Abstract: A computer system where pre-existing virtual models of physical assets, processes and/or computer system supply input data streams to a streaming analytics application through respective stream operators. The streaming analytics application uses this input data to make improvements to the code and/or configuration of the streaming analytics application itself and/or to create newly-created virtual model(s).

Abstract: Computer software executing on computer hardware that performs the following operations: (i) training a machine learning model to determine allocations of computing resources to processing elements of a stream processing job according to a specified objective; and (ii) allocating a set of computing resources to the processing elements by: allocating to the processing elements a first subset of the set of computing resources based, at least in part, on a minimum resource requirement for the processing elements, and allocating to the processing elements a second subset of the set of computing resources based, at least in part, on an allocation determined using the trained machine learning model.

Abstract: In response to receiving a flow of data, one or more operators in a parallel region included in a distributed data processing system for stream processing the flow of data are initialized. In response to identifying a change to the flow of data or a change to the distributed data processing system, at least one of the flow of data and a size of the parallel region included in the distributed data processing system are dynamically adjusted. The flow of data is processed via the dynamically adjusted one of the flow of data and the parallel region included in the distributed data processing system.

Abstract: A stream of tuples to be processed by processing elements that operate on one or more compute nodes of a stream application is monitored. Each processing element has one or more stream operators. A program request to execute a set of program code instructions is received. A stream application target of the set of program code instructions is identified based on the program request. A portion of the stream application is encapsulated with a generic program execution structure during operation of the stream application. The structure is configured to receive and execute program code instructions outside of a preconfigured operation of the stream application. The set of program code instructions is executed by the structure during operation of the stream application, at a time period of execution of the portion, in response to the program request, and based on the stream application target.

Abstract: At least one job in a data processing environment is split into two or more processing elements. A determination is made whether at least one processing element of the two or more processing elements include sensitive data. In response to determining that at least one processing element of the two or more processing elements includes sensitive data, a set of secure nodes in the data processing environment which are available for processing are identified. A first subset of optimal nodes in the set of secure nodes for processing the at least one processing element is determined based on a security state of each node in the set of secure nodes. The at least one processing element is assigned to one or more nodes in the first subset of optimal nodes.

Abstract: A stream of tuples is monitored. The stream of tuples is to be processed by a plurality of processing elements of a stream application that operate on one or more compute nodes, each processing element having one or more stream operators. A processing rate of a first stream operator of the stream application is calculated. The processing rate is based on the number of tuples that are processed by the first stream operator. It is determined that the processing rate of the first stream operator meets a predetermined tuple processing criterion. The processing rate of the first stream operator is adjusted based on the predetermined tuple processing criterion.

Abstract: A stream of tuples to be processed by processing elements that operate on one or more compute nodes of a stream application is monitored. Each processing element has one or more stream operators. A program request to execute a set of program code instructions is received. A stream application target of the set of program code instructions is identified based on the program request. A portion of the stream application is encapsulated with a generic program execution structure during operation of the stream application. The structure is configured to receive and execute program code instructions outside of a preconfigured operation of the stream application. The set of program code instructions is executed by the structure during operation of the stream application, at a time period of execution of the portion, in response to the program request, and based on the stream application target.

Abstract: Computer software that causes a stream processing management node to perform the following operations: (i) establishing data communication between the stream processing management node and a plurality of stream processing instances executing on respective computing environments in a multi-environment computing system; (ii) distributing or more processing units of a stream processing job to a first set of stream processing instances of the plurality of stream processing instances; (iii) receiving, from the one or more stream processing instances of the first set of stream processing instances, processing results associated with the one or more processing units of the stream processing job; and (iv) performing a machine learning based stream management operation based, at least in part, on the received processing results.

Abstract: Computer software executing on computer hardware that performs the following operations: (i) training a machine learning model to determine allocations of computing resources to processing elements of a stream processing job according to a specified objective; and (ii) allocating a set of computing resources to the processing elements by: allocating to the processing elements a first subset of the set of computing resources based, at least in part, on a minimum resource requirement for the processing elements, and allocating to the processing elements a second subset of the set of computing resources based, at least in part, on an allocation determined using the trained machine learning model.

Abstract: In response to receiving a flow of data, one or more operators in a parallel region included in a distributed data processing system for stream processing the flow of data are initialized. In response to identifying a change to the flow of data or a change to the distributed data processing system, at least one of the flow of data and a size of the parallel region included in the distributed data processing system are dynamically adjusted. The flow of data is processed via the dynamically adjusted one of the flow of data and the parallel region included in the distributed data processing system.

Abstract: A stream of tuples is monitored. The stream of tuples is to be processed by a plurality of processing elements of a stream application that operate on one or more compute nodes, each processing element having one or more stream operators. A processing rate of a first stream operator of the stream application is calculated. The processing rate is based on the number of tuples that are processed by the first stream operator. It is determined that the processing rate of the first stream operator meets a predetermined tuple processing criterion. The processing rate of the first stream operator is adjusted based on the predetermined tuple processing criterion.

Abstract: A method, apparatus, system, and computer program product for detecting and obscuring references that inadvertently disclose key events according to a user's progress through a media. A computer system ingests data from the media and conceptualizes events from the data. The computer system identifies the key events in the media based on the conceptualized events. The computer system assigns a value to each identified key event. The computer system intakes the data that tracks a progression through the media. The computer system identifies references in related content data that are associated with the key events having values that exceeds a threshold. The computer system obscures the references to the key events displayed in the related content data according to the progression through the media.

Abstract: Apparatuses, systems and computer-implemented methods are disclosed for augmented reality stimulus removal. A field definition module determines a permitted field of view for a user. A field recognition module identifies a portion of an image stream outside the permitted field of view. The image stream is obtained by an augmented reality device of the user. An overlay module modifies the image stream for display to the user via the augmented reality device. The overlay module modifies the image stream by obscuring the portion of the image stream outside the permitted field of view in response to a restrictive mode being set for the user.

Abstract: A method, apparatus, system, and computer program product for detecting and obscuring references that inadvertently disclose key events according to a user's progress through a media. A computer system ingests data from the media and conceptualizes events from the data. The computer system identifies the key events in the media based on the conceptualized events. The computer system assigns a value to each identified key event. The computer system intakes the data that tracks a progression through the media. The computer system identifies references in related content data that are associated with the key events having values that exceeds a threshold. The computer system obscures the references to the key events displayed in the related content data according to the progression through the media.

Abstract: Apparatuses, systems and computer-implemented methods are disclosed for augmented reality stimulus removal. A field definition module determines a permitted field of view for a user. A field recognition module identifies a portion of an image stream outside the permitted field of view. The image stream is obtained by an augmented reality device of the user. An overlay module modifies the image stream for display to the user via the augmented reality device. The overlay module modifies the image stream by obscuring the portion of the image stream outside the permitted field of view in response to a restrictive mode being set for the user.