L
Luca Gagliardelli
Researcher at University of Modena and Reggio Emilia
Publications - 24
Citations - 193
Luca Gagliardelli is an academic researcher from University of Modena and Reggio Emilia. The author has contributed to research in topics: Data integration & Big data. The author has an hindex of 6, co-authored 20 publications receiving 86 citations.
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Journal ArticleDOI
Three-dimensional Entity Resolution with JedAI
George Papadakis,Georgios M. Mandilaras,Luca Gagliardelli,Giovanni Simonini,Emmanouil Thanos,George Giannakopoulos,Sonia Bergamaschi,Themis Palpanas,Manolis Koubarakis +8 more
TL;DR: JedAI is an open-source system that puts together a series of state-of-the-art ER techniques that have been proposed and examined independently, targeting parts of the ER end-to-end pipeline, a unique approach as no other ER tool brings together so many established techniques.
Journal ArticleDOI
Reproducible experiments on Three-Dimensional Entity Resolution with JedAI
Georgios M. Mandilaras,George Papadakis,Luca Gagliardelli,Giovanni Simonini,Emmanouil Thanos,George Giannakopoulos,Sonia Bergamaschi,Themis Palpanas,Manolis Koubarakis,Alicia Lara-Clares,Antonio Fariña +10 more
TL;DR: JedAI as mentioned in this paper is 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.
Journal ArticleDOI
Scaling entity resolution: A loosely schema-aware approach
TL;DR: It is demonstrated how “loose” schema information 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.
Proceedings ArticleDOI
SparkER: Scaling Entity Resolution in Spark
TL;DR: The new version of SparkER, an ER tool that can scale practitioners’ favorite ER algorithms, and a supervised mode has been added, which can be assisted in supervising the entire process and in injecting his knowledge in order to achieve the best result.
Journal ArticleDOI
BigBench Workload Executed by using Apache Flink
TL;DR: This paper compares two of the most employed and promising frameworks to manage big data: Apache Flink and Apache Hive, which are general purpose distributed platforms under the umbrella of the Apache Software Foundation.