Abstract: This disclosure is directed to techniques for inferring traffic information or estimating total volume of traffic/data flowing through a target network/entity, wherein only a partial subset of inferred traffic information or volume of data is available to a predictor entity/network that infers such traffic information. In an embodiment, such partial subset of total traffic can either be made available to the entity/network for inferring and estimating total traffic or such partial data can actually flow through the entity/network.

Abstract: The present disclosure is directed to techniques for efficient streaming SVD computation. In an embodiment, streaming SVD can be applied for streamed data and/or for streamed processing of data. In another embodiment, the streamed data can include time series data, data in motion, and data at rest, wherein the data at rest can include data from a database or a file and read in an ordered manner. More particularly, the disclosure is directed to an efficient and faster method of computation of streaming SVD for data sets such that errors including reconstruction error and loss of orthogonality are error bounded. The method avoids SVD re-computation of already computed data sets and ensures updates to the SVD model by incorporating only the changes introduced by the new entrant data sets.