Abstract: A sharable resource of a first user's environment is identified. The sharable resource is configured as sharable in a shared computer environment. A matching resource that is sufficiently similar to the sharable resource is located. The matching resource is used by pre-existing users of the shared computer environment. Agreement from the pre-existing users for the first user to access the matching resource is obtained. The first user is then provided access to the matching resource.

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: A method for maintaining version consistency of resources. The method provides for one or more processors to receive a submitted request to run a job in which the job includes a processing element and a timestamp associated with running the job. Identification of a resource type associated with the processing element is determined, based on a tag included in the job, associated with the processing element. A version of the resource type of the processing element is determined, based on a mapping of the tag associated with the identified resource type and the timestamp of the job. The resource type of the determined version is requested from a resource manager, and responsive to a confirmation of assigning the version of the resource type from the resource manager, the process element of the job is performed on the version of the resource type assigned by the resource manager.

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 computer deploys one or more stream instances, where each one of the one or more stream instances having an instance deployment manager to control one or more operators of the each of the one or more stream instances. The computer causes the instance deployment manager to collect information of one or more operators in the one or more stream instances The computer may determine a deployment plan for the one or more stream instances based on the information and send one or more instructions to the instance deployment manager to optimize performance of at least one of the one or more stream instances, based on the deployment plan.

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: A sharable resource of a first user's environment is identified. The sharable resource is configured as sharable in a shared computer environment. A matching resource that is sufficiently similar to the sharable resource is located. The matching resource is used by pre-existing users of the shared computer environment. Agreement from the pre-existing users for the first user to access the matching resource is obtained. The first user is then provided access to the matching resource.

Abstract: In an embodiment, a method for controlling a database in a streaming application includes receiving, by a compute node, data for transmission as streaming data on a network connected to the compute node and a database. The compute node then assembles a body portion of a tuple to include the data and assembles a header portion of the tuple to include a database statement that identifies a change to be made by the database to an aspect of the database in a format that is identifiable by the database. The compute node then transmits the tuple having the header portion thereof and the body portion thereof on the network as streaming data with the header and body portions of the tuple being directed to the database.

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: A method, system and computer program product for facilitating remote debugging of parallel regions in stream computing applications. A stream computing management server (SCMS) communicates a list of processing elements to a debugging interface. Responsive to setting a debugging breakpoint for a processing element of the list of processing elements, the SCMS receives a command to enable remote debugging for the selected processing element. In this regard, the processing element is a part of a parallel channel in a distributed processing environment. The SCMS maps the processing element to an attachment information in the distributed environment. The SCMS dynamically attaches a remote debugger to the processing element based on the attachment information.

Abstract: The present invention discloses an automatic laser calibration kit for calibrating the distance between a test device of a wireless charging system and a device under test (DUT). The calibration kit may be located in a wireless charging test system. The test system may comprise a test plane for controlling the DUT and a gripping arm for controlling the test device. The calibration kit may comprise: a laser pointer, configured to emit a laser beam; a mirror, positioned on the gripping arm and configured to reflect the laser beam to form a spot on the test plane; and a camera, configured to monitor the position of the spot.

Abstract: A method for maintaining version consistency of resources. The method provides for one or more processors to receive a submitted request to run a job in which the job includes a processing element and a timestamp associated with running the job. Identification of a resource type associated with the processing element is determined, based on a tag included in the job, associated with the processing element. A version of the resource type of the processing element is determined, based on a mapping of the tag associated with the identified resource type and the timestamp of the job. The resource type of the determined version is requested from a resource manager, and responsive to a confirmation of assigning the version of the resource type from the resource manager, the process element of the job is performed on the version of the resource type assigned by the resource manager.

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 tuple manager of a database system processes partial tuples from a streaming application and stores them in a database. The partial tuples may include a large object (LOB) that arrives at the database at a different time than the rest of the corresponding tuple. A tuple manager stores partial tuples and uses a partial tuples index to track the partial tuples and coordinate recombination of corresponding partial tuples. The database allows queries to be run on the partial data before the tuples are reconstructed allowing faster access to potentially important data before the arrival and processing of a partial tuple such as an LOB.