Abstract: Aspects of the present disclosure relate to converting between structured and tabular data formats. Data can be received in a tabular format. An array can be built for each of a plurality of objects within the data in the tabular format, each object corresponding to at least one identified header of the identified headers. A data row can be parsed using at least one of the built arrays and data within the data row can be added to the structured format in a specific location based characteristics indicated in the at least one array. Data can also be converted from the structured format into the tabular format using the built arrays.

Abstract: An artificial intelligence (AI) platform to support workflow version process control. One or more workflows corresponding to one or more workflow engines are monitored. A neural network is employed to capture a relationship associated with a detected change in the monitored workflows. The neural network is leveraged to identify and assess an impact of the detected change to one or more additional workflows. Responsive to the assessment, the impacted workflow engines are optimized. The optimization includes automatically mapping and encoding changes corresponding to the impacted workflow. The one or more workflows containing the encoded changes are then executed.

Abstract: Aspects of the present disclosure relate to converting between structured and tabular data formats. Data can be received in a tabular format. An array can be built for each of a plurality of objects within the data in the tabular format, each object corresponding to at least one identified header of the identified headers. A data row can be parsed using at least one of the built arrays and data within the data row can be added to the structured format in a specific location based characteristics indicated in the at least one array. Data can also be converted from the structured format into the tabular format using the built arrays.

Abstract: Provided is a method, computer program product, and system for adjusting a container-based application. A processor may receive one or more function calls at a container-based application. The processor may process the one or more function calls using the container-based application. The processor may obtain performance data from the container-based application based on the processing of the one or more function calls. The processor may analyze the performance data against a performance measure limit. The processor may adjust an aspect of the container-based application based on the analysis of the performance data. The aspect may be one or more selected from the group consisting of a resource and a functionality of the contain-based application.

Abstract: Provided is a method, computer program product, and system for adjusting a container-based application. A processor may receive one or more function calls at a container-based application. The processor may process the one or more function calls using the container-based application. The processor may obtain performance data from the container-based application based on the processing of the one or more function calls. The processor may analyze the performance data against a performance measure limit. The processor may adjust an aspect of the container-based application based on the analysis of the performance data. The aspect may be one or more selected from the group consisting of a resource and a functionality of the contain-based application.