Palantir Technologies Inc.

Dolby Laboratories Licensing Corporation

Doppler Labs, Inc.

Matthew Sills

Mark Elliot

Rahij Ramsharan

Lawrence Manning

Joseph Ellis

Leslie Bennett

Kevin Meurer

Jeffrey R Baker

Benjamin Duffield

Richard Fritz Lanman, Iii

Katherine Brainard

Ilya Nepomnyashchiy

Jacob Meacham

Yeong Wei Wee

Rastan Boroujerdi

Adam Borochoff

Allen Chang

Punyashloka Biswal

Robert Kruszewski

Mark Schafer

Matthew Lynch

Walker Burgin

Gregory O'Connor

Caitlin Colgrove

Sander Kromwijk

Stephanie Yu

Gabrielle Javitt

Alexandru Antihi

Brandon Krieger

Nir Ackner

Samuel Szuflita

Phillip Chen

Diran Li

Helen Kaltegaertner

Joshua Casale

Shannon Scott

Maria Franco

Richard Fritz Lanman Iii

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 computer system can receive one or more edits to be made to a canonical dataset and can temporarily store the one or more edits in a buffer. In response to receipt of a query of the canonical dataset, the computer system can rewrite the query to read from the canonical dataset and the buffer; combine the one or more edits from the buffer with the canonical dataset to form a combined dataset based on resolution policies to avoid conflicts between data; rewrite the query to execute on the combined dataset in lieu of the canonical dataset to optimize query performance; and execute the query on the combined dataset.

Low-latency database system

Systems and methods are provided for determining a set of objects, the objects corresponding to a given case or application of a deletion/retention policy; determining at least one object in the set of objects to be scheduled for deletion, the at least one object being associated with a given state that specifies a deletion and/or retention type and schedule for the at least one object; and scheduling data corresponding to the at least one object for deletion from one or more data sources based at least in part on the deletion and/or retention type and schedule specified in the object state.

Approaches for managing data retention lifecycle

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 an edits dataset. 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 edits dataset is the set of data that contains edits such as cell mutations, row appends and/or row deletions. The database system enables users to make cell or row-level edits to tables and observe those edits in analytical systems or downstream builds with minimal latency.

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

One or more context aware processing parameters and an ambient audio stream are received. One or more sound characteristics associated with the ambient audio stream are identified using a machine learning model. One or more actions to perform are determined using the machine learning model and based on the one or more context aware processing parameters and the identified one or more sound characteristics. The one or more actions are performed.

Context aware hearing optimization engine

Approaches for displaying a user interface including a map based on interaction data are disclosed. A set of interaction data and can be acquired and stored in a data structure. This data can be associated with a plurality of consuming entities that may have purchased something during these interactions. A set of provisioning entities can be determined based on spending or purchasing habits of the consuming entities. Based on this set of provisioning entities, a user interface can be generated which may include various shapes similar to a heat map. These shapes can indicate an average amount spent in a particular neighborhood, among other attributes.

Methods and systems for determining location information

Approaches for analyzing entity performance are disclosed. A first set of data and a second set of data can be stored in a data structure. This data can be associated with a plurality of interactions, and can be modified to include additional interactions. These interactions can involve consuming entities and provisioning entities. The modified data structure can be queried to retrieve information associated with one or more entities. After information is retrieved, it can be provided to a user.

Methods and systems for analyzing entity performance

Growing community of inventors

San Francisco, CA, United States of America

Matthew Sills