George Mandilaras, George Papadakis, Luca Gagliardelli, Giovanni Simonini, Emmanouil Thanos, George Giannakopoulos, Sonia Bergamaschi, Themis Palpanas, Manolis Koubarakis, Alicia Lara-Clares, Antonio Fariña

Abstract:
In Papadakis et al. (2020), we presented the latest release of JedAI, an open-source Entity Resolution (ER) system that allows for building a large variety of end-to-end ER pipelines. Through a thorough experimental evaluation, we compared a schema-agnostic ER pipeline based on blocks with another schema-based ER pipeline based on similarity joins. We applied them to 10 established, real-world datasets and assessed them with respect to effectiveness and time efficiency. Special care was taken to juxtapose their scalability, too, using seven established, synthetic datasets. Moreover, we experimentally compared the effectiveness of the batch schema-agnostic ER pipeline with its progressive counterpart. In this companion paper, we describe how to reproduce the entire experimental study that pertains to JedAI’s serial execution through its intuitive user interface. We also explain how to examine the robustness of the parameter configurations we have selected.