Nowadays, there is a trend in automating repetitive tasks in order to reduce human errors or costs. Digitization asks for new strategies in business processes. Robotic Process Automation (RPA) includes a set of emerging technologies that promises the automation of business processes by using software robots that are trained based on human tasks. Companies need to constantly monitor their own business processes in order to identify and optimize processes suitable for automation. In 2018, Forrester identified UiPath, Automation Anywhere, and Blue Prism as being the leaders providing RPA solutions by using 30-criteria evaluation. This paper examines ten case studies of companies integrating RPA and presents a series of lessons provided by practice. Not all processes are suitable for automation, all case studies identifying five fundamental criteria to be considered.
Over the last 20 years, research groups focused on automating the process of extracting valuable information from Natural Language text in order to discover data and process models. In this context, several tools and approaches have been proposed. The overall objective of this survey is to examine existing literature works that transform textual specifications into visual models. This paper aims to give a comprehensive account of the existing tools meant to discover data and process models from natural language text. Our analysis focuses on approaches of these tools in the model extraction process and highlight issues of each proposed approach. In the case of object oriented software modelling of data models extraction we analyze the degree of automation, efficiency and completeness of the transformation process. Regarding process models extraction, the study is not limited only to business process discovery, but it also provides case studies from several fields such as medical or archaeological. Even if not all the tools developed are clearly depicting a Natural Language Processing technique, a review of each approach is presented.
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