Uber Technologies, Inc.

Chun-Chen Kuo

Alvin AuYoung

Chandan Prakash Sheth

Kapil Gupta

Sheng Yang

Shivendra Pratap Singh

Vikram Saxena

Livia Zarnescu Yanez

Jeremy Hintz

Susmit Biswas

Sai Bhuvan Teja Balla

Systems and methods are provided for generating a places dataset comprising place data from multiple data sources each having a plurality of place data. Based on the places dataset, the systems and methods generate local clusters comprising places located within a predetermined distance, generate probable pairs of places in each local cluster, and distribute the probable pairs to one or more processors to determine similar edges between one or more places in each local cluster based on a plurality of combined similarity measures indicating whether or not a place is similar to another place. The systems and methods further generate similar pairs based on the combined similarity measures and convert the similar pairs into connected components comprising groups of similar places based on a best similar edge from all the candidates for a group of similar places.

Multiple source place matching system

Systems and methods for improving accuracy of geographic position data are provided. A networked system mines ticket data from content of the ticket. Based on the ticket data, a determination is made that the ticket indicates an issue with a trip involving a point of interest (POI). The networked system extracts trip data from a trip log corresponding to the trip involving the POI, and identifies, from a data storage, stored attributes of the POI. The networked system analyzes the ticket data, trip data, and attributes to determine a workflow to improve accuracy of the POI, whereby the analyzing comprises determining a priority level to verify accuracy of the POI. The workflow is triggered based on the priority level to verify accuracy of the POI.

Point of interest accuracy using tickets

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Palo Alto, CA, United States of America

Chun-Chen Kuo