Abstract: A computer implemented method for predicting equipment failure by monitoring equipment data, the method comprising: generating a first set of predictions by processing equipment data via a plurality of first models of data analysis and machine learning techniques; generating a second set of predictions by processing equipment data via a plurality of second models of data analysis and machine learning techniques; generating, using machine learning techniques, a consensus decision by comparing the first set of predictions and the second set of predictions; estimating, using machine learning techniques, a level of confidence for the consensus decision; and selectively disclosing the consensus decision qualifying a confidence threshold.

Abstract: A method and system of systematic data polling/processing within a networked computing system for augmented/mixed reality display. Including: establishing an overlay governed data stream from a persistent data storage system to an augmented/mixed reality (AR/MR) display device; receiving, over a network, pushed metric data from a plurality of remote IoT devices that are associated with networked assets, the plurality of remote IoT devices not all having the same push frequency, wherein location information of the networked assets is known to the networked computing system; storing received pushed metric data within the persistent data storage system; polling the persistent data storage system for data points from the pushed metric data; generating an overlay template; and/or publishing the pushed metric data that has been polled to the AR/MR display device according to the overlay governed data stream in association with the location data of the remote IoT devices.

Abstract: A method and system of systematic data polling/processing within a networked computing system for augmented/mixed reality display. Including: establishing an overlay governed data stream from a persistent data storage system to an augmented/mixed reality (AR/MR) display device; receiving, over a network, pushed metric data from a plurality of remote IoT devices that are associated with networked assets, the plurality of remote IoT devices not all having the same push frequency, wherein location information of the networked assets is known to the networked computing system; storing received pushed metric data within the persistent data storage system; polling the persistent data storage system for data points from the pushed metric data; generating an overlay template; and/or publishing the pushed metric data that has been polled to the AR/MR display device according to the overlay governed data stream in association with the location data of the remote IoT devices.

Abstract: Some embodiments described herein are directed to memory page or bad block monitoring and retirement algorithms, systems and methods for random access memory (RAM). Reliability issues or errors can be detected for multiple memory pages using one or more retirement criterion. In some embodiments, when reliability errors are detected, it may be desired to remove such pages from operation before they create a more serious problem, such as a computer crash. Thus, bad block retirement and replacement mechanisms are described herein.

Abstract: Methods, apparatuses, systems, and devices are described for improving data durability in a data storage system. In one example method of improving data durability, a hardware failure risk indicator may be determined for each of a plurality of data storage elements in the data storage system. The method may also include storing one or more replicas of a first data object on one or more of the plurality of data storage elements, with a quantity of the one or more replicas and a distribution of the one or more replicas among the plurality of data storage elements being a function of the hardware failure risk indicators for each of the plurality of data storage elements. In some examples, the hardware failure risk indicators may be dynamically updated based on monitored conditions, which may result in dynamic adjustments to the quantity and distribution of the data object replicas.

Abstract: Some embodiments described herein are directed to memory page or bad block monitoring and retirement algorithms, systems and methods for random access memory (RAM). Reliability issues or errors can be detected for multiple memory pages using one or more retirement criterion. In some embodiments, when reliability errors are detected, it may be desired to remove such pages from operation before they create a more serious problem, such as a computer crash. Thus, bad block retirement and replacement mechanisms are described herein.

Abstract: Some embodiments described herein are directed to memory page or bad block monitoring and retirement algorithms, systems and methods for random access memory (RAM). Reliability issues or errors can be detected for multiple memory pages using one or more retirement criterion. In some embodiments, when reliability errors are detected, it may be desired to remove such pages from operation before they create a more serious problem, such as a computer crash. Thus, bad block retirement and replacement mechanisms are described herein.

Abstract: A method and system of systematic data polling/processing within a networked computing system for augmented/mixed reality display. Including: establishing an overlay governed data stream from a persistent data storage system to an augmented/mixed reality (AR/MR) display device; receiving, over a network, pushed metric data from a plurality of remote IoT devices that are associated with networked assets, the plurality of remote IoT devices not all having the same push frequency, wherein location information of the networked assets is known to the networked computing system; storing received pushed metric data within the persistent data storage system; polling the persistent data storage system for data points from the pushed metric data; generating an overlay template; and/or publishing the pushed metric data that has been polled to the AR/MR display device according to the overlay governed data stream in association with the location data of the remote IoT devices.

Abstract: Methods, systems, and devices are described for providing proactive cloud orchestration services for a cloud hardware infrastructure. A health management system may monitor component(s) of the cloud hardware infrastructure. The health management system may determine a failure probability metric for the component(s) based on the monitoring of the component and in consideration of historical information associated with the component, or similar components. The health management system may determine an optimization strategy for the component and, when an optimization decision has been reached, initiate a reconfiguration procedure to implement the optimization strategy. The optimization strategy may provide for mitigating or eliminating the consequences of the component failure associated with data loss, downtime, and the like.

Abstract: A method and system of data storage. First and second data sets are received from a data collection system with a plurality of data collection devices that provide data in at least two different data formats. The data is space-time stamped (collection location, and/or orientation) and sensor data is also stamped along with. A content acquisition module with a narration unit template having first and second narration unit associated with each other tills the narration units in sequence via a data associator according to operator instructions. A data recorder that records filled narration units in association with each other according to a predefined association within a federated database.

Abstract: Systems and methods for data storage power management are described. In one embodiment, the method includes analyzing metrics collected from one or more storage devices, identifying, based at least in part on analyzing the metrics, a pattern of activity for at least one of the one or more storage devices over a predetermined period of time, generating a control model based at least in part on the identified pattern of activity, and modifying a power mode of at least one of the one or more storage devices based on the control mode. The control model indicate a first time period when at least one of the one or more storage devices is active and a second time period when at least one of the one or more storage devices is inactive.

Abstract: Methods, apparatuses, systems, and devices are described for redundancy management for a storage system including a plurality of storage devices. Approaches for redundancy management may involve storage device failure prediction techniques and/or a redundancy value associated with a data file. In one example, a copy of the file may be stored on at least two storage devices. Whether or not to store an additional copy of the file on another storage device may be based at least in part on the redundancy value for the file. In another example, a determination may be made whether to store a copy of the file on another storage device when a storage device storing a copy of the file is predicted to fail. Whether to store a copy of the file on another storage device may be based at least in part on a redundancy value associated with the file.

Abstract: A method is provided for a device comprising at least one sensor providing sensor data. The method includes receiving, by a computer coupled to the device, the sensor data, providing the sensor data to at least one rule detector, evaluating, by the at least one rule detector, instances of sensor data triggered by the at least one rule detector, and determining, by the computer, that a sampling interval has ended, and in response calculating a data quality index based on the instances of triggered sensor data.

Abstract: A method is provided for a device comprising at least one sensor providing sensor data. The method includes receiving, by a computer coupled to the device, the sensor data, providing the sensor data to at least one rule detector, evaluating, by the at least one rule detector, instances of sensor data triggered by the at least one rule detector, and determining, by the computer, that a sampling interval has ended, and in response calculating a data quality index based on the instances of triggered sensor data.

Abstract: A method is provided for a device not having an available history of either failures or degraded performance. The method includes establishing, by a computer coupled to the device, an initial baseline of sensor data from the device, receiving new sensor data after establishing the initial baseline, creating an updated baseline based on the new sensor data, evaluating, by the computer, the new sensor data compared to the updated baseline based on a plurality of different time scales, and determining whether the device is indicating an increased probability of failure or degraded performance based on the evaluated sensor data.

Abstract: Methods, apparatuses, systems, and devices are described for improving data durability in a data storage system. In one example method of improving data durability, a hardware failure risk indicator may be determined for each of a plurality of data storage elements in the data storage system. The method may also include storing one or more replicas of a first data object on one or more of the plurality of data storage elements, with a quantity of the one or more replicas and a distribution of the one or more replicas among the plurality of data storage elements being a function of the hardware failure risk indicators for each of the plurality of data storage elements. In some examples, the hardware failure risk indicators may be dynamically updated based on monitored conditions, which may result in dynamic adjustments to the quantity and distribution of the data object replicas.

Abstract: Systems and methods for data storage power management are described. In one embodiment, the method includes analyzing metrics collected from one or more storage devices, identifying, based at least in part on analyzing the metrics, a pattern of activity for at least one of the one or more storage devices over a predetermined period of time, generating a control model based at least in part on the identified pattern of activity, and modifying a power mode of at least one of the one or more storage devices based on the control mode. The control model indicate a first time period when at least one of the one or more storage devices is active and a second time period when at least one of the one or more storage devices is inactive.

Abstract: Methods, apparatuses, systems, and devices are described for improving data durability in a data storage system. In one example method of improving data durability, a hardware failure risk indicator may be determined for each of a plurality of data storage elements in the data storage system. The method may also include storing one or more replicas of a first data object on one or more of the plurality of data storage elements, with a quantity of the one or more replicas and a distribution of the one or more replicas among the plurality of data storage elements being a function of the hardware failure risk indicators for each of the plurality of data storage elements. In some examples, the hardware failure risk indicators may be dynamically updated based on monitored conditions, which may result in dynamic adjustments to the quantity and distribution of the data object replicas.

Abstract: Systems and methods for data storage power management are described. In one embodiment, the method includes analyzing metrics collected from one or more storage devices, identifying, based at least in part on analyzing the metrics, a pattern of activity for at least one of the one or more storage devices over a predetermined period of time, generating a control model based at least in part on the identified pattern of activity, and modifying a power mode of at least one of the one or more storage devices based on the control mode. The control model indicate a first time period when at least one of the one or more storage devices is active and a second time period when at least one of the one or more storage devices is inactive.

Abstract: Methods, systems, and devices are described for providing proactive cloud orchestration services for a cloud hardware infrastructure. A health management system may monitor component(s) of the cloud hardware infrastructure. The health management system may determine a failure probability metric for the component(s) based on the monitoring of the component and in consideration of historical information associated with the component, or similar components. The health management system may determine an optimization strategy for the component and, when an optimization decision has been reached, initiate a reconfiguration procedure to implement the optimization strategy. The optimization strategy may provide for mitigating or eliminating the consequences of the component failure associated with data loss, downtime, and the like.