Telefonaktiebolaget Lm Ericsson (publ)

Brindha Padmanaabhan

Manoj Prasanna Kumar

Subramanian Shivashankar

N Hari Kumar

Karthikeyan Premkumar

Vikas Verma

Jawad Mohamed Zahoor

The present disclosure is directed towards a data storage setting arrangement in a federated database system that includes applications configured to handle data in corresponding databases. The arrangement includes a communication interface for obtaining application requirement data (AIC) related to database usage, a database determining unit for predicting database type using the application requirement data and selecting database based on the database type prediction, a processing type determining unit for predicting database processing type based on the application requirement data and selecting database processing type based on the processing type prediction, a data model creating unit for creating a data model for storing of data in the database based on the application requirement data and an instantiating unit for instantiating a connection between the application and the selected database based on the selected database processing type and data model.

Selection of data storage settings for an application

A query handling arrangement is disclosed for a federated database system. The database system includes data storing applications configured to store data in corresponding databases. The query handling arrangement includes a query handling device that receives a database query from a query generating application concerning the obtaining of data from the databases, obtains usage profiles regarding allowed use of the data in the databases provided by a usage profile announcing device, investigates the usage profiles, obtains data from the databases according to the obtained usage profiles, and responds to the query with the obtained data.

Exposing data to query generating applications using usage profiles

A method for propagating user preference information in a communications network, in which the user preference information may be available for a subset of users within the network. The method comprises generating individual user attribute vectors, based on user historical data and estimating user preference information. The method further comprises defining a community structure for the network and generating a stacked representation for users, the representation comprising the user attribute vector augmented with an aggregated vector of estimated user preferences of members of the user's community. The method further comprises learning a function relating the stacked representation to user preference, using the subset of users, and applying the learned function to users outside the subset.

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Chennai, India

Brindha Padmanaabhan