Task-Driven Big Data Integration

Data integration aims at combining data acquired from different autonomous sources to provide the user with a unified view of this data. One of the main challenges in data integration processes is entity resolution, whose goal is to detect the different representations of the same real-world entity across the sources, in order to produce a unique and consistent representation for it. The advent of big data has challenged traditional data integration paradigms, making the offline batch approach to entity resolution no longer suitable for several scenarios (e.g., when performing data exploration or dealing with datasets that change with a high frequency). Therefore, it becomes of primary importance to produce new solutions capable of operating effectively in such situations. In this paper, I present some contributions made during the first half of my PhD program, mainly focusing on the design of a framework to perform entity resolution in an on-demand fashion, building on the results achieved by the progressive and query-driven approaches to this task. Moreover, I also briefly describe two projects in which I took part as a member of my research group, touching on some real-world applications of big data integration techniques, to conclude with some ideas on the future directions of my research.