Palantir Technologies Inc.

Rahul Mehta

Rahul Kumar Agarwal

Matthew Elkherj

Daniel Erenrich

Lawrence Manning

Xavier Falco

Eli Bingham

Yifei Huang

Roger Hu

Daniel Fernandez

Guillem Palou Visa

Aleksandr Zamoshchin

Jeremy Elser

Jason Prestinario

Clayton Sader

Rowan Gilmore

Nicholas Latourette

Ben Duffield

Patrick Woody

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

Datasets are annotated with metadata including categories. Each category corresponds to one or more fields. A hierarchy mapping is generated to indicate a hierarchical relationship between different categories. A natural language query specifies a first granularity level indicating a particular category and one or more field values corresponding to the particular category. Based on the hierarchy mapping, one or more categories that are hierarchically related to the particular category are identified. Based on the metadata, two or more datasets that include at least one hierarchically related category is selected. Based on the first granularity level, one or more dataset filters are generated. The one or more dataset filters are translated to a second granularity level corresponding to the at least one hierarchically related category. The translated filters are applied to at least one of the selected datasets. The two or more datasets are joined to generate a composite dataset.

Automatic generation of composite datasets based on hierarchical fields

Growing community of inventors

New York, NY, United States of America

Rahul Mehta