Abstract: Techniques for query execution against relational databases using connection pooling are described. According to some embodiments, a query processing service is disclosed that enables users to execute queries against target relational databases implemented by a relational database service. The service receives query requests originated by a client application at a web service endpoint and identifies a connection to a particular target database. In some examples, the query request is a Hyper Text Transfer Protocol (HTTP) message comprising a query to be executed by a target database instance within the provider network. The service transmits the query for execution at the target database via the connection and obtains a query result based on the execution of the query. The service transforms the query result into a format suitable for the client and transmits a query response to the client.

Abstract: Inferences may be obtained to handle access requests at a non-relational database system. An access request may be received at a non-relational database system. The non-relational database system may determine that the access request uses a machine learning model to complete the access request. The non-relational database system may cause an inference to be generated using data items for the access request as input to the machine learning model. The access request may be completed using the generated inference.

Abstract: Inferences may be obtained to handle access requests at a non-relational database system. An access request may be received at a non-relational database system. The non-relational database system may determine that the access request uses a machine learning model to complete the access request. The non-relational database system may cause an inference to be generated using data items for the access request as input to the machine learning model. The access request may be completed using the generated inference.

Abstract: In response to receiving a parallel processing job from a customer, a system operated by a computing resource service provider allocates and configures a cluster of computer systems capable of executing the job. In an embodiment, each computer system is configured with a first network stack that allows access to resources of the computing resource service provider and a second network stack that allows access to resources of the customer. In an embodiment, the state of the cluster is monitored by the system via the first network stack. In an embodiment, the system deploys a set of tasks on the cluster for fulfilling the processing job. In an embodiment, the tasks have access to the second network stack so that each task can access resources of the customer.

Abstract: A method and system of a predictive maintenance IoT system comprises receiving a plurality of sensor data over a communications network and determining one or more clusters from the sensor data based on a pre-determined rule set. Further, the sensor data is classified through a machine learning engine and the sensor data is further base-lined through a combination of database architecture, data training architecture, and a base-lining algorithm. Intensity or degree of fault state is mapped to a fuel gauge to be depicted on a user interface and a predictive maintenance state is predicted through a regression model and appropriate alarm is raised for user action.

Abstract: A method and system is provided for locating a fault in a mixed power transmission line. The method is implemented by an Intelligent Electronic Device (IED) of the mixed line. The IED detects a travelling wave from one or more signals received from one or more measurement equipment. Thereafter, the IED identifies a line section with the fault, and generates two or more estimates for the location of the fault based on a time difference between arrival of two peaks of the travelling wave, a velocity of propagation of the travelling wave in the line section identified with the fault, and a length of one or more line sections. The IED determines the location of the fault based on a comparison of each estimate with a threshold, wherein the threshold is estimated based on the one or more signals, equivalent source impedance of each source and total line impedance.

Abstract: A method and system of a machine learning architecture for predictive and preventive maintenance of vacuum pumps. The method includes receiving one of a motor sensor data and a blower sensor data over a communications network. The motor sensor data is classified into one of a vacuum state sensor data and break state sensor data. The vacuum state sensor data is analyzed to detect an operating vacuum level and an alarm is raised when the vacuum state sensor data exceeds a pre-defined safety range. Vacuum break data is classified into one of a clean filter category and clogged filter category and an alarm is raised if an entry under the clogged filter category is detected. The blower sensor data in association with the motor sensor data is analyzed based on machine learning to detect one of a deficient oil level and a deficient oil structure.

Abstract: A system operated by a computing resource service provider maintains a pool of computing resources for use in processing job requests submitted by customers. The system allocates computing resources to the pool in anticipation of future resource needs. In various embodiments, future resource needs can be estimated based on scheduled jobs, or historical job information. In an embodiment, the computing resources are virtual computer systems which may be arranged in a cluster. In response to receiving a parallel processing job from a customer, the system reserves computing resources from the pool for performing the job. In an embodiment, the reserved resources are configured with a network namespace that is able to access to a customer's resources.

Abstract: A method and system of a predictive maintenance IoT system comprises receiving a plurality of sensor data over a communications network and determining one or more clusters from the sensor data based on a pre-determined rule set. Further, the sensor data is classified through a machine learning engine and the sensor data is further base-lined through a combination of database architecture, data training architecture, and a base-lining algorithm. Intensity or degree of fault state is mapped to a fuel gauge to be depicted on a user interface and a predictive maintenance state is predicted through a regression model and appropriate alarm is raised for user action.

Abstract: A method and system of a predictive maintenance IoT system comprises receiving a plurality of sensor data over a communications network and determining one or more clusters from the sensor data based on a pre-determined rule set. Further, the sensor data is classified through a machine learning engine and the sensor data is further base-lined through a combination of database architecture, data training architecture, and a base-lining algorithm. Intensity or degree of fault state is mapped to a fuel gauge to be depicted on a user interface and a predictive maintenance state is predicted through a regression model and appropriate alarm is raised for user action.

Abstract: A method and system is provided for locating a fault in a mixed power transmission line. The method is implemented by an Intelligent Electronic Device (IED) of the mixed line. The IED detects a travelling wave from one or more signals received from one or more measurement equipment. Thereafter, the IED identifies a line section with the fault, and generates two or more estimates for the location of the fault based on a time difference between arrival of two peaks of the travelling wave, a velocity of propagation of the travelling wave in the line section identified with the fault, and a length of one or more line sections. The IED determines the location of the fault based on a comparison of each estimate with a threshold, wherein the threshold is estimated based on the one or more signals, equivalent source impedance of each source and total line impedance.

Abstract: A method is provided for protection in a mixed power transmission line by controlling a switching device connected thereto through an Intelligent Electronic Device (IED). The mixed line has two or more sections with at least one overhead section (10A) and at least one underground section (10B), wherein every two consecutive sections are connected at a junction (10C). The method is implemented by the IED (14), which receives a signal from a measurement equipment. The IED detects a travelling wave from the signal, and determines a first peak of the travelling wave and at least one a peak width, a rise time and a discharge time of the first peak. The IED identifies the section with the fault based on a comparison of at least one of the peak width, the rise time and the discharge time with a corresponding threshold value of each junction, and controls the switching device based on the comparison.

Abstract: A method and system of a predictive maintenance IoT system comprises receiving a plurality of sensor data over a communications network and determining one or more clusters from the sensor data based on a pre-determined rule set. Further, the sensor data is classified through a machine learning engine and the sensor data is further base-lined through a combination of database architecture, data training architecture, and a base-lining algorithm. Intensity or degree of fault state is mapped to a fuel gauge to be depicted on a user interface and a predictive maintenance state is predicted through a regression model and appropriate alarm is raised for user action.

Abstract: A method and system of a machine learning architecture for predictive and preventive maintenance of vacuum pumps. The method includes receiving one of a motor sensor data and a blower sensor data over a communications network. The motor sensor data is classified into one of a vacuum state sensor data and break state sensor data. The vacuum state sensor data is analyzed to detect an operating vacuum level and an alarm is raised when the vacuum state sensor data exceeds a pre-defined safety range. Vacuum break data is classified into one of a clean filter category and clogged filter category and an alarm is raised if an entry under the clogged filter category is detected. The blower sensor data in association with the motor sensor data is analyzed based on machine learning to detect one of a deficient oil level and a deficient oil structure.

Abstract: A method is provided for protection in a mixed power transmission line by controlling a switching device connected thereto through an Intelligent Electronic Device (IED). The mixed line has two or more sections with at least one overhead section (10A) and at least one underground section (10B), wherein every two consecutive sections are connected at a junction (10C). The method is implemented by the IED (14), which receives a signal from a measurement equipment. The IED detects a travelling wave from the signal, and determines a first peak of the travelling wave and at least one a peak width, a rise time and a discharge time of the first peak. The IED identifies the section with the fault based on a comparison of at least one of the peak width, the rise time and the discharge time with a corresponding threshold value of each junction, and controls the switching device based on the comparison.

Abstract: In one embodiment, a self-healing baseband unit for modifying a key parameter indicator (KPI) value includes a processor that executes a real-time platform health processing agent that generates a fault alarm message based on real-time platform health data received from various components of the baseband unit. The baseband unit includes a L1 sub-system connected via a shared memory to a LL2 processing agent. The L2 processing agent includes a data plane processing module for generating control data and a scheduling module. The scheduling module includes a scheduler trade-off module for generating a trade-off value based on the KPI value and the fault alarm message, and an air interface scheduler that modifies primary uplink and downlink transmission schedules based on the trade-off value, a bearer QoS value, and the control data. The KPI is modified by transmission and reception using the modified uplink and downlink transmission schedules.

Abstract: In one embodiment, a self-healing baseband unit for modifying a key parameter indicator (KPI) value includes a processor that executes a real-time platform health processing agent that generates a fault alarm message based on real-time platform health data received from various components of the baseband unit. The baseband unit includes a L1 sub-system connected via a shared memory to a LL2 processing agent. The L2 processing agent includes a data plane processing module for generating control data and a scheduling module. The scheduling module includes a scheduler trade-off module for generating a trade-off value based on the KPI value and the fault alarm message, and an air interface scheduler that modifies primary uplink and downlink transmission schedules based on the trade-off value, a bearer QoS value, and the control data. The KPI is modified by transmission and reception using the modified uplink and downlink transmission schedules.

Abstract: A method and system of a predictive maintenance IoT system comprises receiving a plurality of sensor data over a communications network and determining one or more clusters from the sensor data based on a pre-determined rule set. Further, the sensor data is classified through a machine learning engine and the sensor data is further base-lined through a combination of database architecture, data training architecture, and a base-lining algorithm. Intensity or degree of fault state is mapped to a fuel gauge to be depicted on a user interface and a predictive maintenance state is predicted through a regression model and appropriate alarm is raised for user action.

Abstract: A method and system of a machine learning architecture for predictive and preventive maintenance of vacuum pumps. The method includes receiving one of a motor sensor data and a blower sensor data over a communications network. The motor sensor data is classified into one of a vacuum state sensor data and break state sensor data. The vacuum state sensor data is analyzed to detect an operating vacuum level and an alarm is raised when the vacuum state sensor data exceeds a pre-defined safety range. Vacuum break data is classified into one of a clean filter category and clogged filter category and an alarm is raised if an entry under the clogged filter category is detected. The blower sensor data in association with the motor sensor data is analyzed based on machine learning to detect one of a deficient oil level and a deficient oil structure.

Abstract: A system for creating logical RLC and MAC PDUs in a mobile communication system includes first and second remote network entities that communicate using the LTE link-layer protocol. The first and second remote network entities include first and second layer-2 protocol stacks, respectively. The first layer-2 protocol stack includes first PDCP, RLC and MAC sub-layers and the second layer-2 protocol stack includes second PDCP, RLC and MAC sub-layers. During transmission of data from the first remote network entity to the second remote network entity, the logical RLC and MAC PDUs are created by the first RLC and MAC sub-layers by populating logical RLC and MAC PDU structures.