Abstract: A system including: a processor; and a memory storing computer program code that controls the processor to: collect real-time business process metrics; collect real-time cluster metrics for a plurality of application clusters indicative of a required allotment of infrastructure resources for a given business process level; estimate a predicted future business process level; based on the estimated predicted future business process level and the real-time cluster metrics, predict a future infrastructure resource requirement of each of the plurality of application clusters; compare the predicted future infrastructure resource requirement of each of the plurality of application clusters to a current dedication of each of the plurality of application clusters; automatically adjust, in real-time and based on the comparison, respective allotments of infrastructure resources for each of the plurality of application clusters; receive an actual business process; and process the business process across the plurality of

Abstract: A system including: a processor; and a memory storing computer program code that controls the processor to: collect real-time business process metrics; collect real-time cluster metrics for a plurality of application clusters indicative of a required allotment of infrastructure resources for a given business process level; estimate a predicted future business process level; based on the estimated predicted future business process level and the real-time cluster metrics, predict a future infrastructure resource requirement of each of the plurality of application clusters; compare the predicted future infrastructure resource requirement of each of the plurality of application clusters to a current dedication of each of the plurality of application clusters; automatically adjust, in real-time and based on the comparison, respective allotments of infrastructure resources for each of the plurality of application clusters; receive an actual business process; and process the business process across the plurality of

Abstract: A system including: a processor; and a memory storing computer program code that controls the processor to: collect real-time business process metrics; collect real-time cluster metrics for a plurality of application clusters indicative of a required allotment of infrastructure resources for a given business process level; estimate a predicted future business process level; based on the estimated predicted future business process level and the real-time cluster metrics, predict a future infrastructure resource requirement of each of the plurality of application clusters; compare the predicted future infrastructure resource requirement of each of the plurality of application clusters to a current dedication of each of the plurality of application clusters; automatically adjust, in real-time and based on the comparison, respective allotments of infrastructure resources for each of the plurality of application clusters; receive an actual business process; and process the business process across the plurality of

Abstract: Disclosed herein are various embodiments for a performance metric monitoring and feedback system. An embodiment operates by determining a first performance metric for a first application available to a plurality of users of the first application operating first user devices. A second performance metric for a second application available to a plurality of users of the second application operating second user devices is determined. A real-time performance of both the first application and the second application is monitored across the set of computing devices. A user interface that simultaneously displays both the first performance metric for the first application and the second performance metric for the second application accessible to one or more members of the organization in accordance with the display format of the metric template is generated.

Abstract: A system including: a processor; and a memory storing computer program code that controls the processor to: collect real-time business process metrics; collect real-time cluster metrics for a plurality of application clusters indicative of a required allotment of infrastructure resources for a given business process level; estimate a predicted future business process level; based on the estimated predicted future business process level and the real-time cluster metrics, predict a future infrastructure resource requirement of each of the plurality of application clusters; compare the predicted future infrastructure resource requirement of each of the plurality of application clusters to a current dedication of each of the plurality of application clusters; automatically adjust, in real-time and based on the comparison, respective allotments of infrastructure resources for each of the plurality of application clusters; receive an actual business process; and process the business process across the plurality of

Abstract: A system including: a processor; and a memory storing computer program code that controls the processor to: collect real-time business process metrics; collect real-time cluster metrics for a plurality of application clusters indicative of a required allotment of infrastructure resources for a given business process level; estimate a predicted future business process level; based on the estimated predicted future business process level and the real-time cluster metrics, predict a future infrastructure resource requirement of each of the plurality of application clusters; compare the predicted future infrastructure resource requirement of each of the plurality of application clusters to a current dedication of each of the plurality of application clusters; automatically adjust, in real-time and based on the comparison, respective allotments of infrastructure resources for each of the plurality of application clusters; receive an actual business process; and process the business process across the plurality of

Abstract: A device may receive data for a plurality of metrics from a set of server resources associated with hosting an application. The plurality of metrics may be related to a performance of the set of server resources. The data may be time series data. The device may normalize the data for the plurality of metrics across a set of points in time to form normalized data. The device may determine a score for the performance of the set of server resources associated with hosting the application at a particular point in time based on the normalized data. The score may be used to determine whether an anomaly is present in the performance of the set of server resources at the particular point in time. The device may perform an action to facilitate improvement of the performance of the set of server resources based on the score satisfying a threshold.

Abstract: Exemplary embodiments provide systems and methods for determining performance metrics or statistics relating to relational databases that are accessed remotely. Such embodiments may automatically discover the presence or identity of such remotely-stored databases using serverless code, query each database for performance information, convert the performance information into performance metrics, and store the performance metrics as time-series data in a time-series database. The performance metrics may be used to generate notifications, provide input to a machine learning process, adjust settings of the relational databases or an associated service, or provide a visualization of the performance of the databases, among other possibilities.

Abstract: Exemplary embodiments provide systems and methods for determining performance metrics or statistics relating to relational databases that are accessed remotely. Such embodiments may automatically discover the presence or identity of such remotely-stored databases using serverless code, query each database for performance information, convert the performance information into performance metrics, and store the performance metrics as time-series data in a time-series database. The performance metrics may be used to generate notifications, provide input to a machine learning process, adjust settings of the relational databases or an associated service, or provide a visualization of the performance of the databases, among other possibilities.

Abstract: A system including: a processor; and a memory storing computer program code that controls the processor to: collect real-time business process metrics; collect real-time cluster metrics for a plurality of application clusters indicative of a required allotment of infrastructure resources for a given business process level; estimate a predicted future business process level; based on the estimated predicted future business process level and the real-time cluster metrics, predict a future infrastructure resource requirement of each of the plurality of application clusters; compare the predicted future infrastructure resource requirement of each of the plurality of application clusters to a current dedication of each of the plurality of application clusters; automatically adjust, in real-time and based on the comparison, respective allotments of infrastructure resources for each of the plurality of application clusters; receive an actual business process; and process the business process across the plurality of

Abstract: A device may receive data for a plurality of metrics from a set of server resources associated with hosting an application. The plurality of metrics may be related to a performance of the set of server resources. The data may be time series data. The device may normalize the data for the plurality of metrics across a set of points in time to form normalized data. The device may determine a score for the performance of the set of server resources associated with hosting the application at a particular point in time based on the normalized data. The score may be used to determine whether an anomaly is present in the performance of the set of server resources at the particular point in time. The device may perform an action to facilitate improvement of the performance of the set of server resources based on the score satisfying a threshold.

Abstract: A device may receive data for a plurality of metrics from a set of server resources associated with hosting an application. The plurality of metrics may be related to a performance of the set of server resources. The data may be time series data. The device may normalize the data for the plurality of metrics across a set of points in time to form normalized data. The device may determine a score for the performance of the set of server resources associated with hosting the application at a particular point in time based on the normalized data. The score may be used to determine whether an anomaly is present in the performance of the set of server resources at the particular point in time. The device may perform an action to facilitate improvement of the performance of the set of server resources based on the score satisfying a threshold.

Abstract: Techniques for providing a secure Fine Timing Measurement (FTM) exchange between two wireless transceivers are disclosed. An example of a method according to the disclosure include transmitting a protected FTM range request message with a Dialog Token of a FTM frame, receiving a protected FTM range report message from a station, wherein the protected FTM range report message includes FTM information, and authenticating the station based at least in part on the FTM information included in the protected FTM range report message.

Abstract: Methods, systems, and devices for wireless communication are described. An access point (AP) transmits instructions to quiet a dynamic frequency selection (DFS) home channel for a period of time. Instructions can include a quiet information element (IE) or a clear-to-send to self (CTS2Self) frame. The AP then allocates a first dedicated RF chain to a first media access control (MAC) component and a second dedicated RF chain, separate from the first dedicated RF chain, to a second MAC component of the access point. The allocation can include reducing the number of RF chains dedicated to the first MAC component. During the time period determined by the instructions, the first dedicated RF chain scans the home DFS channel and the second dedicated RF chain scans at least one other channel (e.g., based at least in part on the time period and channel scan priority) in the same band as the DFS home channel.

Abstract: Methods, systems, and devices for wireless communication are described. An access point (AP) transmits instructions to quiet a dynamic frequency selection (DFS) home channel for a period of time. Instructions can include a quiet information element (IE) or a clear-to-send to self (CTS2Self) frame. The AP then allocates a first dedicated RF chain to a first media access control (MAC) component and a second dedicated RF chain, separate from the first dedicated RF chain, to a second MAC component of the access point. The allocation can include reducing the number of RF chains dedicated to the first MAC component. During the time period determined by the instructions, the first dedicated RF chain scans the home DFS channel and the second dedicated RF chain scans at least one other channel (e.g., based at least in part on the time period and channel scan priority) in the same band as the DFS home channel.

Abstract: Techniques for providing a secure Fine Timing Measurement (FTM) exchange between two wireless transceivers are disclosed. An example of a method according to the disclosure include transmitting a protected FTM range request message with a Dialog Token of a FTM frame, receiving a protected FTM range report message from a station, wherein the protected FTM range report message includes FTM information, and authenticating the station based at least in part on the FTM information included in the protected FTM range report message.

Abstract: A system for automation of processes in a working environment to achieve targeted deliverables, said system comprising: mapping means adapted to map discrete processes in a working environment; reader means adapted to read human instructions in relation to targeted deliverables at mapped discrete processes; interpreter means adapted to interpret said read instructions into a machine readable instruction format; compiler means adapted to compile said machine readable instruction format into a pre-defined operating system executable instruction format; packager means adapted to classify and package said operating system executable instruction format in a pre-defined format; and installer means adapted to install and deploy said packaged format in said working environment to complete the automation process and achieve targeted deliverables.

Abstract: Described embodiments are directed to systems, methods, and apparatuses for dynamically scheduling channel dwell times across two or more channels across a single radio using Multi-Channel Concurrency. Communications may be transmitted and/or received over a first channel of the radio during a first dwell time of a dwell period and over a second channel of the radio during a second dwell time of the dwell period. Some embodiments include adjusting the dwell period and/or dwell time spent actively transmitting or receiving on two or more channels based on measured channel utilization metrics, to drive toward a balanced utilization across the two or more channels. Other embodiments include adjusting the dwell period and/or dwell time of multiple channels based on latency information and/or priority information of at least one of the channels.

Abstract: A position calculation method and apparatus are described. The position calculation apparatus may include an inertial measurement unit and be configured to be coupled with at least one sensor unit for detecting a physical event for use in position calculation. The presence of and type sensor unit may identified, and the position processing to be undertaken may depend on this identification.

Abstract: A position calculating method performed by a position calculating device having a sensor unit carried by or mounted on a mobile body and moving with the mobile body, includes: calculating the position of the mobile body; setting a detection value agreement range of the sensor unit for each of the possibility of moving, possibility of uncertainty, and possibility of stopping of the mobile body, and setting the detection value agreement range of the possibility of moving as the widest range to establish a membership function; determining whether the mobile body is in the stopping condition by predetermined fuzzy inference calculation using the membership function; changing an error parameter used for predetermined Kalman filter process to correct the calculated position by the Kalman filter process according to whether the stopping condition of the mobile body is determined or not; and correcting the calculated position by the Kalman filter process.