Abstract: Automated reconfiguration of real time data stream processing may be implemented. A processing function that describes one or more operations to be performed with respect to one or more data streams may be executed at one or more processing nodes. Performance metrics describing the performance of the processing function at the processing nodes may be collected and monitored. A reconfiguration event may be detected for the processing function. A different execution configuration for the processing function may be determined and initiated in response to detecting the reconfiguration event.

Abstract: Automated reconfiguration of real time data stream processing may be implemented. A processing function that describes one or more operations to be performed with respect to one or more data streams may be executed at one or more processing nodes. Performance metrics describing the performance of the processing function at the processing nodes may be collected and monitored. A reconfiguration event may be detected for the processing function. A different execution configuration for the processing function may be determined and initiated in response to detecting the reconfiguration event.

Abstract: An event detection system for detecting events related to vehicles based on sensor data from the vehicles. An event detection service on a network may receive streams of sensor data from vehicles and apply machine learning techniques to train models and to analyze the data to detect vehicle-related events. Supervised and unsupervised techniques may be used to train the models and to detect known as well as anomalous events. Events may be detected based on data from one or from multiple vehicles. The system may respond to events by alerting entities and/or signaling vehicle control systems to respond to the events. Trained models may be downloaded to vehicles; the vehicle computer systems can then locally detect events. The downloaded models can be further trained on the vehicles, uploaded to the network, and integrated into the network-based models to improve the performance of the models.

Abstract: Automated reconfiguration of real time data stream processing may be implemented. A processing function that describes one or more operations to be performed with respect to one or more data streams may be executed at one or more processing nodes. Performance metrics describing the performance of the processing function at the processing nodes may be collected and monitored. A reconfiguration event may be detected for the processing function. A different execution configuration for the processing function may be determined and initiated in response to detecting the reconfiguration event.

Abstract: Automated reconfiguration of real time data stream processing may be implemented. A processing function that describes one or more operations to be performed with respect to one or more data streams may be executed at one or more processing nodes. Performance metrics describing the performance of the processing function at the processing nodes may be collected and monitored. A reconfiguration event may be detected for the processing function. A different execution configuration for the processing function may be determined and initiated in response to detecting the reconfiguration event.

Abstract: Disclosed are various embodiments for a framework for time-associated data stream storage, processing, and replication. A plurality of streams of time-associated data are received from a plurality of sources via a network using an application-layer protocol. Each of the plurality of streams is divided into a plurality of fragments. An acknowledgement is sent to each of the plurality of sources for each of the plurality of fragments via the network using the application-layer protocol. Processing is performed on each of the plurality of fragments for individual ones of the plurality of streams. An action is implemented relative to a respective fragment based at least in part on a result of the processing.

Abstract: Real time detection of anomalies may be implemented for a data stream. A data stream may receive data records as input. An evaluation of the data records may be performed as the data records are received utilizing an anomaly detection model that is dynamically generated for a time period that includes the data records. A responsive action may be performed in response to detecting various anomalies, such as providing a notification of the anomaly, requesting a corrective action, invoking further analysis of the detected anomaly. Evaluations of the data record and responsive actions may be performed as specified by a user, in some embodiments.

Abstract: Managed function execution for processing data streams in real time may be. A function that describes one or more operations to be performed with respect to one or more data streams may be received via programmatic interface for a managed stream processing system. Stream processing nodes capable of applying the function may be determined and execution of the one or more operations may be initiated at the stream processing nodes as data records of the data stream are received. Results of the application of the processing function may be provided to one or more destinations specified for the function. Performance metrics may also be collected for the execution of the function and provided to a client that submitted the function.

Abstract: Real time detection of anomalies may be implemented for a data stream. A data stream may receive data records as input. An evaluation of the data records may be performed as the data records are received utilizing an anomaly detection model that is dynamically generated for a time period that includes the data records. A responsive action may be performed in response to detecting various anomalies, such as providing a notification of the anomaly, requesting a corrective action, invoking further analysis of the detected anomaly. Evaluations of the data record and responsive actions may be performed as specified by a user, in some embodiments.

Abstract: Managed function execution for processing data streams in real time may be. A function that describes one or more operations to be performed with respect to one or more data streams may be received via programmatic interface for a managed stream processing system. Stream processing nodes capable of applying the function may be determined and execution of the one or more operations may be initiated at the stream processing nodes as data records of the data stream are received. Results of the application of the processing function may be provided to one or more destinations specified for the function. Performance metrics may also be collected for the execution of the function and provided to a client that submitted the function.

Abstract: Real time detection of anomalies may be implemented for a data stream. A data stream may receive data records as input. An evaluation of the data records may be performed as the data records are received utilizing an anomaly detection model that is dynamically generated for a time period that includes the data records. A responsive action may be performed in response to detecting various anomalies, such as providing a notification of the anomaly, requesting a corrective action, invoking further analysis of the detected anomaly. Evaluations of the data record and responsive actions may be performed as specified by a user, in some embodiments.

Abstract: Managed function execution for processing data streams in real time may be. A function that describes one or more operations to be performed with respect to one or more data streams may be received via programmatic interface for a managed stream processing system. Stream processing nodes capable of applying the function may be determined and execution of the one or more operations may be initiated at the stream processing nodes as data records of the data stream are received. Results of the application of the processing function may be provided to one or more destinations specified for the function. Performance metrics may also be collected for the execution of the function and provided to a client that submitted the function.

Abstract: Managed function execution for processing data streams in real time may be. A function that describes one or more operations to be performed with respect to one or more data streams may be received via programmatic interface for a managed stream processing system. Stream processing nodes capable of applying the function may be determined and execution of the one or more operations may be initiated at the stream processing nodes as data records of the data stream are received. Results of the application of the processing function may be provided to one or more destinations specified for the function. Performance metrics may also be collected for the execution of the function and provided to a client that submitted the function.

Abstract: Creating and executing a distributed stream processing operator graph based on a query. The operator graph includes movable stream algebra operators for processing events received from high volume data streams. The operators are partially compiled and distributed to computing devices for completion of the compilation and subsequent execution. During execution, the operators maintain minimal state information associated with received events via an expiration time assigned to each of the event instances. Additional events are generated and aggregated by the operators for communication to a service responsible for the query.

Abstract: Creating and executing a distributed stream processing operator graph based on a query. The operator graph includes movable stream algebra operators for processing events received from high volume data streams. The operators are partially compiled and distributed to computing devices for completion of the compilation and subsequent execution. During execution, the operators maintain minimal state information associated with received events via an expiration time assigned to each of the event instances. Additional events are generated and aggregated by the operators for communication to a service responsible for the query.

Abstract: Creating and executing a distributed stream processing operator graph based on a query. The operator graph includes movable stream algebra operators for processing events received from high volume data streams. The operators are partially compiled and distributed to computing devices for completion of the compilation and subsequent execution. During execution, the operators maintain minimal state information associated with received events via an expiration time assigned to each of the event instances. Additional events are generated and aggregated by the operators for communication to a service responsible for the query.

Abstract: Creating and executing a distributed stream processing operator graph based on a query. The operator graph includes movable stream algebra operators for processing events received from high volume data streams. The operators are partially compiled and distributed to computing devices for completion of the compilation and subsequent execution. During execution, the operators maintain minimal state information associated with received events via an expiration time assigned to each of the event instances. Additional events are generated and aggregated by the operators for communication to a service responsible for the query.

Abstract: Creating and executing a distributed stream processing operator graph based on a query. The operator graph includes movable stream algebra operators for processing events received from high volume data streams. The operators are partially compiled and distributed to computing devices for completion of the compilation and subsequent execution. During execution, the operators maintain minimal state information associated with received events via an expiration time assigned to each of the event instances. Additional events are generated and aggregated by the operators for communication to a service responsible for the query.

Abstract: Creating and executing a distributed stream processing operator graph based on a query. The operator graph includes movable stream algebra operators for processing events received from high volume data streams. The operators are partially compiled and distributed to computing devices for completion of the compilation and subsequent execution. During execution, the operators maintain minimal state information associated with received events via an expiration time assigned to each of the event instances. Additional events are generated and aggregated by the operators for communication to a service responsible for the query.