Oracle International Corporation

Amazon Technologies, Inc.

Matthew Perry Abrams

John Patrick Connelly

Robert Otto Barnes, Ii

Guy Lewis Steele, Jr

Gokul Soundararajan

Ippokratis Pandis

Gaurav Saxena

Andrew Brownsword

Carlos Felipe Reverte

Punit Rajgaria

Vernon W Hui

Murali Brahmadesam

Kendra Mariko Chen

David Lawrence Rager

Anping Wang

Hemanth Satyanarayana

Sanuj Basu

Ron Nordan

Gopal Paliwal

Todd Jeffrey Green

Jeevan Gheevarghese Joseph

Vadim Skipin

Aditya Mugali

Daniel Fichter

Gennady Nurik

Aparajeeth Thupil

Richard Scott LaBarca

Hyunjung Lee

Rahul Dilip Jagad

Sanjeev Khodaskar

Johannes Wust

Methods for replicating transactional tables of a transactional database to an analytical database and maintaining updates to those transactional table representations are disclosed. Snapshots of the transactional tables are provided to the analytical database via a transport mechanism, such as a data storage service or a data streaming service, and stored at the analytical database. Then, checkpoints comprising portions of a change-data-capture log that has recorded transactional changes to the transactional tables of the transactional database are provided to the analytical database via the same or different transport mechanism and used to commit those transactional changes to the snapshot representations. The snapshot representations may be used to respond to incoming analytical queries in order to provide real-time querying results.

Hybrid transactional and analytical processing architecture for optimization of real-time analytical querying

Disclosed are techniques for processing user profiles using data structures that are specialized for processing by a GPU. More particularly, the disclosed techniques relate to systems and methods for evaluating characteristics of user profiles to determine whether to offload certain user profiles to the GPU for processing or to process the user profiles locally by one or more central processing units (CPUs). Processing user profiles may include comparing the interest tags included in the user profiles with logic trees, for example, logic trees representing marketing campaigns, to identify user profiles that match the campaigns.

Enhanced processing of user profiles using data structures specialized for graphical processing units (GPUs)

Certain aspects and features of the present disclosure relate to systems and methods for automatically classifying hosts in real-time. For instance, classifying hosts as bots, and subsequently mitigating or blocking traffic from the hosts classified as bots can be advantageous in real-time data exchange systems. In a real-time data exchange system, data can be exchanged between a server and a target host in real-time when the target host accesses a webpage. Inhibiting data communication between servers and hosts operated by bot scripts can reduce fraudulent activity. In some implementations, hosts can be automatically classified into various groups based at least in part on the data included in requests received from the hosts.

Systems and methods for enhanced host classification

Techniques are provided to quickly process stored objects to generate an accurate estimate of the number of live objects that will be responsive to a client's filtering request defined at least in part based on a filtering parameter. Stored objects generated from user interactions with webpages are modified to simulate live objects and processed by a live object-intake simulator. The simulated results are then filtered using the filtering parameter to form a subset of matching objects. An estimate of the number of live objects produced by the filtering parameter is generated using at least the count of the objects in the subset. A confidence metric of the estimate is computed, in some cases using a statistical model. Under certain conditions, the estimate is delivered, the confidence metric is delivered, and/or live object intake based on the filtering parameter is initiated.

Object count estimation by live object simulation

The present disclosure relates to synchronizing entity identifiers across multiple data centers. In some embodiments, an entity identifier translation server may be deployed within a cloud-based distributed environment. The entity identifier translation server may receive data from multiple different data centers and detect changes/updates to entity identifiers within the data centers. In response to detected changes, the entity identifier translation server may identify one or more corresponding entity identifiers within the other data centers. For each of the corresponding entity identifiers within the other data centers, the entity identifier translation server may determine whether or not the corresponding entity identifier is to be synchronized with the newly-changed entity identifier in the first data center, and if so, may initiate synchronization processes.

Entity identifier synchronization across data centers

The present disclosure generally relates to storing, processing, and classification of content resources, such as documents, web-based resources, and other content. More particularly, the present disclosure describes techniques for distributed storage of network session data in hierarchical data structures stored on multiple servers and/or physical storage devices, and techniques for analyzing and classifying the distributed hierarchical structures. Such techniques may include executing different machine-learning algorithms on different servers and/or different storage devices, and generating node mapping data between a plurality of different hierarchical structures and a top-level derivative hierarchy that references the underlying hierarchical structures in order to access and manage the different distributed taxonomies within the underlying hierarchical structures.

Distributed storage and processing of hierarchical data structures

Growing community of inventors

Las Cruces, NM, United States of America

Matthew Perry Abrams