Abstract: A method of adaptive differential propagation of Simple Object Access Protocol (SOAP) messages is provided herein. The method includes the following stages: classifying incoming SOAP messages according to at least one of: structure, content, and context thereof; selecting a corresponding template out of a plurality of templates for each classified SOAP message such that the selected template minimized a specified diff function applied to the classified SOAP message and the selected template; applying the specified diff function to the SOAP message and the selected template to yield a respective delta; and encapsulating a template identifier with the respective delta, wherein the template identifier is uniquely associated with the selected template, to yield an outgoing SOAP message.

Abstract: A method of adaptive differential propagation of Simple Object Access Protocol (SOAP) messages is provided herein. The method includes the following stages: classifying incoming SOAP messages according to at least one of: structure, content, and context thereof; selecting a corresponding template out of a plurality of templates for each classified SOAP message such that the selected template minimized a specified diff function applied to the classified SOAP message and the selected template; applying the specified diff function to the SOAP message and the selected template to yield a respective delta; and encapsulating a template identifier with the respective delta, wherein the template identifier is uniquely associated with the selected template, to yield an outgoing SOAP message.

Abstract: Methods and systems for processing an input. An input vector y is received that represents a noisy observation of Ax, where A is a data matrix and x is a data vector of unknown variables. Data vector x is recovered from the received input vector y via an iterative method. The recovering comprises determining an inference of a vector of marginal means over a graph G, where the graph G is of a joint Gaussian probability density function p(x) associated with noise in the received input vector y.

Abstract: A novel and useful framework, system and method of monitoring one or more performance parameters (e.g., distributed system performance), filtering the performance parameters data collected and identifying one or more performance parameters that affect one or more target performance measures. This can be achieved in the case of a delay parameter, for example, by determining the root-cause of the increased delay and taking corrective actions in order to avoid violation of the timeliness constraints. The present invention is a statistical based performance monitoring mechanism that uses statistical signal processing techniques and is applicable, for example, in soft real-time distributed systems. The monitoring framework efficiently and distributively characterizes the behavior of the varying network conditions as a stochastic process and performs root-cause analysis to detecting the parameters which affect one or more target performance measures, e.g., latency.

Abstract: Methods and systems for processing an input. An input vector y is received that represents a noisy observation of Ax, where A is a data matrix and x is a data vector of unknown variables. Data vector x is recovered from the received input vector y via an iterative method. The recovering comprises determining an inference of a vector of marginal means over a graph G, where the graph G is of a joint Gaussian probability density function p(x) associated with noise in the received input vector y.