Abstract: A system and method operates to analyze a data stream, for example, a stream of download times for accessing information through a computer network. The system and method are capable of identifying abnormalities in the data stream compared to previous data in the data stream. More particularly, the system and method is capable of dynamically analyzing a data stream to detect abrupt, severe abnormalities as well subtle changes that occur over time.

Abstract: A system and method operates to analyze a data stream, for example, a stream of download times for accessing information through a computer network. The system and method are capable of identifying abnormalities in the data stream compared to previous data in the data stream. More particularly, the system and method is capable of dynamically analyzing a data stream to detect abrupt, severe abnormalities as well subtle changes that occur over time.

Abstract: A system and method operates to analyze a data stream, for example, a stream of download times for accessing information through a computer network. The system and method are capable of identifying abnormalities in the data stream compared to previous data in the data stream. More particularly, the system and method is capable of dynamically analyzing a data stream to detect abrupt, severe abnormalities as well subtle changes that occur over time.

Abstract: A system and method operates to analyze a data stream, for example, a stream of download times for accessing information through a computer network. The system and method are capable of identifying abnormalities in the data stream compared to previous data in the data stream. More particularly, the system and method is capable of dynamically analyzing a data stream to detect abrupt, severe abnormalities as well subtle changes that occur over time.

Abstract: A system and method operates to analyze a data stream, for example, a stream of download times for accessing information through a computer network. The system and method are capable of identifying abnormalities in the data stream compared to previous data in the data stream. More particularly, the system and method is capable of dynamically analyzing a data stream to detect abrupt, severe abnormalities as well subtle changes that occur over time.

Abstract: Sensors (12) attached to various parameters of a system (11), such as an elevator system or a chiller, provide values (14) of corresponding parameters which are utilized to build (15) an empirical distribution of the process, such as by means of bootstrapping methodology using a five-dimensional Markov chain model. In normal operation thereafter, the sensors are read periodically and in response to events, and an abnormality is determined by comparison of current information against the empirical distribution of the process. Deviations from normal behavior provide quantitative measure of system malfunction or abnormality; eliminating data from one or more sensors in each iteration of processing identifies one or more sensors associated with the abnormality. By utilizing cumulative distribution norm of deviation from normal behavior, the relative health of one system can be compared with the relative health of other, similar or dissimilar systems.