Abstract: Methods, apparatus, and processor-readable storage media for implementing a machine learning-based recommendation engine for storage system usage within an enterprise are provided herein. An example computer-implemented method includes processing input data pertaining to multiple storage systems within an enterprise; determining association rules applicable to the multiple storage systems by applying machine learning techniques to the processed input data; generating configuration-related recommendations applicable to one or more of the storage systems by applying content filtering techniques to the determined association rules; and outputting, via user interfaces, the configuration-related recommendations to a user for use in connection with storage system configuration actions and/or an entity within the enterprise for use in connection with user-support actions.

Abstract: Methods, apparatus, and processor-readable storage media for determining user retention values using machine learning and heuristic techniques are provided herein. An example computer-implemented method includes processing multiple forms of input data pertaining to interactions between a user and an enterprise; generating one or more user sentiment values from the processed input data by applying machine learning techniques to the processed input data; determining a user-specific estimate for the enterprise retaining the user, wherein determining the user-specific estimate comprises combining the one or more sentiment values with one or more storage system heuristics-based values derived from enterprise-related data; and outputting the user-specific estimate to at least one entity within the enterprise for use in connection with user-support actions.

Abstract: Methods, apparatus, and processor-readable storage media for reactive storage system-based software version analysis using machine learning techniques are provided herein. An example computer-implemented method includes obtaining user service requests, each comprising a description of problems and data pertaining to storage systems associated with the requests; calculating similarity measures for the user service requests by applying a machine learning algorithm to the user service requests; automatically grouping the user service requests into a set based on the similarity measures; automatically grouping, within the set, two or more of the user service requests into subsets based on a software version attributed to the storage systems associated with the two or more user service requests; generating an output pertaining to actions related to a software version update; and transmitting the output to at least one of the users corresponding to the user service requests in at least one of the subsets.

Abstract: Methods, apparatus, and processor-readable storage media for proactive storage system-based software version analysis using machine learning techniques are provided herein. An example computer-implemented method includes obtaining storage system data from multiple storage systems; determining performance issues among the storage systems by applying a machine learning algorithm to the storage system data; automatically grouping the storage system data into a set of groups based on issue type among the determined performance issues; automatically grouping, within the set, the storage system data into subsets based on a software version attributed to the corresponding storage system data; generating an output pertaining to actions to be performed with respect to at least one software version update; and transmitting the output to users of the storage systems which correspond to the storage system data in at least one of the subsets.

Abstract: Methods, apparatus, and processor-readable storage media for proactive storage system-based software version analysis using machine learning techniques are provided herein. An example computer-implemented method includes obtaining storage system data from multiple storage systems; determining performance issues among the storage systems by applying a machine learning algorithm to the storage system data; automatically grouping the storage system data into a set of groups based on issue type among the determined performance issues; automatically grouping, within the set, the storage system data into subsets based on a software version attributed to the corresponding storage system data; generating an output pertaining to actions to be performed with respect to at least one software version update; and transmitting the output to users of the storage systems which correspond to the storage system data in at least one of the subsets.

Abstract: Methods, apparatus, and processor-readable storage media for reactive storage system-based software version analysis using machine learning techniques are provided herein. An example computer-implemented method includes obtaining user service requests, each comprising a description of problems and data pertaining to storage systems associated with the requests; calculating similarity measures for the user service requests by applying a machine learning algorithm to the user service requests; automatically grouping the user service requests into a set based on the similarity measures; automatically grouping, within the set, two or more of the user service requests into subsets based on a software version attributed to the storage systems associated with the two or more user service requests; generating an output pertaining to actions related to a software version update; and transmitting the output to at least one of the users corresponding to the user service requests in at least one of the subsets.

Abstract: Methods, apparatus, and processor-readable storage media for classification of storage systems and users thereof using machine learning techniques are provided herein. An example computer-implemented method includes processing input data pertaining to multiple storage systems within an enterprise; classifying one or more of the storage systems by applying a first set of machine learning techniques to the processed input data; classifying one or more respective users of the classified storage systems by applying a second set of machine learning techniques to the processed input data associated with the classified storage systems; and outputting, via one or more user interfaces, at least a portion of the storage system classifications and at least a portion of the user classifications to a user for use in connection with storage system configuration actions and/or an entity within the enterprise for use in connection with user-support actions.

Abstract: Methods, apparatus, and processor-readable storage media for classification of storage systems and users thereof using machine learning techniques are provided herein. An example computer-implemented method includes processing input data pertaining to multiple storage systems within an enterprise; classifying one or more of the storage systems by applying a first set of machine learning techniques to the processed input data; classifying one or more respective users of the classified storage systems by applying a second set of machine learning techniques to the processed input data associated with the classified storage systems; and outputting, via one or more user interfaces, at least a portion of the storage system classifications and at least a portion of the user classifications to a user for use in connection with storage system configuration actions and/or an entity within the enterprise for use in connection with user-support actions.

Abstract: Methods, apparatus, and processor-readable storage media for implementing a machine learning-based recommendation engine for storage system usage within an enterprise are provided herein. An example computer-implemented method includes processing input data pertaining to multiple storage systems within an enterprise; determining association rules applicable to the multiple storage systems by applying machine learning techniques to the processed input data; generating configuration-related recommendations applicable to one or more of the storage systems by applying content filtering techniques to the determined association rules; and outputting, via user interfaces, the configuration-related recommendations to a user for use in connection with storage system configuration actions and/or an entity within the enterprise for use in connection with user-support actions.

Abstract: Methods, apparatus, and processor-readable storage media for determining user retention values using machine learning and heuristic techniques are provided herein. An example computer-implemented method includes processing multiple forms of input data pertaining to interactions between a user and an enterprise; generating one or more user sentiment values from the processed input data by applying machine learning techniques to the processed input data; determining a user-specific estimate for the enterprise retaining the user, wherein determining the user-specific estimate comprises combining the one or more sentiment values with one or more storage system heuristics-based values derived from enterprise-related data; and outputting the user-specific estimate to at least one entity within the enterprise for use in connection with user-support actions.

Abstract: A network-based data discovery and consumption coordination service may provide data set listings for purchase offered by respective data vendors. The data discovery and consumption coordination service may provide listings in response to search requests based on various search criteria, as well as various filtering techniques based on user reviews, ratings, and other user purchase history for data set listings. The data discovery and consumption coordination service may receive a purchase request via a network-based interface for the service. Data consumption resources which may consume a purchased data set may also be identified via the network-based interface for the data discovery and consumption coordination service. The data discovery and consumption coordination service may direct the transfer of the data set for the purchased data set listing to the identified data consumption resource.

Abstract: Disclosed are various embodiments for a framework for time-associated data stream storage, processing, and replication. A plurality of streams of time-associated data are received from a plurality of sources via a network using an application-layer protocol. Each of the plurality of streams is divided into a plurality of fragments. An acknowledgement is sent to each of the plurality of sources for each of the plurality of fragments via the network using the application-layer protocol. Processing is performed on each of the plurality of fragments for individual ones of the plurality of streams. An action is implemented relative to a respective fragment based at least in part on a result of the processing.

Abstract: Systems, methods, and computer-readable storage media are provided for selecting and serving user-customized search engine homepage experiences. Upon receiving a request for presentation of a search engine homepage from a user, data points about the user are accessed to determine the type of homepage experience the requesting user desires to receive. The type of homepage experience the user desires to receive may include a user-tailored image to be presented as the background of the search engine homepage. Upon determining the type of experience the user desires, a search engine homepage experience is selected for the user. In this way, the user is provided with a search engine homepage experience that is tailored to his or her interests and behavior, thus increasing user satisfaction and engagement with the search engine homepage.

Abstract: A data sharing methodology and structure for communication between users of digital Internet-accessible devices, such as set-top boxes, is described. Users of Internet-accessible devices can establish connections between trusted users of other Internet-accessible devices and transfer personal information using either the Internet or the public switched telephone network (PSTN). Some examples of the type of information that may be transferred include IP addresses, e-mail, multimedia objects, and voice data. Once personal identification information has been exchanged, future communications may be easily re-established and maintained.

Abstract: A method and structure that allows a user who has access to an audio/visual object to cause an interactive control element of an interactive multimedia system to grab and store off for future retrieval the audio/visual object by simply activating a selection mechanism of the interactive multimedia system.

Abstract: A method, apparatus computer program, and storage medium for storing the computer program for downloading a data program to a computing device are provided. The method comprises the steps of designating a data program; receiving the designated program at a set top box; and transferring the received data program from the set top box to the computing device.

Abstract: A television set-top box used as a point of sale terminal at a retail establishment such as a department store or kiosk. The television set-top box may include a swipe card reader and is coupled to a television display, an input device and a printer. The customer accesses a catalog database of merchandise and inventory using a user interface to point of sale software. The customer may also enter an order using order entry software forming a part of the point of sale software. The catalog database is situated on a disc drive within the set-top box and can be updated from a catalog server as required to maintain accurate inventory and merchandise information in one embodiment. In another embodiment, the set-top box operates in a client mode to access information from the catalog server.

Abstract: A data sharing methodology and structure for communication between users of digital Internet-accessible devices, such as set-top boxes, is described. Users of Internet-accessible devices can establish connections between trusted users of other Internet-accessible devices and transfer personal information using either the Internet or the public switched telephone network (PSTN). Some examples of the type of information that may be transferred include IP addresses, e-mail, multimedia objects, and voice data. Once personal identification information has been exchanged, future communications may be easily re-established and maintained.

Abstract: The present invention includes a system for enabling a user to download information displayed on a display screen of a television receiver. The system includes a device coupled to said television receiver to access information to be displayed on the display screen and a portable data device adapted to download the information from the device. The device includes a set-top box and the portable data device may be a smart card which may include an integrated circuit (IC) chip having information stored therein. The set-top box includes a back-channel for outputting information and the portable data device is coupled to the device by way of a smart card reader. The portable data device includes a display, a processor, a memory, navigation buttons and a remind button. The portable data device may communicate to the device using the ISO7816 Protocol, USB Protocol, a Serial Parallel Interface, Sony Memory Stick Protocol, etc.

Abstract: An adaptive user interface and method thereof for simplifying the use of new electronic devices, such as set top boxes, by adapting the user interface on the basis of usage history. The adaptive user interface monitors input commands to the user interface and tracks usage statistics for use in determining whether to add or remove features from the interface. A ramp-up embodiment of the invention automatically adds user interface features as the user becomes more proficient with a device. A ramp-down embodiment of the invention automatically removes seldom used interface features. By tracking users' actions, the user interface can more accurately be modified to fit actual use habits of each user, rather than relying on user perception of what functionality is used.