Abstract: Learned scheduling of autonomous actions includes generating groups of action executions that execute on a collaboration platform. The groups of action executions are generated by natural language processing of contextual information extracted from one or more Chat Operations conversations. Recommendation candidates corresponding to the action executions are generated by clustering action executions contained in each of the groups. The action executions are clustered based on times of past executions. A leaned schedule is generated by ranking the recommendation candidates based on the contextual information. As generated, the learned schedule indicates one or more recommendations to execute a specific action within a specific time.

Abstract: Methods and systems for process analysis include determining attributes of an event log relating to a process. Attributes of an end task of the process are determined based on a comparison of a vector representation of the end task to vector representations to a set of criteria relating to process representations. Hyper-parameters of process mining models are tuned based on the attributes of the event log and the attributes of the end task. The process mining models are ranked based on the attributes of the event log and the attributes of the end task. The event log is mined using a top-ranked process mining model to generate a process representation. An inefficiency of the process is identified based on the process representation. The inefficiency is automatically corrected.

Abstract: An example operation may include one or more of tuning a language model based on dependencies between an original data set and a paraphrase data set of the original data set, parsing and annotating the paraphrase dataset with entity identifiers of predefined entities to generate an annotated paraphrase dataset, additionally tuning the language model based on entity dependencies between the original data set and the paraphrase data set based on the annotated paraphrase dataset, and storing the additionally tuned language model in a storage device.

Abstract: Mechanisms are provided for indexing microservices for optimized querying based on microservice attributes. A plurality of application graph data structures are generated with nodes representing microservices and edges representing functionality of microservices. A data transformation is performed on the graphs to generate, for each node, a corresponding microservice document specifying microservice attributes of the corresponding microservice. A machine learning training operation is executed on an embedding computer model based on a plurality of the microservice documents to train the embedding computer model to learn a representation vector space for representing microservices as vector representations. The trained embedding computer model is executed on the microservice documents to generate corresponding vector representations and compile them into entries of a microservice index data structure which is used to process queries for microservices.

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.