Abstract: A system and method for consolidating a plurality of heterogeneous storage systems in a data center comprising collecting data from a plurality of heterogeneous storage devices using data collection tools, using Data Preparation Tool for extracting and translating the collected data, populating a Data Model stored in source storage configuration unit suitable for analysis, analyzing and classifying the collected data by an analysis unit based upon a plurality of attributes, comprising of a Consolidation Advisor that uses the analyzed data and candidate Target System Configurations, Preferences & Constraints for generating optimum number, specification & configuration of the Consolidate Target State infrastructure and mappings of logical units from as-is data center storage infrastructure to the target state, and iteratively validating the same in a Validation task till the final desired consolidation and objectives are met.

Abstract: A highly reliable data archival and retrieval method that enables fine grained control over data availability is implemented across a Quality of Service driven archival system, configured to fragment the data into data and parity chunks for storing onto the storage node. The technique employed by the archival system enables files to be read without having need to access any metadata, thereby tolerating complete loss of such metadata. Further, the Quality of Service driven system architecture improves upon the system performance and throughput by means of a storage node regeneration process which ensures balanced load on participating storage node during various storage, retrieval and regeneration operations.

Abstract: A system and method for consolidating a plurality of heterogeneous storage systems in a data center comprising collecting data from a plurality of heterogeneous storage devices (101) using data collection tools (102), using Data PreparationTool (103) for extracting and translating the collected data, populating a Data Model stored in source storage configuration unit (104) suitable for analysis, analyzing and classifying the collected data by an analysis unit (105) based upon a plurality of attributes, comprising of a Consolidation Advisor that uses the analyzed data and candidate Target System Configurations, Preferences & Constraints (106) for generating optimum number, specification & configuration of the Consolidate Target State (109) infrastructure and mappings of logical units from as-is data center storage infrastructure to the target state, and iteratively validating the same in a Validation task (108) till the final desired consolidation and objectives are met.

Abstract: A highly reliable data archival and retrieval method that enables fine grained control over data availability is implemented across a Quality of Service driven archival system, configured to fragment the data into data and parity chunks for storing onto the storage node. The technique employed by the archival system enables files to be read without having need to access any metadata, thereby tolerating complete loss of such metadata. Further, the Quality of Service driven system architecture improves upon the system performance and throughput by means of a storage node regeneration process which ensures balanced load on participating storage node during various storage, retrieval and regeneration operations.

Abstract: A method including providing a television service to customers; identifying a customer when the customer uses the television service; tracking content viewed by the customer; calculating probabilities based on the customer's viewing of contents, wherein the probabilities pertain to features applicable to the viewed contents and the features include genres of the viewed contents; selecting one or more upcoming contents, available at a future date to the customer, to recommend to the customer based on one or more features applicable to the upcoming content that correspond to one or more features applicable to the viewed contents; and providing a user interface to permit the customer to select from the one or more upcoming contents recommended to the customer.

Abstract: A method including providing a television service to customers; identifying a customer when the customer uses the television service; tracking content viewed by the customer; calculating probabilities based on the customer's viewing of contents, wherein the probabilities pertain to features applicable to the viewed contents and the features include genres of the viewed contents; selecting one or more upcoming contents, available at a future date to the customer, to recommend to the customer based on one or more features applicable to the upcoming content that correspond to one or more features applicable to the viewed contents; and providing a user interface to permit the customer to select from the one or more upcoming contents recommended to the customer.

Abstract: Methods and systems are provided, in an online advertising marketplace including publishers, advertisers and data providers, for allocating or partitioning, or automatically allocating or partitioning, advertiser payment, in connection with a purchase of advertising inventory, between a publisher and one or more data providers. Techniques are provided that seek to efficiently and fairly allocate the payment between the parties, taking into account value provided, such as by utilizing Shapley values. Provided techniques may also include efficiently or optimally integrating, facilitating, selecting or automating connections or arrangements between parties within the marketplace.

Abstract: Systems and methods for pricing portfolio allocations of ad deliveries to a plurality of online ads are disclosed. A first set of values is determined, each value of the first set of values representing for an online ad of the plurality of online ads, a bid an advertiser has associated with the online ad. A second set of values is determined, the second set of values representing statistics on variability of payoffs from showing ads. A risk value associated with the publisher is determined. Using a portfolio allocation algorithm, a set of ad delivery allocations associated with the plurality of online ads is determined, and, using a Vickrey-Clarke-Groves mechanism, a set of prices associated with the plurality of online ads is determined, both of which are determined based on the first set of values, the second set of values, and the risk value.