Amazon.com

About: Amazon.com is a company organization based out in Seattle, Washington, United States. It is known for research contribution in the topics: Computer science & Service (business). The organization has 13363 authors who have published 17317 publications receiving 266589 citations.

Providing enhanced data retrieval from remote locations

Abstract: Techniques are described for providing client computing nodes with enhanced access to data from remote locations, such as by providing and using local capabilities specific to the remote locations. In at least some situations, the access of a client computing node to data from a remote location may be enhanced by automatically performing activities local to the client computing node that improve the efficiency of communications sent between the client computing node and the remote location. As one example, access to data from a remote service may be enhanced by locally performing activities specific to the remote service, such as by using information about the remote service's internal mechanisms to cause the desired data to be provided from internal storage devices of the remote service without passing through front-end or other intermediate devices of the remote service while traveling to the client computing node.

Providing access to configurable private computer networks

Abstract: Techniques are described for providing users with access to computer networks, such as to enable users to interact with a remote configurable network service in order to create and configure computer networks that are provided by the configurable network service for use by the users. Computer networks provided by the configurable network service may be configured to be private computer networks that are accessible only by the users who create them, and may each be created and configured by a client of the configurable network service to be an extension to an existing computer network of the client, such as a private computer network extension to an existing private computer network of the client. If so, secure private access between an existing computer network and new computer network extension that is being provided may be enabled using one or more VPN connections or other private access mechanisms.

Service for providing item recommendations

Abstract: A service is disclosed for enabling web sites and other entities to provide item recommendations and other behavior-based content to end users. The service can be implemented as a web service that is remotely accessible over the Internet. Web sites use the web service's interface to report events descriptive of item-related actions performed by end users (e.g., item views, item purchases, searches for items, etc.). The web service analyzes the reported event data on an aggregated basis to detect various types of associations between particular items, and stores resulting datasets that map items to associated items. The web service's interface also provides various API calls for enabling the web sites to request item recommendations and other behavior-based content, including but not limited to personalized recommendations that are based on the event history of the target user. Advantageously, the web sites need not host the infrastructure for providing such content.

Chargrid: Towards Understanding 2D Documents.

Abstract: We introduce a novel type of text representation that preserves the 2D layout of a document. This is achieved by encoding each document page as a two-dimensional grid of characters. Based on this representation, we present a generic document understanding pipeline for structured documents. This pipeline makes use of a fully convolutional encoder-decoder network that predicts a segmentation mask and bounding boxes. We demonstrate its capabilities on an information extraction task from invoices and show that it significantly outperforms approaches based on sequential text or document images.

ERMIA: Fast Memory-Optimized Database System for Heterogeneous Workloads

Abstract: Large main memories and massively parallel processors have triggered not only a resurgence of high-performance transaction processing systems optimized for large main-memory and massively parallel processors, but also an increasing demand for processing heterogeneous workloads that include read-mostly transactions. Many modern transaction processing systems adopt a lightweight optimistic concurrency control (OCC) scheme to leverage its low overhead in low contention workloads. However, we observe that the lightweight OCC is not suitable for heterogeneous workloads, causing significant starvation of read-mostly transactions and overall performance degradation. In this paper, we present ERMIA, a memory-optimized database system built from scratch to cater the need of handling heterogeneous workloads. ERMIA adopts snapshot isolation concurrency control to coordinate heterogeneous transactions and provides serializability when desired. Its physical layer supports the concurrency control schemes in a scalable way. Experimental results show that ERMIA delivers comparable or superior performance and near-linear scalability in a variety of workloads, compared to a recent lightweight OCC-based system. At the same time, ERMIA maintains high throughput on read-mostly transactions when the performance of the OCC-based system drops by orders of magnitude.