Abstract: Datasets can be synchronized across cloud-based computer environments using computer systems. A data map can be generated for source datasets for a plurality of computing environments, respectively, and an activation function can be determined based on the data map, for simultaneously activating a new dataset of a source system from one of the plurality of computing environments. A generation ID (identification) can be shared from the source system with one or more computing environments of the plurality of the computing environments, in response to receiving the activation function. The new dataset of the source system can be replicated across the one or more computing environments. A data synchronization trigger can be activated, using a synchronization coordinator module of the computer, and the data synchronization can be completed across the plurality of computing environments.

Abstract: The continual retraining of Cognitive Bots (“CogBots”) is of CogBots allows for the adaptation and evolution to ever changing environments. In this retraining process a CogBot response model continually searches the response space for potential responses in which it may shift. An approach for optimizing such retraining of CogBots may be presented herein. The approach may include receiving a prompt at a CogBot retraining framework. The approach may include analyzing the prompt and determining potential responses to the prompt. The approach may include generating a dialogue benchmark for each of the potential responses. The approach may further include generating a decision shift score for the prompt. Further, the approach may additionally include updating the CogBot retraining framework based on the generated decision shift score.

Abstract: A user-initiated request from a user device of a user is detected. The user-initiated request is directed to a product provider, the user-initiated request includes one or more parameters. an anonymized request is created based on the one or more parameters. The anonymized request is sent to the product provider. A provider response is received from the product provider. A targeted response is generated based on the one or more parameters of the user-initiated request and based on the provider response. The targeted response is transmitted to the user device of the user.

Abstract: Embodiments of the present invention provide methods, computer program products, and systems. Embodiments of the present invention can. in response to receiving information, map an environment comprising one or more electronic devices. Embodiments of the present invention can then dynamically update the mapped environment based on the received information. Embodiments of the present invention can then display the updated environment on display areas of respective electronic devices of the one or more electronic devices.

Abstract: Inadvertent data swaps can be prevented by measuring volume of transactions in distributed computing environment to determine locations for potential data swaps; and managing a correlation between a thread identification (ID) and transaction header (ID) for transactions in the distributed computing environment. In some embodiments, the prevention of data swaps can further include performing a data transmission interruption to avoid data swaps at the locations for potential data swaps. When the thread identification (ID) and transaction header (ID) do not match the potential for data swaps can be high.

Abstract: Datasets can be synchronized across cloud-based computer environments using computer systems. A data map can be generated for source datasets for a plurality of computing environments, respectively, and an activation function can be determined based on the data map, for simultaneously activating a new dataset of a source system from one of the plurality of computing environments. A generation ID (identification) can be shared from the source system with one or more computing environments of the plurality of the computing environments, in response to receiving the activation function. The new dataset of the source system can be replicated across the one or more computing environments. A data synchronization trigger can be activated, using a synchronization coordinator module of the computer, and the data synchronization can be completed across the plurality of computing environments.

Abstract: A method, computer program product, and a system where a processor(s) obtains a universe of social media items posted to a social media platform at a given time. The processor(s) determines a likelihood of each item of the universe of social media items becoming viral by linking each item of the universe to a breakout pattern based on predefined dimensions and bucketing each item into a defined grouping by a measure of anticipated virality of the item. The processor(s) applies a susceptible-infected-recovered (SIR) model as a differential equation to the bucketed items to determine an equilibrium and one or more breakout points. The processor(s) determines if a social media item will become viral based on whether the item matches at least one or the one or more breakout points.

Abstract: An approach to generating a learning path framework may be provided. A Cognitive Bot may monitor the knowledge stream of a subject matter expert (SME) to glean insights from the activities and events performed by the SME. The CogBot determine categories within the subject matter. The CogBot may tune a grade scoring engine using the insights gleaned from the knowledge stream as a threshold for the grade scoring module. The knowledge stream of a second user may be monitored by a CogBot. A grade score of the subject matter for the second user may be generated by the grade scoring engine. An expertise level associated with the categories may be determined. A learning path framework may be generated based on the generated grade score and expertise level.

Abstract: Systems and methods for analyzing machine learning of cognitive software robots (CogBots) over time are provided. In implementations, a method includes generating, by a computing device, a graph of historic learning curves based on historic learning data over time for a subject obtained from a primary cognitive software robot (CogBot) and at least one secondary CogBot; generating, by the computing device, a best probable learning curve based on the historic learning curves of the graph, wherein the best probable learning curve is predictive of future learning by the primary CogBot for the subject; and generating, by the computing device, information regarding a current status of the learning of the primary CogBot based on the best probable learning curve.

Abstract: A processor may be configured to perform operations that include computing a benefit-to-resource score for a dataset and selecting an autoencoder architecture based on the benefit-to-resource score. The autoencoder architecture may balance minimizing reconstruction loss with minimizing required storage space based on the benefit-to-resource score. The operations performed by the processor may further include transforming the dataset into transformed data with a transformation function based on the autoencoder architecture and storing the transformed data in a user space.

Abstract: An approach to generating a learning path framework may be provided. A Cognitive Bot may monitor the knowledge stream of a subject matter expert (SME) to glean insights from the activities and events performed by the SME. The CogBot determine categories within the subject matter. The CogBot may tune a grade scoring engine using the insights gleaned from the knowledge stream as a threshold for the grade scoring module. The knowledge stream of a second user may be monitored by a CogBot. A grade score of the subject matter for the second user may be generated by the grade scoring engine. An expertise level associated with the categories may be determined. A learning path framework may be generated based on the generated grade score and expertise level.

Abstract: A user-initiated request from a user device of a user is detected. The user-initiated request is directed to a product provider, the user-initiated request includes one or more parameters. an anonymized request is created based on the one or more parameters. The anonymized request is sent to the product provider. A provider response is received from the product provider. A targeted response is generated based on the one or more parameters of the user-initiated request and based on the provider response. The targeted response is transmitted to the user device of the user.

Abstract: A method, computer program product, and a system where a processor(s) obtains a universe of social media items posted to a social media platform at a given time. The processor(s) determines a likelihood of each item of the universe of social media items becoming viral by linking each item of the universe to a breakout pattern based on predefined dimensions and bucketing each item into a defined grouping by a measure of anticipated virality of the item. The processor(s) applies a susceptible-infected-recovered (SIR) model as a differential equation to the bucketed items to determine an equilibrium and one or more breakout points. The processor(s) determines if a social media item will become viral based on whether the item matches at least one or the one or more breakout points.

Abstract: A method, system and computer program product are disclosed for creating a dynamic heterogeneous IoT network from existing IoT network devices to identify and implement a dynamic response to an event. In an embodiment, the method comprises identifying an occurrence of a specified event; and creating a heterogeneous IoT network to respond to the event, including selecting a master IoT device and identifying one or more participatory IoT devices for the created network from multiple IoT devices from one or more existing IoT networks. The master IoT device is in bidirectional communications with the participatory IoT devices. The master device identifies a response to the specified event, translates the response into commands, and sends the commands to the participatory IoT devices to implement the response. The master IoT device may pass control to another master device in another heterogeneous IoT network if the response is not accomplished.

Abstract: Application programming interface (API) discovery includes receiving source code associated with a computer system, and analyzing the source code to generate domain specific language (DSL) represented within the source code. The DSL is mapped to terms of reference associated with an enterprise, and at least one candidate API is identified based upon the terms of reference. The at last one candidate API is mapped to a portion of the source code. One or more patterns are identified between terms in the portion of source code. A source code component of the source code representative of a separate functional component within the source code is identified based upon the one or more patterns. The source code component is mapped to an enabling API.

Abstract: Attribute-based application programming interface (API) comparative benchmarking is provided. In response to determining that a target API maps to an existing API classification based on attributes of the target API, a weighted average of benchmark confidence scores of other APIs in a same class as the target API is determined. A benchmark confidence score is determined for the target API based on feedback, reviews, and ratings. The benchmark confidence score of the target API is compared with the weighted average of benchmark scores. An attribute-based API classification mapping is updated based on the comparison. Pricing for the target API is determined based on a weighted average of API pricing across the other APIs in the same class as the target API.

Abstract: Attribute-based application programming interface (API) comparative benchmarking is provided. In response to determining that a target API maps to an existing API classification based on attributes of the target API, a weighted average of benchmark confidence scores of other APIs in a same class as the target API is determined. A benchmark confidence score is determined for the target API based on feedback, reviews, and ratings. The benchmark confidence score of the target API is compared with the weighted average of benchmark scores. An attribute-based API classification mapping is updated based on the comparison. Pricing for the target API is determined based on a weighted average of API pricing across the other APIs in the same class as the target API.

Abstract: A method, system and computer program product are disclosed for creating a dynamic heterogeneous IoT network from existing IoT network devices to identify and implement a dynamic response to an event. In an embodiment, the method comprises identifying an occurrence of a specified event; and creating a heterogeneous IoT network to respond to the event, including selecting a master IoT device and identifying one or more participatory IoT devices for the created network from multiple IoT devices from one or more existing IoT networks. The master IoT device is in bidirectional communications with the participatory IoT devices. The master device identifies a response to the specified event, translates the response into commands, and sends the commands to the participatory IoT devices to implement the response. The master IoT device may pass control to another master device in another heterogeneous IoT network if the response is not accomplished.