Abstract: Disclosed aspects relate to post-compilation configuration management in a stream computing environment to process a stream of tuples. An escalation request may be detected pertaining to a post-compilation phase in the stream computing environment. The escalation request may relate to a requested computing configuration for a process in the stream computing environment. An appropriate computing configuration may be determined for the process in the stream computing environment. The appropriate computing configuration may be determined based on the requested computing configuration for the process in the stream computing environment. The appropriate computing configuration may be established using a containerization technique for the process in the stream computing environment.

Abstract: Disclosed aspects relate to checkpointing a set of stream computing data with respect to a stream computing environment having a set of windowed stream operators including both a first windowed stream operator and a second windowed stream operator. It may be identified that the first windowed stream operator has a first subset of the set of stream computing data. It may be identified that the second windowed stream operator has the first subset of the set of stream computing data. It may be determined to checkpoint the first subset of the set of stream computing data without a redundant checkpoint related to the first and second windowed stream operators. The set of stream computing data may be checkpointed without the redundant checkpoint of the first subset of the set of stream computing data.

Abstract: Disclosed aspects relate to checkpointing a set of stream computing data with respect to a stream computing environment having a set of windowed stream operators including both a first windowed stream operator and a second windowed stream operator. It may be identified that the first windowed stream operator has a first subset of the set of stream computing data. It may be identified that the second windowed stream operator has the first subset of the set of stream computing data. It may be determined to checkpoint the first subset of the set of stream computing data without a redundant checkpoint related to the first and second windowed stream operators. The set of stream computing data may be checkpointed without the redundant checkpoint of the first subset of the set of stream computing data.

Abstract: Disclosed aspects relate to checkpointing a set of stream computing data with respect to a stream computing environment having a set of windowed stream operators including both a first windowed stream operator and a second windowed stream operator. It may be identified that the first windowed stream operator has a first subset of the set of stream computing data. It may be identified that the second windowed stream operator has the first subset of the set of stream computing data. It may be determined to checkpoint the first subset of the set of stream computing data without a redundant checkpoint related to the first and second windowed stream operators. The set of stream computing data may be checkpointed without the redundant checkpoint of the first subset of the set of stream computing data.

Abstract: Disclosed aspects relate to tuple traffic management in a stream computing environment to process a stream of tuples using a set of control group data with respect to a set of operation-system-level virtualization and resource isolation containers. The set of control group data may be collected with respect to a set of containers in the stream computing environment. A tuple flow model may be determined with respect to the set of containers in the stream computing environment based on the set of control group data. The stream of tuples may be processed using the tuple flow model with respect to the set of containers in the stream computing environment.

Abstract: Disclosed aspects relate to tuple traffic management in a stream computing environment to process a stream of tuples using a set of control group data with respect to a set of operation-system-level virtualization and resource isolation containers. The set of control group data may be collected with respect to a set of containers in the stream computing environment. A tuple flow model may be determined with respect to the set of containers in the stream computing environment based on the set of control group data. The stream of tuples may be processed using the tuple flow model with respect to the set of containers in the stream computing environment.

Abstract: Disclosed aspects relate to tuple traffic management in a stream computing environment to process a stream of tuples using a set of control group data with respect to a set of operation-system-level virtualization and resource isolation containers. The set of control group data may be collected with respect to a set of containers in the stream computing environment. A tuple flow model may be determined with respect to the set of containers in the stream computing environment based on the set of control group data. The stream of tuples may be processed using the tuple flow model with respect to the set of containers in the stream computing environment.

Abstract: A system and method for selecting a speech-to-text engine are disclosed. The method includes selecting, by an engine selection component, at least two speech-to-text engines to decode a portion of computer-readable speech data. The portion of speech data can be decoded simultaneously by the selected speech-to-text engines for a designated length of time. In some embodiments portions of the speech data can be simultaneously decoded with selected speech-to-text engines at periodic intervals. An accuracy of decoding can be determined for each selected speech-to-text engine by an accuracy testing component. Additionally, the relative accuracies and speeds of the selected speech-to-text engines can be compared by an output comparison component. The engine selection component can then select the most accurate speech-to-text engine accurate to decode a next portion of speech data. Further, the engine selection module may select a speech-to-text engine that meets or exceeds a speed and/or accuracy threshold.

Abstract: An operator split mechanism analyzes code in a streaming application according to specified split criteria to determine when an operator in the streaming application can be split. At compile-time, when an operator satisfies the split criteria, the operator split mechanism splits the operator according to the split criteria. In an integrated development environment (IDE), the operator split mechanism determines when an operator satisfies the split criteria, and splits the operator according to the split criteria. The operator split mechanism can operate in an automatic mode where operators are split without further input from the user, or in a more interactive mode where the operator split mechanism provides recommendations and options to a user, who makes appropriate selections, and the operator split mechanism then functions according to the selections by the user.

Abstract: Disclosed aspects relate to stream operator management. A stream operator may be analyzed with respect to a set of usage indicators. The stream operator may operate on a computer processor and correspond to a processing element for processing a stream of tuples. Based on analyzing the stream operator, a set of profile data for the stream operator may be determined. The set of profile data for the stream operator may be established for utilization to develop a streaming application.

Abstract: A method and system are provided for implementing user configurable probing with magnetic connections and printed circuit board (PCB) features. A first magnet is located at a desired probe point on the PCB, a probe having a second magnet of suitable polarity and an electrical contact is moved to the probe point. The first and second magnets attract each other, and the probe point makes electrical contact with an electrical conductor at the probe point.

Abstract: A latency reconfiguration mechanism in a streams manager detects latency mismatches in a streaming application that affect the performance of the streaming application, and reconfigures the streaming application to minimize the latency mismatches. The reconfiguration can include fusing adjacent processing elements into a single processing element, co-locating adjacent processing elements on the same machine, moving processing elements and/or network links, adding parallel processing elements, and rearranging processing elements and/or network links to create more consistent latency paths. By minimizing latency mismatches and providing more consistent latency paths, the performance of a streaming application is increased by not having to perform significant reordering of data tuples.

Abstract: A first set of code origination data which corresponds to a first computing object and a second set of code origination data which corresponds to a second computing object may be detected for utilization to develop a streaming application in the stream computing environment. Based on the first and second sets of code origination data, a code assembly arrangement with respect to the first and second computing objects may be determined. Based on the first and second sets of code origination data, a consistent region may be determined to utilize for the code assembly arrangement. The code assembly arrangement may be established, in the consistent region, with respect to the first and second computing objects to develop the streaming application.

Abstract: Disclosed aspects relate to tuple traffic management in a stream computing environment to process a stream of tuples using a set of control group data with respect to a set of operation-system-level virtualization and resource isolation containers. The set of control group data may be collected with respect to a set of containers in the stream computing environment. A tuple flow model may be determined with respect to the set of containers in the stream computing environment based on the set of control group data. The stream of tuples may be processed using the tuple flow model with respect to the set of containers in the stream computing environment.

Abstract: Disclosed aspects relate to checkpointing a set of stream computing data with respect to a stream computing environment having a set of windowed stream operators including both a first windowed stream operator and a second windowed stream operator. It may be identified that the first windowed stream operator has a first subset of the set of stream computing data. It may be identified that the second windowed stream operator has the first subset of the set of stream computing data. It may be determined to checkpoint the first subset of the set of stream computing data without a redundant checkpoint related to the first and second windowed stream operators. The set of stream computing data may be checkpointed without the redundant checkpoint of the first subset of the set of stream computing data.

Abstract: A computing system for tampering detection for digital images is disclosed. The computing system uses a LTI filter to filter time series data representing images in one or both of two videos and/or a still image of a live photo captured by a camera. A causal relationship in time existed between the images in the two videos and the still image when they were captured by the camera. The computing system determines whether the filtered output data representing an image by using the LTI filter is causal. If the computing system determines that the filtered output data representing the image is non-causal, it asserts a signal indicating that the image has been modified after it was captured.

Abstract: Apparatus and methods to determine a change in atmospheric pressure between a forward portion of a flying object and a rear portion of the flying object based on at least one radio frequency (RF) return signal, and to determine a password using the determined change in atmospheric pressure.

Abstract: Aspects of the disclosure relate to managing code origination data for a stream computing environment. A first set of code origination data which corresponds to a first computing object and a second set of code origination data which corresponds to a second computing object may be detected for utilization to develop a streaming application in the stream computing environment. Based on the first and second sets of code origination data, a code assembly arrangement with respect to the first and second computing objects may be determined. The code assembly arrangement may be established with respect to the first and second computing objects to develop the streaming application.

Abstract: Disclosed aspects relate to managing a set of development data for a stream computing environment. A set of development data related to a computing object may be detected. The set of development data may be derived from application development for utilization in the stream computing environment. The set of development data may be established in association with the computing object. A computing artifact which has the computing object in association with the set of development data may be compiled.

Abstract: A latency reconfiguration mechanism in a streams manager detects latency mismatches in a streaming application that affect the performance of the streaming application, and reconfigures the streaming application to minimize the latency mismatches. The reconfiguration can include fusing adjacent processing elements into a single processing element, co-locating adjacent processing elements on the same machine, moving processing elements and/or network links, adding parallel processing elements, and rearranging processing elements and/or network links to create more consistent latency paths. By minimizing latency mismatches and providing more consistent latency paths, the performance of a streaming application is increased by not having to perform significant reordering of data tuples.