Abstract: Mechanisms are provided for generating, executing, orchestrating, and monitoring an information technology (IT) incident remediation task workflow. An IT incident notification is received and a knowledge data structure associated with an IT resource corresponding to the IT incident is retrieved. IT remediation task(s) are extracted from the knowledge data structure and correlated with skills in a plurality of predetermined skills. Automated tools are correlated with corresponding skills in the plurality of predetermined skills. An IT incident remediation task workflow is generated based on a matching of skills associated with the IT remediation tasks and automation tools. The generated IT incident remediation task workflow is automatically executed on the at least one IT resource.

Abstract: Described are techniques for generating an adaptive data protocol for an IoT network having a plurality community networks of IoT devices. The techniques include determining a data synchronization policy associated with Internet of Things (IoT) devices contained in a plurality of community networks within an IoT network. The techniques further include determining a data sharing policy associated with the IoT devices in the IoT network. The techniques further include analyzing transactions of the data synchronization policy and the data sharing policy to identify transactional inefficiencies in the data synchronization policy and the data sharing policy. The techniques further include generating an adaptive data protocol to increase transactional efficiency within the IoT network based on the analyzing of the data synchronization policy and the data sharing policy.

Abstract: Performing container scaling and migration for container-based microservices is provided. A first set of features is extracted from each respective microservice of a plurality of different microservices. A number of containers required at a future point in time for each respective microservice of the plurality of different microservices is predicted using a trained forecasting model and the first set of features extracted from each respective microservice. A scaling label and a scaling value are assigned to each respective microservice of the plurality of different microservices based on a predicted change in a current number of containers corresponding to each respective microservice according to the number of containers required at the future point in time for each respective microservice.

Abstract: A processor may receive a media frame collection. The media frame collection may include two or more frames. The processor may identify, from the media frame collection, a location of an entity. The processor may determine whether the location of the entity is within a threshold location in regard to the other two or more frames in the media frame collection. The processor may display the one or more frames that exceed the threshold location to a user.

Abstract: Knowledge gaps in a chatbot are identified with reference to a domain-specific document and a set of QA pairs of the chatbot. Entities and/or entity values associated with the document are compared to the entities and/or entity values of the QA pairs. Entities of the document not associated with the QA pairs are identified as knowledge gaps. The QA pairs and knowledge gaps are ranked by relevance to the domain.

Abstract: Performing container scaling and migration for container-based microservices is provided. A first set of features is extracted from each respective microservice of a plurality of different microservices. A number of containers required at a future point in time for each respective microservice of the plurality of different microservices is predicted using a trained forecasting model and the first set of features extracted from each respective microservice. A scaling label and a scaling value are assigned to each respective microservice of the plurality of different microservices based on a predicted change in a current number of containers corresponding to each respective microservice according to the number of containers required at the future point in time for each respective microservice.

Abstract: A method, computer system, and a computer program product for process optimization is provided. The present invention may include analyzing a business process model comprised of one or more activities. The present invention may include extracting one or more key phrases from one or more event logs, wherein the one or more event logs are based on the business process model. The present invention may include determining a corresponding activity for the one or more extracted key phrases. The present invention may include generating an enriched business process model based on the business process model and one or more derived activities.

Abstract: Knowledge gaps in a chatbot are identified with reference to a domain-specific document and a set of QA pairs of the chatbot. Entities and/or entity values associated with the document are compared to the entities and/or entity values of the QA pairs. Entities of the document not associated with the QA pairs are identified as knowledge gaps. The QA pairs and knowledge gaps are ranked by relevance to the domain.

Abstract: A processor may receive a media frame collection. The media frame collection may include two or more frames. The processor may identify, from the media frame collection, a location of an entity. The processor may determine whether the location of the entity is within a threshold location in regard to the other two or more frames in the media frame collection. The processor may display the one or more frames that exceed the threshold location to a user.