Amazon Technologies, Inc.

Other

Vaibhav Sharma

Andrew Jude Gacek

Léon Thrane

Shen Pan

Marvin Michael Theimer

Marc Stephen Olson

James Michael Thompson

Gaurav D Ghare

Neha Rungta

Michael P Hammer

Zachary Mohamed Shalla

Zachary Thomas Crowell

Rajesh Puri

David Grootwassink

Ullas Sankhla

Michael William Whalen

Nagesh V Honnalli

Samuel Bayless

Surendra Moolchand Bashani

Sushanth Tiruvaipati

Saswat Padhi

Raveesh Yadav

Roman Pruzhanskiy

Nitin Kumar Gupta

Harshil Rajeshkumar Shah

Rajat Gupta

Andrew Apicelli

Fernando Dias Pauer

Dhivashini Jaganathan

Ankush Das

Harsh Raju Chamarthi

Jared Curran Davis

Secure data handling discovery techniques model are implemented to discover access to secure data within an application. A dataflow model is generated for an application to describe a secure zone with respect to secure data. The dataflow model is then evaluated and updated when dataflows that exit the secure zone are detected. Classifications of the exits are received and used to update the dataflow model.

Secure data handling discovery

System and methods for IoT event detector correctness verification. Detector models (e.g., state-based models including variables, states, transitions and actions) take IoT device data as input and detect, based on the data, events that triggers actions. To verify a correctness of the models prior to deploying the models at scale, an event detector model correctness checker obtains a representation of a definition of the model, verifies, based on analysis of the model definition, whether the model complies with correctness properties, and generates a report indicating whether the model complies. Example correctness properties include a reachability correctness property that indicates that respective states or actions are reachable according to the definition of the event detector model. The analysis may be accessed via an interface element and may result in generation of a report that identifies a location of non-compliance within the model definition.

IoT event detector correctness verification

A distributed key-value database management system may provide transactions having a high degree of conformance to ACID properties. A data element may be maintained as a versioned list, where each entry may comprise a timestamp and a value indicative of a corresponding version of the data element. The timestamp may be based at least in part on a vended time value. Timestamps may be monotonically increasing and unique across all entries in a distributed system. Conflicting updates to a data element may be detected directly, prior to the completion of involved transactions.

Acid transaction for distributed, versioned key-value databases

Data, attributes, and metrics from unavailable resource hosts may be collected and used for cluster analysis in order to correlate the different hosts and group similar hosts into clusters. The clusters may be ranked based on the collected information and used to provide a simple way to identify shared failure modes among the unavailable hosts. By identifying the hosts of each cluster, shared failures can be corrected for large groups of hosts at the same time, enabling the hosts to return to operational states.

Using cluster processing to identify sets of similarly failing hosts

A system and method for discovering user equipment in a network is disclosed. According to one embodiment, a discovery proxy periodically scans an access router that serves a target user equipment and collects an address assignment record of the target user equipment via the access router. The discovery proxy passes the address assignment record to a discovery server, and the discovery server identifies a location of the target user equipment based on the address assignment record of the target user equipment.

Method and system for discovering user equipment in a network

To achieve long running batch updates, a transaction aggregator layer and one or more data aggregation queues, such as a command queue, are placed between one or more clients and a database. Writes go to the database aggregator layer and are enqueued in the appropriate data aggregation queue, which may be allocated by transaction. Committing the transaction drains the queue and writes the contents of the queue in one transaction-like update to the database. By using the transaction logic in the transaction aggregator layer, transaction-like behavior may be achieved that allows both updates and transactions to scale with less impact on database servers.

Concurrency control in database transactions

Growing community of inventors

Kirkland, WA, United States of America

Vaibhav Sharma