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Method and storage and manipulation of storage system metrics

A method for storage and manipulation of storage system metrics incorporates a self-describing format wherein each data file includes a header block that contains the description and order of the periodic data. The header block is followed by a data block in which the data items are presented in the order that they appear in the data description block for that category. Two types of data are managed, including Base Metrics and Derived Metrics. Base Metrics are metrics that appear in the data file sent by an agent. Derived Metrics are computed based on a set of functions that derive new metrics from the base metrics as well as from previously defined derived metrics. A subset of the data block or file describes the configuration of the storage system at the time that the data file was created. Thus the data file contains a header section that in addition to describing the metrics also describes the configuration. A performance view component a user interface that facilitates access to the archives, and data manipulation effecting enhanced performance analysis, workload characterization and capacity planning. The performance view component facilitates generation of factory and user defined views of monitored metrics / parameters. Metrics from a storage system can be correlated using the performance view features, and parameters across machines can be correlated as well. System configuration(s) can be viewed and changed via the performance view user interface.
Owner:EMC IP HLDG CO LLC

Systems and methods for collecting, analyzing, and sharing bio-signal and non-bio-signal data

A computer network implemented system for improving the operation of one or more biofeedback computer systems is provided. The system includes an intelligent bio-signal processing system that is operable to: capture bio-signal data and in addition optionally non-bio-signal data; and analyze the bio-signal data and non-bio-signal data, if any, so as to: extract one or more features related to at least one individual interacting with the biofeedback computer system; classify the individual based on the features by establishing one or more brain wave interaction profiles for the individual for improving the interaction of the individual with the one or more biofeedback computer systems, and initiate the storage of the brain waive interaction profiles to a database; and access one or more machine learning components or processes for further improving the interaction of the individual with the one or more biofeedback computer systems by updating automatically the brain wave interaction profiles based on detecting one or more defined interactions between the individual and the one or more of the biofeedback computer systems. A number of additional system and computer implemented method features are also provided.
Owner:INTERAXON

System and device for multi-scale analysis and representation of physiological data

System comprised of a medical device and method for analyzing physiological and health data and representing the most significant parameters at different levels of detail which are understandable to a lay person and a medical professional. Low, intermediate and high-resolution scales can exchange information between each other for improving the analyses; the scales can be defined according to the corresponding software and hardware resources. A low-resolution Scale I represents a small number of primary elements such as intervals between the heart beats, duration of electrocardiographic PQ, QRS, and QT-intervals, amplitudes of P-, Q-, R-, S-, and T-waves. This real-time analysis is implemented in a portable device that requires minimum computational resources. The set of primary elements and their search criteria can be adjusted using intermediate or high-resolution levels. At the intermediate-resolution Scale II, serial changes in each of the said elements can be determined using a mathematical decomposition into series of basis functions and their coefficients. This scale can be implemented using a specialized processor or a computer organizer. At the high-resolution Scale III, combined serial changes in all primary elements can be determined to provide complete information about the dynamics of the signal. This scale can be implemented using a powerful processor, a network of computers or the Internet. The system can be used for personal or group self-evaluation, emergency or routine ECG analysis, or continuous event, stress-test or bed-side monitoring.
Owner:SHUSTERMAN VLADIMIR
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