Abstract: A computer-implemented method of enforcing secondary data usage control includes providing, via a policy manager, secondary data usage policies of a data owner of original data. The method provides, via a service orchestrator, a description of a service intended to be applied by a data consumer to input data contained in the original data, the service including one or more data processing functions. The method matches, by a secondary usage control policy enforcement point (SUC PEP) component, the secondary data usage policies provided via the policy manager with the input data or specific classes of the input data and/or with data processing functions of the service provided via the service orchestrator. The method applies, by the SUC PEP component, the matched secondary usage policies on the secondary data.

Abstract: A method for monitoring of a proneness of a physical environment to infectious disease transmission includes a training phase in which unlabeled sensor data is obtained from sensors of the physical environment in order to provide a set of sensor features. A labeling matrix that is fed to a generative model is generated by applying situation labeling functions, wherein the generative model feeds a discriminative classifier model with probabilistic labels for the sensor features, wherein the probabilistic labels of the generative model are used for training the discriminative classifier model. A subset of the sensor features is determined based on an optimization procedure by a feature selection optimizer entity. In an operational phase, the discriminative classifier model uses the subset of sensor features for detecting predefined situations which make the physical environment prone to infectious disease transmission.

Abstract: A method augments data labels of a machine learning task. The method includes applying at least one labeling function for each of a plurality of pairs of semantic concepts and producing a labeling matrix, computing pairwise similarity scores using similarity metrics for each of the pairs of semantic concepts, augmenting the labeling matrix using the pairwise similarity scores to increase a density of the labeling matrix, and inputting the labeling matrix to a label aggregator to apply a single label for each of the pairs of semantic concepts labeled by the at least one labeling function.

Abstract: A method for operating a machine learning (ML) system by means of a data processing system is provided. Original data points of a data set are labeled by the data processing system. The method provides the data set and a set of labeling functions for the original data points, applies the labeling functions to the original data points for providing a corresponding output of the labeling functions, the output comprising labeled data points and labeling function outputs corresponding to each data point. The method processes at least a part of the output for learning correlations and/or similarities between labeled data points and original data points, and predicts and/or generates labels for abstains or abstain cases of labeling function outputs under consideration of data point correlations and/or similarities.

Abstract: A method preserving privacy in federated machine learning system is provided. In the method, a first computing entity in the federated learning system determines a first labeling matrix based on applying a first set of labeling functions to first data points. The first labeling matrix includes a plurality of first labels. The first computing entity obtains a similarity matrix indicating similarity scores between the first data points and second data points associated with a second computing entity. The first computing entity augments the first labeling matrix by transferring labels from a second labeling matrix into the first labeling matrix using the similarity scores between the first data points and the second data points. The first computing entity trains a discriminative machine learning model associated with the first computing entity based on the first augmented labeling matrix.

Abstract: Systems and methods for controlling data usage in a distributed environment among multiple entity domains. A method include steps of: receiving, in a local entity domain, a data consuming application comprising or identifying at least a first analytics task, wherein the first analytics task processes data inputs to produce first output data; determining availability of the data inputs; interpreting data usage policies, or data control policies, to determine atomic actions to be executed, wherein at least one of the data usage policies indicates that one of the data inputs for the first analytics task must remain in a remote entity domain; and executing the atomic actions, wherein the atomic actions include dispatching the first analytics task to the remote entity domain for remote execution of the first analytics task and receiving the first output data of the first analytics task from the remote entity domain.

Abstract: A method augments data labels of a machine learning task. The method includes applying at least one labeling function for each of a plurality of pairs of semantic concepts and producing a labeling matrix, computing pairwise similarity scores using similarity metrics for each of the pairs of semantic concepts, augmenting the labeling matrix using the pairwise similarity scores to increase a density of the labeling matrix, and inputting the labeling matrix to a label aggregator to apply a single label for each of the pairs of semantic concepts labeled by the at least one labeling function.

Abstract: A method for traffic control includes ingesting public transport data with one or more extract-transform-load procedures. A route of a public transport vehicle is reconstructed based on the ingested public transport data. The reconstructed vehicle route is partitioned into discrete route segments, each of the discrete route segments being defined by parameters spanning multiple dimensions. The discrete route segments are clustered into multiple groups based on the respective multidimensional parameters of each of the discrete route segments. The route segments are scored based on the clustering to create a traffic model which is useable to implement traffic control measures or to change mobility infrastructure.

Abstract: A method for performing privacy-preserving data federation in an internet-of-things (IoT) system includes: receiving a data message; determining whether the data message contains new information; based on determining that the data message contains the new information, determining one or more directives to trigger based on the data message; and generating a registration based on the one or more triggered directives.

Abstract: Systems and methods for controlling data usage in a distributed environment among multiple entity domains. A method include steps of: receiving, in a local entity domain, a data consuming application comprising or identifying at least a first analytics task, wherein the first analytics task processes data inputs to produce first output data; determining availability of the data inputs; interpreting data usage policies, or data control policies, to determine atomic actions to be executed, wherein at least one of the data usage policies indicates that one of the data inputs for the first analytics task must remain in a remote entity domain; and executing the atomic actions, wherein the atomic actions include dispatching the first analytics task to the remote entity domain for remote execution of the first analytics task and receiving the first output data of the first analytics task from the remote entity domain.

Abstract: A method for traffic control includes ingesting public transport data with one or more extract-transform-load procedures. A route of a public transport vehicle is reconstructed based on the ingested public transport data. The reconstructed vehicle route is partitioned into discrete route segments, each of the discrete route segments being defined by parameters spanning multiple dimensions. The discrete route segments are clustered into multiple groups based on the respective multidimensional parameters of each of the discrete route segments. The route segments are scored based on the clustering to create a traffic model which is useable to implement traffic control measures or to change mobility infrastructure.

Abstract: A method for Internet of Things (IoT) traffic management in a communication network supporting a plurality of network slices, in particular a 5th generation wireless communication network, includes dynamically assigning, by a network slice management component, network resources to a number of network slices. One or more of the network slices are dedicated IoT network slices being adapted to enable IoT-related communications between an IoT platform and IoT devices or IoT gateways. The method also includes the network slice management component, interacting with an IoT broker associated with the IoT platform and, based on the interaction, performing actions with respect to an allocation of network resources to the number of network slices and/or triggering actions on the IoT broker with respect to a configuration of IoT data traffic.

Abstract: A method for performing privacy-preserving data federation in an internet-of-things (IoT) system includes: receiving a data message; determining whether the data message contains new information; based on determining that the data message contains the new information, determining one or more directives to trigger based on the data message; and generating a registration based on the one or more triggered directives.

Abstract: A method for Internet of Things (IoT) traffic management in a communication network supporting a plurality of network slices, in particular a 5th generation wireless communication network, includes dynamically assigning, by a network slice management component, network resources to a number of network slices. One or more of the network slices are dedicated IoT network slices being adapted to enable IoT-related communications between an IoT platform and IoT devices or IoT gateways. The method also includes the network slice management component, interacting with an IoT broker associated with the IoT platform and, based on the interaction, performing actions with respect to an allocation of network resources to the number of network slices and/or triggering actions on the IoT broker with respect to a configuration of IoT data traffic.

Abstract: A method for discovering a set of objects within a defined geographical space, wherein the objects in the set of objects match at least one predefined characteristic, and wherein object information regarding the at least one predefined characteristic is provided by different servers, includes discovering objects within the defined geographical space by a geo discovery request based on geographical coordinates; finding, from the discovered objects, a starting object matching the at least one predefined characteristic within a definable sub-space of the defined geographical space; ranking objects within the definable sub-space by increasing distance to the starting object to establish a rank; sending a query to one or more of the different servers for gathering object information regarding a first object in the rank which is located nearest to the starting object; checking whether the first object in the rank matches the at least one predefined characteristic.

Abstract: A method for discovering a set of objects within a defined geographical space, wherein the objects in the set of objects match at least one predefined characteristic, and wherein object information regarding the at least one predefined characteristic is provided by different servers, includes discovering objects within the defined geographical space by a geo discovery request based on geographical coordinates; finding, from the discovered objects, a starting object matching the at least one predefined characteristic within a definable sub-space of the defined geographical space; ranking objects within the definable sub-space by increasing distance to the starting object to establish a rank; sending a query to one or more of the different servers for gathering object information regarding a first object in the rank which is located nearest to the starting object; checking whether the first object in the rank matches the at least one predefined characteristic.