The widespread use of information systems has become a valuable source of semi-structured data. In this context, Entity Resolution (ER) emerges as a fundamental task to integrate multiple knowledge bases or identify similarities between data items (i.e., entities). Since ER is an inherently quadratic task, blocking techniques are often used to improve efficiency. Beyond the challenges related to the data volume and heterogeneity, blocking techniques also face two other challenges: streaming data and incremental processing. To address these challenges, we propose PRIME, a novel incremental schema-agnostic blocking technique that utilizes parallelism to enhance blocking efficiency. The proposed technique deals with streaming and incremental data using a distributed computational infrastructure. To improve efficiency, the technique avoids unnecessary comparisons and applies a time window strategy to prevent excessive memory consumption.

Blocking techniques are widely applied in Entity Resolution (ER) approaches as preprocessing step in order to avoid the quadratic cost of the ER task. In this context, heterogeneous data and Big Data emerges as the major challenges that are faced by blocking techniques. In this sense, we propose the novel approach Spark-based Streamlined Metablocking (SS-Metablocking). Moreover, this work proposes the Cardinality-based load balancing technique to be applied in SS-Metablocking in order to improve its efficiency. To improve the effectiveness of the SS-Metablocking, the GWNP pruning algorithm is proposed in this work. Based on the experimental results, we can highlight that the proposed approach presents better results regarding efficiency and effectiveness than the state-of-the-art approach.