Uber Technologies, Inc.

Hewlett Packard Enterprise Development LP

Hewlett-packard Development Company, L.p.

Alvin AuYoung

Chandan Prakash Sheth

Sheng Yang

Shivendra Pratap Singh

Vikram Saxena

Livia Zarnescu Yanez

Kevin T Lim

Upamanyu Madhow

Indrajit Roy

Parthasarathy Ranganathan

Vanish Talwar

Kyle Elliot DeHovitz

Ted Douglas Herringshaw

Joshua Lodge Ross

Michael Mihn-Jong Lee

Puneet Sharma

John Wilkes

Robert S Schreiber

Sujata Banerjee

Yoshio Frank Turner

Anurag Singla

Janet Lynn Wiener

Daniel Juergen Gmach

Mitchel E Wright

Robert Block

Suranjan Pramanik

Junggun Lee

Shivaram Venkataraman

Yadi Ma

Chun-Chen Kuo

Laura Ellen Grit

Minzhen Yi

Jayaram Kallapalayam Radhakrishnan

Julian James Stephen

Sai Rahul Chalamalasetti

Systems and methods of using sensor data for coordinate prediction are disclosed herein. In some example embodiments, for a place, a computer system accesses corresponding service data comprising pick-up data and drop-off data for requests, and accesses corresponding sensor data indicating at least one path of mobile devices of the requesters of the requests, with the at least one path comprising at least one of a pick-up path ending at the pick-up location indicated by the pick-up data and a drop-off path beginning at the drop-off location indicated by the drop-off data. In some example embodiments, the computer system generates at least one predicted geographic location using the paths indicated by the sensor data, and stores the at least one predicted geographic location in a database in association with an identification of the place.

Using sensor data for coordinate prediction

Systems and methods for detecting and verifying closed places (e.g., claims no longer in business) from trip data are provided. A networked system accesses trip data associated with the POI. The networked system processes the trip data to generate at least two time buckets based on timestamps from the trip data associated with the POI, and calculates trip counts associated with the POI for each of the time buckets. Using a machine learning algorithm and based on the at least two time buckets, the networked system determines that the trip counts show a decline over time that indicates that the POI is likely closed. In response to the determining, the networked system updates a database to indicate the POI is closed.

Detecting attribute change from trip data

A system and method for retrieving cached data are disclosed herein. The system includes a cache server including a local memory and a table residing on the local memory, wherein the table is used to identify data objects corresponding to cached data. The system also includes the data objects residing on the local memory, wherein the data objects contain pointers to the cached data. The system further includes a remote memory communicatively coupled to the cache server through an Input-Output (I/O) connection, wherein the cached data resides on the remote memory.

Data caching using local and remote memory

Systems and methods for improving attribute data for a point of interest (POI) are provided. A networked system accesses trip data associated with the POI. The networked system generates, using a processor-implemented clustering algorithm, a first spatial cluster and a second spatial cluster using coordinates corresponding to the POI indicated in the trip data. A centroid for the first spatial cluster and a centroid for the second spatial cluster are identified by the networked system. The networked system determines that a difference in distance between the centroid for the first spatial cluster and the centroid for the second spatial cluster meets or transgresses a centroid distance threshold. In response to the determining, a database is updated to indicate a new attribute for the POI, the new attribute corresponds to an attribute associated with either the first spatial cluster or the second spatial cluster.

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

Systems and methods of deep learning coordinate prediction using satellite and service data are disclosed herein. In some example embodiments, for each one of a plurality of places, a computer system trains a deep learning model based on training data of the plurality of places. The deep leaning model is configured to generate a predicted geographical location of a place based on satellite image data and service data associated with the place. The training data for each place comprises satellite image data of the place, service data, and a ground truth geographical location of the place. The service data comprises at least one of pick-up data indicating a geographical location at which a provider started transporting a requester in servicing a request associated with the place or drop-off data indicating a geographical location at which the provider completed transporting the requester in servicing the request associated with the place.

Deep learning coordinate prediction using satellite and service data

Methods, systems, and computer-readable and executable instructions are provided for checkpointing using a field programmable gate array (FPGA). Checkpointing using FPGA can include checkpointing data within a region of a server's contents to memory and monitoring the checkpointed data using the FPGA.

Checkpointing using FPGA

Example implementations disclosed herein can be used to allocate network resources in a software defined network (SDN). In one example implementation, a method can include receiving a plurality of resource allocation proposals from a plurality of controller modules, instructing the controller modules to generate votes for the plurality of resource allocation proposals, and selecting one of the plurality of resource allocation proposals based on the votes to instantiate the selected resource allocation proposal in the SDN.

Network resource allocation proposals

In response to determining that an event matches a condition of a rule, a given one of a plurality of computing nodes is selected to send the event, based on one or both of an attribute of the event and an identifier of the rule. Information of the event is sent to the given computing node to perform correlation of the event with another event.

Selecting from computing nodes for correlating events

A sparse array is partitioned into first partitions and a second array is partitioned into second partitions based on an invariant relationship between the sparse array and the second array. The sparse array and the second array are associated with a computation involving the sparse array and the second array. The first partitions and the corresponding second partitions are distributed to workers. A different first partition and corresponding second partition is distributed to each of the workers. Third partitions of the sparse array and corresponding fourth partitions of the second array are determined based on the invariant relationship and measurements of load are received from each of the workers. At least one of the first partitions and the corresponding second partition is different from one of the third partitions and the corresponding fourth partition. The at least one of the first partitions and the corresponding second partition that is different is redistributed among the workers. A different third partition and corresponding fourth partition is executed by each of the workers.

Load-balanced sparse array processing

According to an example, data for a memcached server is replicated to a memcached replication server. Data operations for the memcached server may be filtered for backing up data to the memcached replication server.

Memcached server replication

In a method of implementing a graph storage system, the graph storage system is stored on a plurality of computing systems. A global address space is provided for distributed graph storage. The global address space is managed with graph allocators, in which a graph allocator allocates space from a block of the distributed global memory in order to store a plurality of graphs.

Distributed graph storage system

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San Jose, CA, United States of America

Alvin AuYoung