Nec Corporation

Nec C Orporation

Nec Europe Ltd.

Mohamed Ahmed

Konstantin Kutzkov

Mathias Niepert

Saverio Niccolini

Felipe Huici

Sofia Nikitaki

Lili Jiang

A method for data mining includes receiving input vectors and converting them into corresponding sketch feature vectors each having a number of output dimensions that is less than a number of dimensions of the corresponding input vector. Each sketch feature vector is compared against parameters and a decision loop generates results of similarities based on the comparisons. An estimate of cosine similarity or Pearson correlation of the input vectors is obtained based on estimates of an inner product of two input vectors and a 2-norm vector of an input vector. The estimates are obtained using respective hash tables for each input vector having a number of entries up to the number of output dimensions of the sketch feature vector. A decision is provided based on the results of the similarities and an application of the data mining such that the decision is implemented by the application.

System and method for data mining and similarity estimation

A method detects an event or anomaly in real-time and triggers an action based thereon. A stream of data is received from data sources. The data includes at least two categorical features and a real-value measurement. Sketching is performed on the features using min-wise hashing to create sketches of the data. A regression tree is learnt on the sketches so as to estimate a mean squared error. It is determined whether an event or anomaly exists based on the mean squared error. An action is triggered based on at least one of a type, location or magnitude of the determined event or anomaly.

Scalable system and method for real-time predictions and anomaly detection

A method estimates similarities in data streams. A data source receives input vectors from users. A sketch feature generator converts the input vectors into sketch feature vectors corresponding to the users, wherein each sketch feature vector represents data and meta-information from each user received in a most recent sample period. A similarity comparator compares each sketch feature vector against other sketch feature vectors to calculate similarity probabilities between the users. A processor running a decision loop determines a prediction result for at least one user based on the similarity probabilities.

Weighted similarity estimation in data streams with applications to collaborative filtering and viral marketing

A method is used to data mine input vectors. A data source receives input vectors. A sketch feature generator converts the input vectors into corresponding sketch feature vectors, where each sketch feature vectors has a number of dimensions that is less than a number of dimensions of the corresponding input vector. A similarity comparator compares each sketch feature vectors against a set of parameters. A decision loop determines results of similarities based upon comparison of each sketch feature vectors against the set of parameters.

Scalable system and method for weighted similarity estimation in massive datasets revealed in a streaming fashion

A personalization system includes a preprocessing component configured to receive a request from a user over a communications network and generate a request key using predefined attributes of the request. A categorization component is configured to map the request key to a subset of domain-dependent vocabulary. An augmentation and buffer component is configured to augment the request with the subset of domain-dependent vocabulary mapped to the request key by the categorization component and to buffer request sequences in queues according to sequence identifiers. An embedding model component is configured to update an embedding model using the buffered request sequences. A personalization component is configured to provide a personalization using the updated embedding model.

Privacy-aware in-network personalization system

A method for caching objects at one or more cache servers of a content delivery network (CDN) includes: determining, by a processor, attributes of objects of a set of objects; calculating, by the processor, an efficiency metric for each object of the set of objects based on the attributes of each object, wherein the attributes of each object include an expected future popularity associated with the object; selecting, by the processor, a subset of objects from the set of objects for caching based on calculated efficiency metrics; and caching the subset of objects at the one or more cache servers.

Efficient caching in content delivery networks based on popularity predictions

For allowing a very flexible scheduling of data flows within an OpenFlow (OF) switch a method for operating an OpenFlow switch within a network includes using the OpenFlow switch to direct arriving data flows out of different ports of the OpenFlow switch. The method is characterized in that a scheduling mechanism for performing at least one scheduling task is assigned to the OpenFlow switch, wherein a metric will be used to assign weights to the arriving data flows and wherein the data flows will then be scheduled based on the assigned weights and based on a scheduling policy. Further, a corresponding OpenFlow switch and a corresponding network are described, preferably for carrying out the above mentioned method.

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Heidelberg, Germany

Mohamed Ahmed