Abstract: The present invention discloses a system and method for non-intrusive monitoring and prediction of cardiac function of a user. The system (100) comprises a sensor device (102), a data capturing device (104), a data receiver module (106), an acoustic engine (108) and user devices (110), which communicate by using communication network (112). The sensor device (102) comprises a sensor array to capture micro-vibrations of physiological parameters of a user through a surface/mattress under which the sensor device (102) is positioned. The acoustic engine (108) converts the digital time-based vibration signals into frequency-based acoustic outputs such as physiological condition acoustic signals. Further, the acoustic engine (108) is configured to determine the user's current and future health issues based on the physiological condition acoustic signals. does not require trained medical practitioners to analyze the vibration signal of the user to determine the physiological conditions of the user.

Abstract: A method, computer program product, and computer system for identifying, by a computing device, historical data. Usage data may be forecasted for at least two frequencies of a plurality of frequencies associated with the historical data based upon, at least in part, the historical data. It may be determined that the usage data is forecasted more accurately for a first frequency of the at least two frequencies than for a second frequency of the at least two frequencies. The usage data forecasted for the first frequency of the at least two frequencies may be selected based upon, at least in part, determining that the usage data is forecasted more accurately for the first frequency. Future usage of an item may be predicted based upon, at least in part, the usage data forecasted for the first frequency.

Abstract: A method includes obtaining, in a given log processing node, at least two different types of logs associated with assets of an enterprise system. The method also includes generating, at the given log processing node, frequency scores for terms in unstructured log data of each of the different log types, the generated frequency score for a given term in unstructured log data of a given log type being based on (i) occurrence of the given term in historical logs of the given log type previously processed by log processing nodes and (ii) occurrence of the given term in the obtained logs of the given log type. The method further includes extracting, at the given log processing node, features from the obtained logs based on the frequency scores, detecting events affecting the assets utilizing the extracted features, and modifying a configuration of the assets responsive to detecting the events.

Abstract: A method includes obtaining, in a given log processing node, at least two different types of logs associated with assets of an enterprise system. The method also includes generating, at the given log processing node, frequency scores for terms in unstructured log data of each of the different log types, the generated frequency score for a given term in unstructured log data of a given log type being based on (i) occurrence of the given term in historical logs of the given log type previously processed by log processing nodes and (ii) occurrence of the given term in the obtained logs of the given log type. The method further includes extracting, at the given log processing node, features from the obtained logs based on the frequency scores, detecting events affecting the assets utilizing the extracted features, and modifying a configuration of the assets responsive to detecting the events.

Abstract: A method includes obtaining historical storage resource utilization data for a given set of storage resources of one or more storage systems, and generating a plurality of model-specific storage resource capacity predictions utilizing the historical storage resource utilization data and respective ones of a plurality of time series capacity prediction forecasting models. The method also includes selecting a subset of the model-specific storage resource capacity predictions having one or more designated characteristics, determining an overall storage resource capacity prediction based at least in part on a combination of the selected subset of the model-specific storage resource capacity predictions, and modifying a provisioning of storage resources of the one or more storage systems based at least in part on the overall storage resource capacity prediction.

Abstract: A method includes obtaining historical storage resource utilization data for a given set of storage resources of one or more storage systems, and generating a plurality of model-specific storage resource capacity predictions utilizing the historical storage resource utilization data and respective ones of a plurality of time series capacity prediction forecasting models. The method also includes selecting a subset of the model-specific storage resource capacity predictions having one or more designated characteristics, determining an overall storage resource capacity prediction based at least in part on a combination of the selected subset of the model-specific storage resource capacity predictions, and modifying a provisioning of storage resources of the one or more storage systems based at least in part on the overall storage resource capacity prediction.

Abstract: A method includes monitoring a given workload running on one or more storage systems to obtain performance data, detecting a given potential performance-impacting event affecting the given workload based at least in part on a given portion of the obtained performance data, and generating a visualization of at least the given portion of the obtained performance data. The method also includes providing the generated visualization as input to a machine learning algorithm, utilizing the machine learning algorithm to classify the given potential performance-impacting event as one of (i) a true positive event affecting performance of the given workload and (ii) a false positive event corresponding to one or more changes in storage resource utilization by the given workload, and modifying provisioning of storage resources of the one or more storage systems responsive to classifying the given potential performance-impacting event as a true positive event affecting performance of the given workload.

Abstract: A method, computer program product, and computer system for identifying, by a computing device, historical data. Usage data may be forecasted for at least two frequencies of a plurality of frequencies associated with the historical data based upon, at least in part, the historical data. It may be determined that the usage data is forecasted more accurately for a first frequency of the at least two frequencies than for a second frequency of the at least two frequencies. The usage data forecasted for the first frequency of the at least two frequencies may be selected based upon, at least in part, determining that the usage data is forecasted more accurately for the first frequency. Future usage of an item may be predicted based upon, at least in part, the usage data forecasted for the first frequency.