Abstract: A system for discovery and analysis of network data usage of users of a communication network may collect information related to data usage over a network. The system may determine network data usage patterns for users from the data usage information. The network usage data, usage patterns and additional information may be analyzed to create user segments, and to analyze network data usage for the user segments. Differentiated data services may be created and implemented based on the network data usage for the user segments.

Abstract: The current document is directed to methods and systems that identify projects for which service consumers desire service provision and identify available candidate service providers that best match various project parameters and service-provision criteria determined by the automated system. Information describing the identified candidate service providers is presented to the service consumers by the systems to allow service consumers in order to facilitate their selection of service providers and scheduling of service provision. In one described implementation, service providers are matched to stock-keeping-unit (“SKU”) identifiers and location or locations. Service providers identified in the initial SKU-and-location-based matching process are then scored and ranked according to additional criteria and constraints, with information describing the highest-ranked service candidate providers provided to service consumers for selection and scheduling.

Abstract: In an example, network node failures may be predicted by extracting performance metrics for the network nodes from a plurality of data sources. A fail condition may be defined for the network nodes and input variables related to the fail condition for the network nodes may then be derived from the extracted performance metrics. A plurality of models may then be trained to predict the fail condition for the network nodes using a training set from the extracted performance metrics with at least one of the identified input variables. Each of the plurality of trained models may be validated using a validation set from the extracted performance metrics and may be rated according to predefined criteria. As a result, a highest rated model of the validated models may be selected to predict the fail condition for the network nodes.

Abstract: Process for the preparation of (S)-(?)-?-ethyl-2-oxo-1-pyrrolidineacetamide of Formula (I) by the steps of condensation of (S)-2-amino butanol of Formula (II) and 4-halobutryl chloride, where halo group can be chloro, bromo or iodo in solvents to form ?-ethyl-2-oxo pyrrolidine ethanol of Formula (III); oxidation of (S)-?-ethyl-2-oxo pyrrolidine ethanol to yield (S)-?-ethyl-2-oxo pyrrolidine acetic acid having the formula (IV); esterification of (S)-?-ethyl-2-oxo pyrrolidine acetic acid (IV) with an alcohol to provide alkyl ester of Formula (V) wherein, R is 14 Carbon atom; ammonolysis of alkyl esters of formula (V) with ammonia to provide (S)-(?)—?-ethyl-2-oxo-1-pyrrolidine acetamide of formula (1).