Abstract: 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.

Abstract: A method for supporting detection of irregularities in a network includes monitoring features of said network using at least one monitoring device in order to collect spatio-temporal measuring data; providing, in an off-line phase, a training matrix where collected measuring data is aggregated in a predetermined time window such that said training matrix includes spatia-temporal correlations; performing, in said off-line phase, non-negative matrix factorization in order to decompose said training matrix into a coefficient matrix and a basis matrix, wherein temporal correlations and spatial correlations are jointly considered; creating, in an on-line phase; a current runtime matrix on a basis of measuring data newly collected in the on-line phase, computing, in said on-line phase, a current runtime coefficient matrix on a basis of said current runtime matrix and said basis matrix; and comparing, in said on-line phase, said current runtime coefficient matrix with at least one previous coefficient matrix.

Abstract: A method for storing objects in a storage, wherein the storage has a predetermined capacity, includes providing an expected popularity and an uncertainty of the expected popularity for each of the objects, and selecting a set of the objects for storing in the storage, wherein the set of is selected on the basis of the expected popularity, the uncertainty and the capacity of the storage.

Abstract: 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.

Abstract: 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.

Abstract: 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.