Abstract: Techniques regarding adaptive data recovery schemes are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a data management component that can modify a data recovery scheme based on performance data exhibited by a network of data centers and a data recovery requirement. The data recovery scheme can direct a relocation of data within the network.

Abstract: Aspects of the present invention detect a maximum size of a screen within a same virtual machine allowed by configuration data; determine a number of concurrent bots that access the same virtual machine within a predetermined timeframe; split a width and a height of the screen by the number of the concurrent bots; allocate a sub-screen of a split screen to each of the concurrent bots; and execute a code package for each the concurrent bots within an allocated sub-screen within the same virtual machine in the predetermined timeframe.

Abstract: In an approach to improve data migration in application performance monitoring, embodiments execute a synthetic transaction to monitor an application, and monitor network traffic during the execution of the synthetic transaction. Further, embodiments extract a plurality of parameters indicative of the synthetic transaction from the network traffic, and generate a script, based on the extracted plurality of parameters and a predetermined template, that generates the synthetic transaction.

Abstract: Static parameters of a software container are identified that relate to metadata of the software container itself. The software container is assigned to a selected runtime environment based on the static parameters using a first machine learning model. Runtime parameters for the software container are identified by analyzing the software container at runtime. The runtime parameters relate to operations that the software container requires during runtime. Using a second machine learning model, it is determined whether the selected runtime environment matches the runtime parameters. Where the runtime environment matches, the software container continues to run in this environment. Where the runtime environment does not match, the software container is run in a different runtime environment that matches both the static and runtime parameters.

Abstract: Detecting vehicle tracking device anomaly is provided. Data signals are collected via a network from a tracking device installed in a vehicle to form collected data signals. An anomaly is detected in the tracking device by applying a classification algorithm to the collected data signals.

Abstract: A computer implemented method, computer readable storage medium, and computer system of operating a robotic process automation organization network comprising multiple software robots. The method includes receiving a task by a main robot via the network. The main robot is one of the multiple software robots. The task comprises a code package. The method also includes broadcasting a multicast request for services by the main robot to multiple software robots. Each robot is implemented as a computer program on individual computer systems of multiple computer systems via the network. The method further includes receiving a response by at least a portion of the multiple software robots via the network and selecting at least one worker robot from the multiple robots by the main robot and distributing at least a portion of the code package to the at least one worker robot by the main robot.

Abstract: A computer-implemented method for optimize memory configuration of a computer system. The computer-implemented method includes determining available online real storage assigned to the computer system. The method further includes computing, using a machine learning model, a Large Frame Area (LFAREA) value to support large pages used by one or more applications executing on the computer system, wherein the large pages are memory pages larger than a predetermined value. The method further includes dynamically updating the LFAREA value of the computer system to the determined LFAREA value to support the large pages of the computer system.

Abstract: A compression method, system, and computer program product include creating compressed data via a first system from input data, sending information to a second system detailing a compression strategy for the compressed data, and learning, via the second system, from the information how to recreate the input to the first system using the compressed data.

Abstract: A computer-implemented method includes: connecting, by a computing device, to a database using an outbound connection, wherein the computing device is an information technology (IT) product in a private network and the database is outside the private network; receiving, by the computing device, a response from the database, the response including a command; executing, by the computing device, the command; and sending, by the computing device, result data to the database, wherein the result data is data that results from executing the command on the computing device.

Abstract: Systems, methods, and computer programming products leveraging recurrent neural network architectures to proactively predict workload demand of container orchestration platforms. The platform continuously collects metric data from clusters of the platform and train multiple parallel neural networks with different architectures to predict future platform workload demands. At periodic intervals, the registered neural networks in consideration for controlling the scaling operations of the platform are compared against one another to identify the neural network demonstrating the highest performance and/or most accurate workload prediction strategy for scaling the orchestration platform. The selected neural network is enforced as controller for the platform to implement the workload prediction strategy.

Abstract: One or more processors receive resource type and capability information and activity information of workloads of a domain. A first model is generated and trained to map the resource information to the activity information of domain workloads. The activity information is decomposed into a set of activity core elements (ACEs). The one or more processors generate a second model, wherein the second model is trained to predict a set of resource types and resource capabilities of the respective resource types, based on an input of the first set of ACEs decomposed from the activity information of the workloads of the domain. The one or more processors receive a second set of ACEs that are decomposed from activities associated with an unprecedented workload, and the one or more processors generate a predicted set of resources to perform the second set of ACEs.

Abstract: A system may receive a query for a patient evaluation. The system may then receive historical patient information or hospital data, current medical information, patient information, and testing information. The system may then use the received information to generate a set of rules using a neural network and uses the rules to process an evaluation for the patient.

Abstract: Described are techniques for testing robotic process automation (RPA) bots. The techniques including a system comprising a computational environment configured to present a graphical user interface (GUI) on a display. The system further comprises an application configured to run on the computational environment. The system further comprises a robotic process automation (RPA) bot configured to execute tasks on the application using the GUI. The system further comprises an autonomous robot tester (ART) bot configured to execute a plurality of tests to disrupt the RPA bot by manipulating the GUI.

Abstract: A computer-implemented method for managing updates to a knowledge graph with incoming change requests is disclosed. The method comprises creating a snapshot of the knowledge graph, building sub-regions of the knowledge graph, classifying each incoming change to the knowledge graph so that it relates to one of the sub-regions, creating at least one change layer, wherein each of the at least one change layer relates to one of the sub-regions, wherein each of the at least one change layer comprises a portion of the incoming change requests, closing the at least one change layer upon reaching a predetermined threshold value for the at least one change layer, and integrating the at least one change layer into the knowledge graph, thereby generating a updated knowledge graph.

Abstract: Techniques regarding adaptive data recovery schemes are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a data management component that can modify a data recovery scheme based on performance data exhibited by a network of data centers and a data recovery requirement. The data recovery scheme can direct a relocation of data within the network.

Abstract: A tool for network-based package monitoring and recovery. The tool scans a database to identify a gap in network traffic data. The tool determines whether at least one gap in the network traffic data is identified in the database, wherein the at least one gap is a gap in communication between a target device and a communication serve. Responsive to a determination that at least one gap in the network traffic data is identified, the tool determines a missing package from a package archive. The tool determines whether there is active network traffic between the target device and the communication server. Responsive to a determination that there is active network traffic between the target device and the communication server, the tool replays the missing package in the active network traffic between the target device and the communication server.

Abstract: In an approach to improve data migration in application performance monitoring, embodiments execute a synthetic transaction to monitor an application, and monitor network traffic during the execution of the synthetic transaction. Further, embodiments extract a plurality of parameters indicative of the synthetic transaction from the network traffic, and generate a script, based on the extracted plurality of parameters and a predetermined template, that generates the synthetic transaction.

Abstract: Techniques regarding adaptive data recovery schemes are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a data management component that can modify a data recovery scheme based on performance data exhibited by a network of data centers and a data recovery requirement. The data recovery scheme can direct a relocation of data within the network.

Abstract: A tape drive includes a plurality of rollers, a head, a tension sensor, and control circuitry. The rollers are spatially arranged within the tape drive to define a route for tape of a received data cartridge to travel from the received data cartridge to a tape reel. The head includes read elements and write elements configured to read from and write to the tape as tape of the received data cartridge travels along the route. The tension sensor is configured to measure tension of the tape as it travels along the route. The control circuitry is configured to cause a motor to impact the tension to keep the tape within a tension threshold.

Abstract: A method makes container services persistently available. A computing device receives a request for implementation of a user job in a container environment, and assigns the user job to a compute runner agent of a plurality of compute runner agents to execute the user job. Each compute runner agent is associated with a compute container instance having a unique compute container identifier corresponding to the user job. A computing device assigns the user job to a balancer task to monitor progress of the user job, and assigns the user job to a storage agent to store artifacts associated with running the user job. A computing device receives a notification from the balancer task describing whether the runner agent is correctly running the user job. In response to the runner agent incorrectly running the user job, a computing device resurrects the user job in a new compute container instance.