Towards an adaptive curation services composition based on machine learning

Data curation deals with managing the data by applying different tasks such as extraction, enrichment, cleaning to fit the purpose of use. Indeed, nowadays, there is an increasing need to implement such tasks in the big data era to maintain data management. Big data is involved in decision processes to perform analysis, visualization, prediction, etc. Thus, there is a dependency between the generated outcomes and the input data of such a process. Therefore, decision process features (e.g., decision context, user constraints, and requirements) need to be taken into account during the data management process, including the data curation phase. Although the proposed curation approaches in the literature are diverse, most of them are static and do not consider the decision process features. Moreover, most of the proposals are dedicated to curating a specific data source format (e.g., structured/unstructured data source). To overcome these limitations, we propose a new approach ACUSEC (Adaptive CUration SErvice Composition) that ensures adaptive curation services composition by considering different features: the source type, the user constraints and preferences, and the decision context. To do so, we rely on AI and machine learning mechanisms such as reinforcement learning. Following the approach's definition, we conducted experiments that show encouraging results in overall execution time and adaptation to the above features.