Abstract
Graph processing is increasingly popular given the wide range of phenomena represented as graphs (e.g., social media networks, pharmaceutical drug compounds, or fraud networks, among others). The increasing amount of data available requires new approaches to efficiently ingest and process such data. In this research, we describe a solution at a conceptual level in the context of the Graph-Massivizer architecture. Graph-Inceptor aims to bridge the void among ETL tools enabling data transformations required for graph creation and enrichment and supporting connectors to multiple graph storages at a massive scale. Furthermore, it aims to enhance ETL operations by learning from data content and load and making decisions based on machine-learning-based predictive analytics.
