Palantir Technologies Inc.

Daniel Fernandez

Rahul Kumar Agarwal

Matthew Elkherj

Daniel Erenrich

Lawrence Manning

Xavier Falco

Eli Bingham

Yifei Huang

Roger Hu

Guillem Palou Visa

Aleksandr Zamoshchin

James Shuster

Jeremy Elser

Rahul Mehta

Rowan Gilmore

Jason Prestinario

Clayton Sader

Nicholas Latourette

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

In an embodiment, a data processing method comprises accessing a computer memory comprising a shareable cell-based computation notebook comprising: notebook metadata specifying a kernel for execution, and a computational cell comprising cell metadata, a source code reference, and an output reference, wherein the cell metadata identifies a particular version of source code of a function that defines an input dataset, a transformation, and one or more variables that are to be associated with output data that is to be generated as a result of executing the particular version of the source code; updating the source code reference to identify a first storage location that is to contain the particular version of the source code of the function; and updating the output reference to identify a second storage location that is to contain the output data that is to be generated as a result of executing the particular version of the source code identified in the cell metadata using the kernel specified in the notebook metadata, wherein the method is performed by one or more computing devices.

Managing sharable cell-based analytical notebooks

In an embodiment, a data processing method comprises creating and storing a plurality of analytical notebooks in digital computer storage, wherein each of the analytical notebooks comprises notebook metadata that specifies a kernel for execution, and one or more computational cells, wherein each of the cells comprises cell metadata, a source code reference and an output reference; receiving, in association with a first cell among the one or more cells, first input specifying computer program source code of a function, wherein the function defines an input dataset, a transformation, and one or more variables associated with output data; storing the first cell, excluding the output data, using a first digital data storage system and updating the source code reference to identify the first data storage system; using the kernel specified in the notebook metadata, executing an executable version of the source code to result in generating the output data; storing the output data using a second digital data storage system that is separate from the first digital data storage system and updating the output reference to identify the second data storage system.

Growing community of inventors

New York, NY, United States of America

Daniel Fernandez