Abstract: An object-based data storage service receives a request to store a data object in association with a smart data storage tier. Based at least in part on characteristics of the data object, the object-based data storage service identifies and stores the data object in a first location corresponding to a first data storage tier. The object-based data storage service monitors access to the data object to identify a second set of characteristics of the data object. This second set of characteristics is used to determine that the data object is to be transitioned to a second data storage tier. The object-based data storage service, based at least in part on this determination, stores the data object in a second location corresponding to the second data storage tier.

Abstract: An object-based data storage service receives a request to store a data object in a first location corresponding to a first data storage tier. The request may specify a parameter to enable transitioning of the data object to another data storage tier. In response to the request, the object-based data storage service stores the data object in the first location and monitors access of the data object to determine usage data associated with the data object. The object-based data storage service processes the usage data to determine that the data object is to be transitioned to a second data storage tier. As a result of this determination, the object-based data storage service transitions the data object to a second location corresponding to the second data storage tier.

Abstract: An object-based data storage service receives a request to store a data object in association with a smart data storage tier. Based at least in part on characteristics of the data object, the object-based data storage service identifies and stores the data object in a first location corresponding to a first data storage tier. The object-based data storage service monitors access to the data object to identify a second set of characteristics of the data object. This second set of characteristics is used to determine that the data object is to be transitioned to a second data storage tier. The object-based data storage service, based at least in part on this determination, stores the data object in a second location corresponding to the second data storage tier.

Abstract: Technologies are described for analyzing interactions with data objects stored by a network-based storage service. The analysis of the interactions can identify patterns of the data object interactions and outcomes that can result from the patterns. Models can be developed that include the patterns and the outcomes corresponding to the patterns. As requests related to data object interactions are subsequently obtained by the system, the requests can be analyzed with respect to the models to identify an outcome that may be associated with the requests.

Abstract: An object-based data storage service receives a request to store a data object in association with a smart data storage tier. Based at least in part on characteristics of the data object, the object-based data storage service identifies and stores the data object in a first location corresponding to a first data storage tier. The object-based data storage service monitors access to the data object to identify a second set of characteristics of the data object. This second set of characteristics is used to determine that the data object is to be transitioned to a second data storage tier. The object-based data storage service, based at least in part on this determination, stores the data object in a second location corresponding to the second data storage tier.

Abstract: An object-based data storage service receives a request to store a data object in association with a smart data storage tier. Based at least in part on characteristics of the data object, the object-based data storage service identifies and stores the data object in a first location corresponding to a first data storage tier. The object-based data storage service monitors access to the data object to identify a second set of characteristics of the data object. This second set of characteristics is used to determine that the data object is to be transitioned to a second data storage tier. The object-based data storage service, based at least in part on this determination, stores the data object in a second location corresponding to the second data storage tier.

Abstract: An extendable augmented reality (AR) system for recognizing objects. The AR system may incorporate plugins to improve the AR system's ability to recognize objects, such as by adding external matchable data or using external recognition algorithms. The plugins may be installed/activated dynamically by a user to expand the system's AR capabilities. The AR system may also incorporate plugins to improve the AR system's post-recognition features such as supplying a user with supplemental content or follow-up functionality. The AR system may filter communications with the plugins to centralize identification processes, to shield plugins, and to ensure plugins are only activated at appropriate times.

Abstract: An extendable augmented reality (AR) system for recognizing objects. The AR system may incorporate plugins to improve the AR system's ability to recognize objects, such as by adding external matchable data or using external recognition algorithms. The plugins may be installed/activated dynamically by a user to expand the system's AR capabilities. The AR system may also incorporate plugins to improve the AR system's post-recognition features such as supplying a user with supplemental content or follow-up functionality. The AR system may filter communications with the plugins to centralize identification processes, to shield plugins, and to ensure plugins are only activated at appropriate times.

Abstract: An object-based data storage service receives a request to store a data object in association with a smart data storage tier. Based at least in part on characteristics of the data object, the object-based data storage service identifies and stores the data object in a first location corresponding to a first data storage tier. The object-based data storage service monitors access to the data object to identify a second set of characteristics of the data object. This second set of characteristics is used to determine that the data object is to be transitioned to a second data storage tier. The object-based data storage service, based at least in part on this determination, stores the data object in a second location corresponding to the second data storage tier.

Abstract: An object-based data storage service receives a request to store a data object in association with a smart data storage tier. Based at least in part on characteristics of the data object, the object-based data storage service identifies and stores the data object in a first location corresponding to a first data storage tier. The object-based data storage service monitors access to the data object to identify a second set of characteristics of the data object. This second set of characteristics is used to determine that the data object is to be transitioned to a second data storage tier. The object-based data storage service, based at least in part on this determination, stores the data object in a second location corresponding to the second data storage tier.

Abstract: An extendable augmented reality (AR) system for recognizing objects. The AR system may incorporate plugins to improve the AR system's ability to recognize objects, such as by adding external matchable data or using external recognition algorithms. The plugins may be installed/activated dynamically by a user to expand the system's AR capabilities. The AR system may also incorporate plugins to improve the AR system's post-recognition features such as supplying a user with supplemental content or follow-up functionality. The AR system may filter communications with the plugins to centralize identification processes, to shield plugins, and to ensure plugins are only activated at appropriate times.

Abstract: In large distributed computing environments, application execution may be distributed between a plurality of groups, the plurality of groups containing a set of host computer systems responsible for the execution of one or more operations of the application. Group membership may be determined by generating configuration information based at least in part on the plurality of groups. The configuration information may be provided to a plurality of host computer systems and each host computer system of the plurality of host computer systems may determine membership to a particular group of the plurality of groups based at least in part on the configuration information.

Abstract: Disclosed are techniques for recognizing text from one or more frames of image data using contextual information. In some implementations, image data including a captured textual item is processed to identify an entity in the image data. A context can be selected using the entity, where the context corresponds to a dictionary. Text in the captured textual item can be identified using the dictionary. The identified text can be output to a display device.

Abstract: Various aspects of the performance of computing resources, such as storage volumes, are measured and used to train a probability model. The probability model is used in a query engine that is able to respond receive queries about a computing resource's state. The queries may specify a state of the computing resource and provide a set of measurements of the computing resource's performance. The query engine may use the probability model, which may be in the form of a contingency table, to provide information that indicates one or more most likely causes of the state.