Amazon Technologies, Inc.

Microsoft Technology Licensing, LLC

David R Azari

Daniel Lee Osiecki

Monty Vanderbilt

Prashant L Sarma

Caitlyn R Schmidt

Eric J Horvitz

Susan Theresa Dumais

Eric David Brill

Alan Kipust

Divya Mahalingam

Tanvi Milind Bhadbhade

Lee M Miller

Pieter Joris De Temmerman

Ronald Kim Peterson

Mitsuo Takaki

David Gordon Leatham

Brian Dennehy

Nadia C Payet

Denis V Batalov

Maksym Kovalenko

Anthony C Martinelli

Kelly Watson

Cynthia A Prentice

Theresa M Hollis

Jonathan D Sanford

Disclosed are various embodiments for processing and storing mass data, where the data may include metrics generated based on performance of an event in a monitored system. Metrics describing a state of a monitored system may be received, accessed, and aggregated to generate a data model that describes performance of the monitored system. The metrics utilized in generating the data model may be disregarded after the data model has been generated. An output describing the state of the monitored system may be generated based on the data model, and the output may be communicated over a network, for example, to a requesting service.

Storage of mass data for monitoring

Various systems, methods, and programs embodied on a computer readable medium that facilitate monitoring of services and/or servers. In one embodiment, an amount of data is stored in at least one storage device, the data being generated by a plurality of services executed on a plurality of servers, and/or by the servers upon which the services are executed. A plurality of monitoring applications are executed in a monitoring server, the monitoring applications being configured to perform a plurality of monitoring functions with respect to at least a portion of the data to provide information associated with an operating condition of the services and/or the servers. An interface layer surrounds the monitoring applications in the monitoring server. The interface layer defines a messaging format that is used by external devices to interact with the monitoring applications.

Monitoring of services

A system and method for determining whether a textual work submitted for publishing is machine generated or non-machine generated by identifying and quantifying various aspects of the textual work and comparing those aspects to known works. For example, the system and method may identify aspects of a textual work, including, a relationship between the sentences within the textual work, a writing style of the author of the textual work, a grammatical structure of the sentences within the textual work, a quality of the textual work, and other aspects of the textual work. Upon determining that the textual work is machine generated the textual work may be rejected for publishing.

Machined book detection

Some implementations include searching for and analyzing public-domain-status information about works (such as e-books) over the Internet. A computer system may search for works recently made available online that are categorized as being in the public domain. Associated metadata is analyzed to generate a confidence level regarding whether the works are in the public domain or protected by copyright. Based on the confidence level, decisions can be made, such as whether to make the works available for free in a particular country.

Public-domain analyzer

Disclosed are various in various embodiments are systems and methods providing for storage of mass data such as metrics. A plurality of data models are generated in the server from a stream of metrics describing a state of a system. Each of the metrics is associated with one of a plurality of consecutive periods of time, and each data model represents the metrics associated with a corresponding one of the consecutive periods of time. The data models are stored in a data store and each of the metrics is discarded after use in generating at least one of the data models.

Systems, devices, and processes for classifying a digital item are described. In some examples, a first classifier determines whether a digital item, such as an electronic book (eBook), includes content of a first category that is acceptable for publication by a publisher. A second classifier determines whether the digital item includes content of a second category that is acceptable for publication by a publisher. In response to determining that the digital item includes content of the first category or content of the second category, a third classifier may determine whether the digital item includes a phrase that is indicative of content of a third category that is unacceptable for publication.

Classifier for classifying digital items

One or more computing systems can implement a classifier to classify content publishers as being likely to provide appropriate content or as being likely to provide inappropriate content. The classifier can gather information from previously classified publishers. The information from the previously classified publishers can used to train the classifier. Based on the training, the classifier can learn about traits, characteristics, and/or behavioral patterns, etc., associated with publishers that have been previously classified as being good as well as publishers previously classified as being bad. The classifier can then process information about an unclassified publisher to determine a classification for the unclassified publisher, as being good (and likely to provide appropriate content) or bad (and likely to provide inappropriate content).

Machine-assisted publisher classification

Various systems, methods, and programs embodied on a computer readable medium that facilitate monitoring of services and servers. In one embodiment, an amount of data is stored in at least one storage device, the data being generated by a plurality of services executed on a plurality of servers, and by the servers upon which the services are executed. A plurality of monitoring applications are executed in a monitoring server, the monitoring applications being configured to perform a plurality of monitoring functions with respect to at least a portion of the data to facilitate an assessment of an operating condition of the services and the servers. An interface layer surrounds the monitoring applications in the monitoring server. The interface layer defines a messaging format that is used by devices external to the interface layer to interact with the monitoring applications.

When a digital item is submitted for publication, an automated system may determine whether the digital item includes content from other digital items. In some implementations, when the digital item is an electronic book (eBook), the automated system may select sets of words from the eBook and compute hash codes, such that each hash code corresponds to a set of words. The automated system may compare the computed hash codes with retained hash codes associated with other electronic books to determine whether the digital item includes duplicate content.

Detecting duplicated content among digital items

Disclosed in various embodiments are systems and methods providing for storage of mass data such as metrics. A plurality of data models are generated in the server from a stream of metrics describing a state of a system. Each of the metrics is associated with one of a plurality of consecutive periods of time, and each data model represents the metrics associated with a corresponding one of the consecutive periods of time. The data models are stored in a data store and each of the metrics is discarded after use in generating at least one of the data models.

A plurality of data models are generated in a server from a stream of metrics describing a state of at least one system. Each of the data models represents a time grouping of a subset of the metrics. One or more dimensions are associated with each of the metrics. The data models are stored in association with respective ones of the dimensions in a memory. The dimensions with which the data models are associated in the memory are increased based upon an appearance of at least one previously non-existing dimension associated with a metric in the stream.

The present invention relates to a system and methodology to facilitate extraction of information from a large unstructured corpora such as from the World Wide Web and/or other unstructured sources. Information in the form of answers to questions can be automatically composed from such sources via probabilistic models and cost-benefit analyses to guide resource-intensive information-extraction procedures employed by a knowledge-based question answering system. The analyses can leverage predictions of the ultimate quality of answers generated by the system provided by Bayesian or other statistical models. Such predictions, when coupled with a utility model can provide the system with the ability to make decisions about the number of queries issued to a search engine (or engines), given the cost of queries and the expected value of query results in refining an ultimate answer. Given a preference model, information extraction actions can be taken with the highest expected utility. In this manner, the accuracy of answers to questions can be balanced with the cost of information extraction and analysis to compose the answers.

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Seattle, WA, United States of America

David R Azari