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: Service (business) & Service provider. The organization has 13363 authors who have published 17317 publications receiving 266589 citations.

Dynamically monitoring and modifying distributed execution of programs

Abstract: Techniques are described for managing distributed execution of programs. In some situations, the techniques include dynamically monitoring the ongoing distributed execution of a program on a cluster of multiple computing nodes, and may include automatically determining the status of execution of the program on each of the multiple computing nodes and/or automatically determining the aggregate usage of one or more types of computing resources across the cluster of multiple computing nodes by the distributed program execution. The information obtained from the dynamic monitoring may be used in various manners, including to facilitate dynamically modifying the ongoing distributed program execution in various manners, such as to temporarily throttle usage of computing resources by the distributed program execution (e.g., to remove or reduce one or more bottlenecks).

Services for increasing the utility of electronic wish lists

Abstract: Various computer-implemented services are disclosed for increasing the utility of electronic wish lists, and for assisting users in making gift purchases. One such service automatically notifies a user when an item being accessed in an electronic catalog is on, or is similar to an item on, an electronic wish list of an “affiliated” user. The affiliations may be created explicitly by the users (e.g., by completing an online form), and/or implicitly based on actions performed by the users (e.g., when one user purchases a gift for another user). Another service automatically reminds a first user who is affiliated with a second user when a known or inferred gift-giving event of the second user is approaching. The reminder may include a link to a wish list of the second user, and/or may include or provide a link for viewing personalized gift recommendations for the second user.

Architecture for multi-domain utterance processing

Abstract: Features are disclosed for processing a user utterance with respect to multiple subject matters or domains, and for selecting a likely result from a particular domain with which to respond to the utterance or otherwise take action. A user utterance may be transcribed by an automatic speech recognition (“ASR”) module, and the results may be provided to a multi-domain natural language understanding (“NLU”) engine. The multi-domain NLU engine may process the transcription(s) in multiple individual domains rather than in a single domain. In some cases, the transcription(s) may be processed in multiple individual domains in parallel or substantially simultaneously. In addition, hints may be generated based on previous user interactions and other data. The ASR module, multi-domain NLU engine, and other components of a spoken language processing system may use the hints to more efficiently process input or more accurately generate output.

Request routing based on class

Abstract: A system and method for management and processing of resource requests is provided. A content delivery network service provider receives a DNS query from a client computing device. The DNS query corresponds to a requested resource from the client computing device. The content delivery network service provider associates the client computing device with a cluster of other client computing devices. Based on routing information for the cluster, the content delivery network service provider routes the DNS query. The process can further include monitoring performance data associated with the delivery of the requested resource and updating the routing information for the cluster based on the performance data for use in processing subsequent requests from client computing devices in the cluster.

Monetary Policy in Real Time

Abstract: We analyze the panel of the Greenbook forecasts (sample 1970-1996) and a large panel of monthly variables for the United States (sample 1970-2003) and show that the bulk of dynamics of both the variables and their forecasts is explained by two shocks. A two-factor model that exploits, in real time, information on many time series to extract a two-dimensional signal produces a degree of forecasting accuracy of the federal funds rate similar to that of the markets and, for output and inflation, similar to that of the Greenbook forecasts. This leads us to conclude that the stochastic dimension of the U.S. economy is two. We also show that dimension two is generated by a real and nominal shock, with output mainly driven by the real shock, and inflation mainly driven by the nominal shock. The implication is that, by tracking any forecastable measure of real activity and price dynamics, the central bank can track all fundamental dynamics in the economy.