In big data sources, real-world entities are typically represented with a variety of schemata and formats (e.g., relational records, JSON objects, etc.). Different profiles (i.e., representations) of an entity often contain redundant and/or inconsistent information. Thus identifying which profiles refer to the same entity is a fundamental task (called Entity Resolution) to unleash the value of big data. The naïve all-pairs comparison solution is impractical on large data, hence blocking methods are employed to partition a profile collection into (possibly overlapping) blocks and limit the comparisons to profiles that appear in the same block together. Meta-blocking is the task of restructuring a block collection, removing superfluous comparisons. Existing meta-blocking approaches rely exclusively on schema-agnostic features, under the assumption that handling the schema variety of big data does not pay-off for such a task. In this paper, we demonstrate how “loose” schema information (i.e., statistics collected directly from the data) can be exploited to enhance the quality of the blocks in a holistic loosely schema-aware (meta-)blocking approach that can be used to speed up your favorite Entity Resolution algorithm. We call it Blast (Blocking with Loosely-Aware Schema Techniques). We show how Blast can automatically extract the loose schema information by adopting an LSH-based step for efficiently handling volume and schema heterogeneity of the data. Furthermore, we introduce a novel meta-blocking algorithm that can be employed to efficiently execute Blast on MapReduce-like systems (such as Apache Spark). Finally, we experimentally demonstrate, on real-world datasets, how Blast outperforms the state-of-the-art (meta-)blocking approaches.
Scaling entity resolution: A loosely schema-aware approach / Simonini, Giovanni; Gagliardelli, Luca; Bergamaschi, Sonia; Jagadish, H. V.. - In: INFORMATION SYSTEMS. - ISSN 0306-4379. - 83(2019), pp. 145-165.
Data di pubblicazione: | 2019 |
Data di prima pubblicazione: | 21-mar-2019 |
Titolo: | Scaling entity resolution: A loosely schema-aware approach |
Autore/i: | Simonini, Giovanni; Gagliardelli, Luca; Bergamaschi, Sonia; Jagadish, H. V. |
Autore/i UNIMORE: | |
Digital Object Identifier (DOI): | http://dx.doi.org/10.1016/j.is.2019.03.006 |
Rivista: | |
Volume: | 83 |
Pagina iniziale: | 145 |
Pagina finale: | 165 |
Codice identificativo ISI: | WOS:000469906000010 |
Codice identificativo Scopus: | 2-s2.0-85063958087 |
Citazione: | Scaling entity resolution: A loosely schema-aware approach / Simonini, Giovanni; Gagliardelli, Luca; Bergamaschi, Sonia; Jagadish, H. V.. - In: INFORMATION SYSTEMS. - ISSN 0306-4379. - 83(2019), pp. 145-165. |
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