

James Baker

Timothy Wilson

Thomas Powell

Greg DeArment

Geoff Stowe

Derek Cicerone

Bradley Moylan

Matthew MacLean

Ashwin Ramaswamy

Javier Campanini

Brian Schimpf

Andrew Ash

Neil Rickards

Thomas Petracca

Jacob Meacham

Jason Zhao

Savino Sguera

Nathan Ziebart

Marco Gelmi

Willi Raschkowski

Qinfeng Chen

John McRaven

Xavier Falco

Steven Fackler

Matthew Sills

Aditya Shashi

Andrew Higgins

Mihir Pandya

Pawel Adamowicz

Hasan Dincel

Stephen Nicholas Barton

Punyashloka Biswal

Robert Kruszewski

Mark Schafer

Walker Burgin

Matthew Lynch

Gregory O'Connor

Caitlin Colgrove

Sander Kromwijk

Rahij Ramsharan

Stephanie Yu

Gabrielle Javitt

Alexander Martino

Brandon Krieger

Alexandru Antihi

Nir Ackner

Gokcan Ozakdag

Austin Czarnecki

Samuel Szuflita

Grace Wang

Phillip Chen

Diran Li

Natacha Gabbamonte

Helen Kaltegaertner

Joshua Casale

Aditya Chaturvedi

Shannon Scott

Maria Franco

Jimin Song

A method may comprise receiving a query for performing one or more computational operations on one or more multi-dimensional data sets representing multi-dimensional time series data collected in real-time from one or more sensors associated with one or more technical systems. The method may also comprise identifying the location of the one or more multi-dimensional time series data sets in one or more databases, retrieving the one or more multi-dimensional time series data sets from the identified one or more databases, and performing the one or more computational operations on the retrieved one or more multi-dimensional time series data sets. The method may also comprise generating output based on the result of the one or more computational operations indicative of one or more states of the one or more technical systems with respect to time.

Multi-dimensional time series datasets

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

Systems and methods are provided for enhanced processing of time series data via parallelization of instructions. An example method includes receiving a query indicating time series datasets and operations to be performed on the time series datasets. Nodes associated with the query are identified, with each node associated with a time series dataset. Nodes associated with operations to be performed are generated. The nodes are assembled into query tree, with parent nodes of the query tree indicating operations that are to be applied to children nodes. Instructions for processing the query tree are generated. At least a subset of the instructions is provided to one or more compute systems for processing in parallel. Results are received, and presented in a user interface.

Enhanced processing of time series data via parallelization of instructions

A method, performed by one or more processors, may comprise receiving a query for performing one or more computational operations on one or more multi-dimensional data sets representing multi-dimensional time series data collected in real-time from one or more sensors associated with one or more technical systems. The method may also comprise identifying the location of the one or more multi-dimensional time series data sets in one or more databases, retrieving the one or more multi-dimensional time series data sets from the identified one or more databases, and performing the one or more computational operations on the retrieved one or more multi-dimensional time series data sets. The method may also comprise generating output based on the result of the one or more computational operations indicative of one or more states of the one or more technical systems with respect to time.

Querying multi-dimensional time series data sets

Computing systems methods, and non-transitory storage media are provided for receiving a monitoring request. The monitoring request includes one or more entities or attributes to be monitored, one or more rules to be evaluated with respect to the entities or attributes, and one or more downstream actions to be selectively triggered based on the evaluation. Next, data regarding the entities or the attributes is obtained. Next, a log is generated. The log includes changes or updates, relative to a previous iteration, of the entities or the attributes. The changes or updates correspond to the rules. Next, the changes or the updates are evaluated against the one or more rules and based on the log. Next, one or more actions are selectively implemented based on the evaluation of the changes or the updates.

Monitoring and alerting mechanisms

Various systems and methods are described herein for an improved spreadsheet application that allows a user to generate, manipulate, and replicate data visualizations (e.g., sparklines, graphs, charts, etc.) using functions without importing data into cells of the application. For example, data is stored in one or more remote or local data stores accessible to the improved spreadsheet application. A user enters a function into a cell of the improved spreadsheet application. The improved spreadsheet application generates a query using the function, the query identifying a portion of a dataset to retrieve from the data store(s). The improved spreadsheet application then transmits the query to the data store(s) and retrieves the requested data. A renderer of the improved spreadsheet application then renders a sparkline using the retrieved data. The improved spreadsheet application displays the rendered sparkline in the cell in which the function was entered, or at another designated location.

Query-based time-series data display and processing system

In various example embodiments, a comparative modeling system is configured to receive selections of a data set, a transform scheme, and one or more machine-learning algorithms. In response to a selection of the one or more machine-learning algorithms, the comparative modeling system determines parameters within the one or more machine-learning algorithms. The comparative modeling system generates a plurality of models for the one or more machine-learning algorithms, determines comparison metric values for the plurality of models, and causes presentation of the comparison metric values for the plurality of models.

User interface based variable machine modeling

A method and system for serving assets is disclosed, comprising receiving an asset request to serve an asset, wherein the asset request originates at an application, and wherein the asset request comprises an advertisement of an asset to be served and a request for the network address of an asset server configured to serve the requested asset. The method further comprises causing a service discovery server to identify an asset server configured to serve the requested asset, and causing the requested asset to be served to the application.

Serving assets in a networked environment

A database system comprised of a decoupled compute layer and storage layer is implemented to store, build, and maintain a canonical dataset, a temporary buffer, and projection datasets. The canonical dataset is a set of batch updated data. The data is appended in chunks to the canonical dataset such that the canonical dataset becomes a historical dataset over time. The buffer is a write ahead log that contains the most recent chunks of data and provides atomicity and durability for the database system. The projection datasets are indexes of the canonical dataset and/or the buffer that may have single or multiple column sort-orders and/or particular data formats. The writes to the canonical dataset, projection datasets, and buffer may be asynchronous and therefore the database system is advantageously less resource constrained.

Projections for big database systems

Aspects of the present disclosure include a system comprising a computer-readable storage medium storing at least one program and a method for managing access permissions associated with data resources. Example embodiments involve evaluating user access permissions with respect to shared data resources of a group of network applications. The method includes receiving a request to access a data resource. The method further includes accessing a policy object linked to the data resource that includes an effective policy for the data resource. The method further includes evaluating a user's access permissions with respect to the data resource based on the policy object and communicating a response to the network application that includes the access permission of the user.

Network-based permissioning system

Systems and methods are provided for improved time series databases and time series operations. A time series service responds to requests from external devices. The external devices request time series data and submit time series queries. The time series service generates planned and efficient time series queries from the initial queries. The time series service performs operations such as unit conversion, interpolation, and performing operations on time series data. The time series service can identify which time series database to query from and/or cause data to be populated into a time series database from a data pipeline system.

Time series database processing system

Growing community of inventors

New York, NY, United States of America

Benjamin Duffield