Dealing With Data Heterogeneity in a Data Fusion Perspective: Models, Methodologies, and Algorithms

Dealing with multiple manifestations of the same real-world entity across several data sources is a very common challenge for many modern applications, including life science applications. This challenge is referenced as data heterogeneity in the data management research field where the final aim is often to get a unified or integrated view of the real-world entities represented in the data sources. Data heterogeneity is a long-standing challenge that has attracted much interest in different computer science disciplines. The main aim of the chapter is to show how data heterogeneity problems that are typical of life science application contexts can be afforded by adopting systematic solutions stemming from the computer science field. To this end, it focusses on the main sources of heterogeneity in the life science context, presents the main problems that arise when dealing with heterogeneity, and provides a review of the solutions proposed in the computer science literature.