George Papadakis

TL;DR: This work considers 17 state-of-the-art blocking methods and uses 6 popular real datasets to examine the robustness of their internal configurations and their relative balance between effectiveness and time efficiency, and investigates their scalability over a corpus of 7 established synthetic datasets.

TL;DR: This paper systemize blocking methods for clean-clean ER (an inherently quadratic task) over highly heterogeneous information spaces (HHIS) through a novel framework that consists of two orthogonal layers: the effectiveness layer encompasses methods for building overlapping blocks with small likelihood of missed matches and the efficiency layer comprises a rich variety of techniques that significantly restrict the required number of pairwise comparisons.

TL;DR: This paper introduces meta-blocking as a generic procedure that intervenes between the creation and the processing of blocks, transforming an initial set of blocks into a new one with substantially fewer comparisons and equally high effectiveness.

TL;DR: This survey provides an end-to-end view of ER workflows for Big Data, critically review the pros and cons of existing methods, and concludes with the main open research directions.

TL;DR: In this paper, a large number of relevant works under two different but related frameworks, blocking and filtering, are reviewed, and a comprehensive list of the relevant works, discussing them in the greater context is provided.