Abstract: Adjusting a computing environment for processing a data stream involves identifying one or more dummy tuples in the data stream processed by the computing environment. Propagation of the identified dummy tuples to be processed through a plurality of operators in the computing environment is controlled in order to control adjustment of the computing environment. The computing environment is adjusted based on the processing of the propagated dummy tuples.

Abstract: Stream applications may inefficiently use the hardware resources that execute the processing elements of the data stream. For example, a compute node may host four processing elements and execute each using a CPU. However, other CPUs on the compute node may sit idle. To take advantage of these available hardware resources, a stream programmer may identify one or more processing elements that may be cloned. The cloned processing elements may be used to generate a different execution path that is parallel to the execution path that includes the original processing elements. Because the cloned processing elements contain the same operators as the original processing elements, the data stream that was previously flowing through only the original processing element may be split and sent through both the original and cloned processing elements. In this manner, the parallel execution path may use underutilized hardware resources to increase the throughput of the data stream.

Abstract: Focus requests to display applications are received, each focus request associated with at least one application from the applications. Application context from metadata tags describing content of the application is received. User context associated with information about a status of a user interacting with the computer system is received. Display priority of the applications based on the application context and the user context is determined. A list of the applications based on the display priority is created. The applications are displayed in windows in the graphical user interface environment based on the list.

Abstract: Focus requests to display applications are received, each focus request associated with at least one application from the applications. Application context from metadata tags describing content of the application is received. User context associated with information about a status of a user interacting with the computer system is received. Display priority of the applications based on the application context and the user context is determined. A list of the applications based on the display priority is created. The applications are displayed in windows in the graphical user interface environment based on the list.

Abstract: A computer-implemented method includes exchanging device data, associated with a first participating user device, with the one or more second participating user devices; receiving audio data associated with a voice command; exchanging the audio data with the one or more second participating user devices; identifying, by the first participating user device, a voice command based on exchanging the audio data; determining which one of the first participating user device or the one or more second participating user devices should respond to the voice command based on details of the voice command and the exchanging the device data; responding to the voice command based on determining that the first participating user device should respond to the voice command; and signaling a particular second participating user device to respond to the voice command based on determining that the particular second participating user device should respond to the voice command.

Abstract: Disclosed aspects relate to debug management in a distributed batch data processing environment which uses a shared pool of configurable computing resources. A debug configuration to fire a breakpoint based on an achievement of a debug criterion may be initiated in the distributed batch data processing environment. A data block may be detected in the distributed batch data processing environment. The data block may be analyzed with respect to the debug criterion by a debug management engine. Achievement of the debug criterion by the data block may be determined by the debug management engine. In response to determining the achievement of the debug criterion by the data block, the breakpoint may be fired based on the achievement of the debug criterion.

Abstract: Managing a streaming environment of an operator graph by performing corrective actions based on a threshold of changes in state being reached. An operator graph includes states of information stored within a memory of a first processing element configured to process a set of tuples. The memory of the first processing element is monitored. A change in the information from a first state to a second state is identified, based on the monitoring. The change from the first state to the second state is recorded. A determination is made if the change from the first state to the second state has caused a threshold of changes between the states of the information to be reached. A corrective action is performed that modifies a configuration of the operator graph in response to the threshold of changes between the states of the information being reached.

Abstract: A computer-implemented method includes exchanging device data, associated with a first participating user device, with the one or more second participating user devices; receiving audio data associated with a voice command; exchanging the audio data with the one or more second participating user devices; identifying, by the first participating user device, a voice command based on exchanging the audio data; determining which one of the first participating user device or the one or more second participating user devices should respond to the voice command based on details of the voice command and the exchanging the device data; responding to the voice command based on determining that the first participating user device should respond to the voice command; and signaling a particular second participating user device to respond to the voice command based on determining that the particular second participating user device should respond to the voice command.

Abstract: Managing a streaming environment of an operator graph by performing corrective actions based on a threshold of changes in state being reached. An operator graph includes states of information stored within a memory of a first processing element configured to process a set of tuples. The memory of the first processing element is monitored. A change in the information from a first state to a second state is identified, based on the monitoring. The change from the first state to the second state is recorded. A determination is made if the change from the first state to the second state has caused a threshold of changes between the states of the information to be reached. A corrective action is performed that modifies a configuration of the operator graph in response to the threshold of changes between the states of the information being reached.

Abstract: A stream of tuples to be processed by a plurality processing elements executing on two or more compute nodes is received. Each compute node stores one or more of the processing elements having one or more stream operators. It is determined whether an overhead parameter associated with a first streams service located at a first stream operator is outside of a first overhead criterion. The first streams service is ended at the first stream operator and a second streams service is instantiated at a second stream operator when the overhead parameter associated with the first streams service is outside of the first overhead criterion. The second stream operator is different from the first stream operator. The method may include determining whether the first streams service samples a first data attribute of tuples or measures performance.

Abstract: Disclosed aspects relate to debug management in a distributed batch data processing environment which uses a shared pool of configurable computing resources. A debug configuration to fire a breakpoint based on an achievement of a debug criterion may be initiated in the distributed batch data processing environment. A data block may be detected in the distributed batch data processing environment. The data block may be analyzed with respect to the debug criterion by a debug management engine. Achievement of the debug criterion by the data block may be determined by the debug management engine. In response to determining the achievement of the debug criterion by the data block, the breakpoint may be fired based on the achievement of the debug criterion.

Abstract: A stream of tuples may be processed by receiving at a first stream operator a first tuple from a stream of tuples. In response to receiving the first tuple, port mutability conditions for a first stream operator and a second stream operator may be analyzed. In response to identifying the port mutability conditions for a first stream operator and a second stream operator, a first set of attribute mutability conditions for the first tuple received at the first stream operator may be identified. Based on the first set of attribute mutability conditions, a reference of an attribute from the first tuple may be generated where the reference is added to a second tuple passing from the first stream operator to the second stream operator.

Abstract: A stream of tuples may be processed by receiving at a first stream operator a first tuple from a stream of tuples. In response to receiving the first tuple, port mutability conditions for a first stream operator and a second stream operator may be analyzed. In response to identifying the port mutability conditions for a first stream operator and a second stream operator, a first set of attribute mutability conditions for the first tuple received at the first stream operator may be identified. Based on the first set of attribute mutability conditions, a reference of an attribute from the first tuple may be generated where the reference is added to a second tuple passing from the first stream operator to the second stream operator.

Abstract: Disclosed aspects relate to debug management in a distributed batch data processing environment which uses a shared pool of configurable computing resources. A debug configuration to fire a breakpoint based on an achievement of a debug criterion may be initiated in the distributed batch data processing environment. A data block may be detected in the distributed batch data processing environment. The data block may be analyzed with respect to the debug criterion by a debug management engine. Achievement of the debug criterion by the data block may be determined by the debug management engine. In response to determining the achievement of the debug criterion by the data block, the breakpoint may be fired based on the achievement of the debug criterion.

Abstract: Stream applications may inefficiently use the hardware resources that execute the processing elements of the data stream. For example, a compute node may host four processing elements and execute each using a CPU. However, other CPUs on the compute node may sit idle. To take advantage of these available hardware resources, a stream programmer may identify one or more processing elements that may be cloned. The cloned processing elements may be used to generate a different execution path that is parallel to the execution path that includes the original processing elements. Because the cloned processing elements contain the same operators as the original processing elements, the data stream that was previously flowing through only the original processing element may be split and sent through both the original and cloned processing elements. In this manner, the parallel execution path may use underutilized hardware resources to increase the throughput of the data stream.

Abstract: Stream applications may inefficiently use the hardware resources that execute the processing elements of the data stream. For example, a compute node may host four processing elements and execute each using a CPU. However, other CPUs on the compute node may sit idle. To take advantage of these available hardware resources, a stream programmer may identify one or more processing elements that may be cloned. The cloned processing elements may be used to generate a different execution path that is parallel to the execution path that includes the original processing elements. Because the cloned processing elements contain the same operators as the original processing elements, the data stream that was previously flowing through only the original processing element may be split and sent through both the original and cloned processing elements. In this manner, the parallel execution path may use underutilized hardware resources to increase the throughput of the data stream.

Abstract: Streamlining a stream computing environment by distributing tuple attributes to associated operators in the environment involves receiving a stream of tuples for processing by one or more stream operators. A tuple in the stream of tuples is analyzed to identify one or more data attributes required by the one or more stream operators during processing. Each of the identified attributes is distributed to particular operators that require the identified attribute.

Abstract: Adjusting a computing environment for processing a data stream involves identifying one or more dummy tuples in the data stream processed by the computing environment. Propagation of the identified dummy tuples to be processed through a plurality of operators in the computing environment is controlled in order to control adjustment of the computing environment. The computing environment is adjusted based on the processing of the propagated dummy tuples.

Abstract: A streams manager intelligently restores multiple consistent regions of streaming applications in a streaming environment to increase performance and reduce overloading of other operators and resources. The streams manager detects a failure that affects multiple consistent regions and determines a preferred schedule to restore the multiple consistent regions by analyzing the relationships of the operators and resources associated with the multiple consistent regions. The streams manager may create a dependency graph that maps the interrelationships of the operators and resources associated with the consistent regions of the streaming application and then consult the dependency graph to determine the schedule to restore the multiple consistent regions affected by the failure.

Abstract: A streams manager intelligently restores multiple consistent regions of streaming applications in a streaming environment to increase performance and reduce overloading of other operators and resources. The streams manager detects a failure that affects multiple consistent regions and determines a preferred schedule to restore the multiple consistent regions by analyzing the relationships of the operators and resources associated with the multiple consistent regions. The streams manager may create a dependency graph that maps the interrelationships of the operators and resources associated with the consistent regions of the streaming application and then consult the dependency graph to determine the schedule to restore the multiple consistent regions affected by the failure.