FDup: a framework for general-purpose and efficient entity deduplication of record collections

Deduplication is a technique aiming at identifying and resolving duplicate metadata records in a collection. This article describes FDup (Flat Collections Deduper), a general-purpose software framework supporting a complete deduplication workflow to manage big data record collections: metadata recor...

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Published inPeerJ. Computer science Vol. 8; p. e1058
Main Authors De Bonis, Michele, Manghi, Paolo, Atzori, Claudio
Format Journal Article
LanguageEnglish
Published San Diego PeerJ. Ltd 06.09.2022
PeerJ, Inc
PeerJ Inc
Subjects
Algorithms
Big Data
Clustering
Data Science
Decision trees
Deduplication
Digital Libraries
Efficiency
Flexibility
Institutional repositories
Knowledge bases (artificial intelligence)
Matching
Metadata
Optimization
Reproduction (copying)
Scholarly communication
Similarity
Similarity measures
Sliding
Software packages
Workflow
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Summary:Deduplication is a technique aiming at identifying and resolving duplicate metadata records in a collection. This article describes FDup (Flat Collections Deduper), a general-purpose software framework supporting a complete deduplication workflow to manage big data record collections: metadata record data model definition, identification of candidate duplicates, identification of duplicates. FDup brings two main innovations: first, it delivers a full deduplication framework in a single easy-to-use software package based on Apache Spark Hadoop framework, where developers can customize the optimal and parallel workflow steps of blocking, sliding windows, and similarity matching function via an intuitive configuration file; second, it introduces a novel approach to improve performance, beyond the known techniques of “blocking” and “sliding window”, by introducing a smart similarity matching function T-match. T-match is engineered as a decision tree that drives the comparisons of the fields of two records as branches of predicates and allows for successful or unsuccessful early-exit strategies. The efficacy of the approach is proved by experiments performed over big data collections of metadata records in the OpenAIRE Research Graph, a known open access knowledge base in Scholarly communication.
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ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.1058