Palantir Technologies Inc.

Xavier Falco

John McRaven

Francis Chen

Matthew Elkherj

Spencer Tank

Shuyang Li

Rahul Kumar Agarwal

Daniel Erenrich

Lawrence Manning

Eli Bingham

Yifei Huang

Roger Hu

Daniel Fernandez

Aleksandr Zamoshchin

Guillem Palou Visa

Jeremy Elser

Rahul Mehta

Clayton Sader

Nicholas Latourette

Rowan Gilmore

Jason Prestinario

Pedro Sanzovo

Anton Apostolatos

Miranda Luna

Sahiba Sachdeva

James Baker

Benjamin Duffield

David Tobin

Arseny Bogomolov

Steven Fackler

Amr Al Mallah

Aditya Shashi

Yixun Xu

Helen Yu

Pierre Cholet

Pawel Adamowicz

Giulio D'Ali' Aula

Andrew Ehrich

Samuel Flavio Barroso Souza

Mikhail Proniushkin

James Campbell

Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.

Systems and methods for automatic clustering and canonical designation of related data in various data structures

A system for resolving conflicts between data source updates and user edits to an ontology before applying the resulting changes to the ontology and related methods are disclosed. The system is programmed to receive data source updates from data sources and transform the data source updates to updates to ontology data. The system is also programmed to receive edits to ontology data from user accounts. The system is programmed to review these updates or edits to the ontology and resolve conflicts according to a predetermined strategy, such as prioritizing a user edit over a data source update. The resulting changes are incorporated to one or more databases where representations of the ontology data are stored.

Resolving conflicts between data source updates and user edits to ontology data

A system for enabling granular access control over ontology data and related methods are disclosed. The system is programmed to receive data source updates from data sources having respective sets of permissions for access control, transform the data source updates to changes to an ontology, and control access to the ontology based on the sets of permissions. The system is further programmed to receive a specification of one or more rules referencing attributes of user accounts or properties of ontology entities and corresponding one or more lists of permissions. The system is programmed to then enforce a security policy based on the specification to further control access to the ontology.

Fine-grained permission models for ontology data

Methods and systems for generating and analyzing visualizations based on a group of sets of data objects. One system includes processors executing instructions to present the sets of data objects in a selectable format on a display device, receive a user selection of a first set of data objects, generate a user interface comprising an indication of the first set of data objects and a plurality of selectable tools to generate a first data visualization of the first set of objects from one or more operations to the first set of objects, receive a user selection of a second set of data objects, receive a user selection to cause the application of the one or more operations to the second set of data objects, and update the user interface to comprise a second visualization based on the one or more operations performed on the second set of data objects.

Object-centric data analysis system and associated graphical user interfaces

Methods and systems for structuring, storing and displaying time series data in a user interface. One system includes processors executing instructions to determine, from time series data from a first sensor, a first subset of time series data for the first batch from the first start time and the first end time, determine, from the time series data from the first sensor, a second subset of time series data for the second batch from the second start time and the second end time, generate a time series user interface comprising a chart, the chart including a first plot for the first subset of time series data and a second plot for the second subset of time series data, the first plot being aligned to the second plot, and cause presentation of the time series user interface.

Object time series system

Methods and systems for presenting time series for analysis. A method includes presenting a first visualization of summary information for an initial data set of a plurality of batches, presenting a filtered data set of the initial data set having a first batch identifier associated with a first batch and the second batch identifier associated with a second batch, executing a time series connector including transmitting a request to a time series application, the request comprising the first batch identifier, the second batch identifier, and the time series configuration data. The method further includes causing presentation of a user interface comprising a chart including a first plot for first time series data for the first batch identifier and a second plot for second time series data for the second batch identifier, the chart configured to the time series configuration data, and the first plot is aligned to the second plot.

Object time series system and investigation graphical user interface

Methods and systems for presenting time series for analysis. A method may receive a first input defining a metric that indicates a relationship between a first and a second time series that are each associated with at least a first data object of a plurality of data objects, generate a first plot depicting the metric as determined from the first and the second time series, receive, via the user interface, a second input of a selection of a second data object of the plurality of data objects, determine, via an ontology, a relationship of the second data object with a third and a fourth time series that, respectively, are associated with series types that match series types associated with the first and the second time series, and generate and display, in the user interface, a second plot depicting the metric as determined from the third and the fourth time series.

Ontology-based time series visualization and analysis

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

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for a feature clustering of users, user correlation database access, and user interface generation system. The system can obtain information stored in different databases located across geographic regions, and determine unique users from the different information. The information can be included in unique records in the databases, with each record describing a particular user, and with each user described with imperfect identifying information. The system can analyze the different information utilizing machine learning models, and can associate each record with a particular unique user. The system can obtain identifications of items associated with each user, and determine the propensity of the user to disassociate with one or more items, or determine likelihoods of future association with different items not presently associated with the user.

Feature clustering of users, user correlation database access, and user interface generation system

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London, United Kingdom

Xavier Falco