The present disclosure relates to systems and methods for matching electronic activities with record objects based on entity relationships. The method can include accessing a plurality of electronic activities, identifying an electronic activity, identifying a first participant associated with a first entity and a second participant associated with a second entity, determining whether a record object identifier is included in the electronic activity, identifying a first record object of the system of record that includes an instance of the record object identifier, and storing an association between the electronic activity and the first record object. The method can include determining a second record object corresponding to the second entity, identifying, using a matching policy, a third record object linked to the second record object and identifying a third entity, and storing, by the one or more processors, an association between the electronic activity and the third record object.

In some embodiments, a method includes generating, based on distributed ledger data associated with a first distributed ledger-based network (DLN), distributed ledger data associated with a second DLN. The first DLN and the second DLN each is a fork and the distributed ledger data associated with the first DLN include account data associated with a set of accounts. The method includes generating a request to initiate a transaction between a first account and a second account. The method includes authenticating the transaction based on a protocol associated with the second DLN and without using a private cryptographic key of the first account. The method includes sending a signal indicating the transaction was authenticated and storing information associated with the transaction in the distributed ledger data associated with the second DLN.

Requests are received for handling by a cloud computing environment which are then executed by the cloud computing environment. While each request is executing, performance metrics associated with the request are monitored. A vector is subsequently generated that encapsulates information associated with the request including the text within the request and the corresponding monitored performance metrics. Each request is then assigned (after it has been executed) to either a normal request cluster or an abnormal request cluster based on which cluster has a nearest mean relative to the corresponding vector. In addition, data can be provided that characterizes requests assigned to the abnormal request cluster. Related apparatus, systems, techniques and articles are also described.

A method and a device for analyzing a consumption of resources in a computing infrastructure to predict a resource consumption anomaly on a computing device. The method includes determining a plurality of resource consumption modeling functions; determining a correlation between the resource consumption modeling functions; measuring a resource consumption by a measurement of a consumption value of a first resource; and predicting the resource consumption of the computing infrastructure. The predicting includes a calculation of a value of future consumption of a resource to be predicted from the consumption value of the first resource and from a previously calculated correlation between modeling functions.

Systems, methods and computer program products for simulating workflows and activities of physical assets using digital twin models. User-defined simulations are performed by selectin digital twin components being analyzed during the simulation, concentrating the analysis on the selectively defined components and bypassing components that will not be simulated. Users can design the digital twin simulation using one or more available digital twin models. The model can be the most current digital twin model, a previous version of a model or a hybridized model comprising components or portions from multiple versions of the available digital twins. Users can further customize simulations by selecting components or sections of the digital twin model to selectively bypass during the simulation or provide overriding values for non-simulated portions of the digital twin which can be used as entry criteria inputted into the next simulated section or component of the digital twin, to complete the simulation.

The disclosure provides an approach for measuring performance between a virtualized desktop infrastructure (VDI) client running on a client device and a remote computing device. Embodiments include generating, by a performance client on the client device, an event and storing a time associated with generating the event. Embodiments include transmitting, by the VDI client to the remote computing device, a message based on the OS event. Embodiments include determining, by a performance agent on the remote computing device, a time associated with receiving the message at the remote computing device and causing an indication of the time to be displayed in a virtual desktop screen. Embodiments include extracting, by the performance client, from the virtual desktop screen, the time, and determining a performance metric based on the extracted time and the time associated with receiving the message at the remote computing device.

Systems, methods and media are directed to automatically generating a recommendation. Data describing a configuration of a production infrastructure is received, the production infrastructure running the system operating in the production environment. One or more metrics data values indicative of a performance of the system operating in the production environment is retrieved. Expected performance values of the system are received. An augmented decisioning engine compares the metrics data values with the expected performance values. The augmented decisioning engine is trained to provide a recommended configuration of the production infrastructure. Based on the comparing, the augmented decisioning engine is trained to improve subsequent recommendations of configuration of the production infrastructure through a feedback process. The augmented decisioning engine is adjusted based on an indication of whether the configuration of production infrastructure satisfies a threshold metric data value in response to the production infrastructure running the system operating in a production environment.

Aspects of the disclosure relate to a real-time dynamic container optimization computing platform. The real-time dynamic container optimization computing platform may receive a request to create a first processing block and first data associated with the first processing block. The real-time dynamic container optimization computing platform may utilize a plurality of models to select a first computing device for the first processing block. The real-time dynamic container optimization computing platform may generate and deploy a container to the first computing device. The real-time dynamic container optimization computing platform may monitor execution of the container on the first computing device. The real-time dynamic container optimization computing platform may migrate the container to the second computing device if an issue with execution of the container on the first computing device is detected.

A method and a system determine a change of state of an autonomous device, such as an autonomous vehicle. A plurality of performance parameter values obtained by monitoring at least one performance parameter during the autonomous operation of the device is received. A performance quantity quantifying the quality of autonomous operation of the device, in particular the quality of driving of the autonomous vehicle, is determined based on the obtained performance parameter values and information associated with a flux of software and/or hardware related to the autonomous operation of the device. Further, a change of state value for the device is determined based on the performance quantity.

Methods, systems, and non-transitory computer readable medium are described for sensor metrology data integration. A method includes receiving sets of sensor data and sets of metrology data. Each set of sensor data includes corresponding sensor values associated with producing corresponding product by manufacturing equipment and a corresponding sensor data identifier. Each set of metrology data includes corresponding metrology values associated with the corresponding product manufactured by the manufacturing equipment and a corresponding metrology data identifier. The method further includes determining common portions between each corresponding sensor data identifier and each corresponding metrology data identifier. The method further includes, for each of the sensor-metrology matches, generating a corresponding set of aggregated sensor-metrology data and storing the sets of aggregated sensor-metrology data to train a machine learning model. The trained machine learning model is capable of generating one or more outputs for performing a corrective action associated with the manufacturing equipment.