Abstract: The method comprises acquiring information from signals exchanged between a user's mobile computing devices and a plurality of Base Transceiver Stations, or BTSs, analyzing said acquired information for determining, over a period of time, the locations of said user's mobile computing device and deduce through a statistical model the points of interest, identifying and labelling at least part of said user's mobile computing device determined locations as points of interest.

Abstract: Disclosed is a method for detecting communities in massive social networks by means of an agglomerative approach in which core communities are built and gradually clustered in an iterative manner into higher level communities until the algorithm converges (a stop condition is met), whereby it becomes possible to easily trace how the communities are being formed, resulting in an easily explainable model that allows the detection of overlapping communities. The disclosed method starts from data representing social interactions between individuals, building a weighted social graph where the vertices represent individuals and the links represent social relationships between individuals.

Abstract: It is characterised in that it comprises creating with computing means a multidimensional profile of a user including at least a prediction of behaviour of said user and a characterisation of social influence of said user, said prediction of behaviour comprising: a) applying predictive models to individual factors; b) calculating influence received by said user from a social circle, said calculation based at least on previous events and Social Network Analysis Information, said previous events referred to behaviour or behaviours previously adopted by members of said social circle; and said characterisation of social influence comprising simulating a determined behaviour in said user and estimating the effect caused over at least part of said members of said social circle.

Abstract: It comprises computing values of cognitive and personality indicators of users of a telecom operator by means of machine learning and data mining algorithms from information available in a telecom operator system extracted from Social Network Analysis metrics, Call Detailed Record information and commercial information of said users stored in an operator's Data Warehouse and Customer Relationship Management systems as well as information from previous surveys, or questionnaires, answered by a representative sample of users as an input of said machine learning and said data mining algorithms. The method involves building a complex computer model that infers the values of the psychological dimensions of said users by means of said machine learning and said data mining algorithms to obtain a multi-dimensional vector for each of the users.

Abstract: A method for selecting and/or labeling a vessel image includes operating an imaging system to obtain reconstructed image data including a volume having vessels therein. A bone removal routine is then applied to the reconstructed image data to produce a bone-free image. An initial point on a main vessel in the bone-free image is located, and bifurcation points and branches departing from the main vessel are identified. An adjacency graph of each branch coming from the main vessel is built. The method includes either or both of selecting and displaying a best or at least a favorable path through the vessels in accordance with predetermined criteria using the adjacency branch, or labeling and displaying branches of the main vessel.