Scalable Similarity Joins for Fast and Accurate Record Deduplication in Big Data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F24%3A39921108" target="_blank" >RIV/00216275:25530/24:39921108 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-031-60328-0_18" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-60328-0_18</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-60328-0_18" target="_blank" >10.1007/978-3-031-60328-0_18</a>

Result language

angličtina

Original language name

Scalable Similarity Joins for Fast and Accurate Record Deduplication in Big Data

Original language description

Record linkage is the process of matching records from multiple data sources that refer to the same entities. When applied to a single data source, this process is known as deduplication. With the increasing size of data source, recently referred to as big data, the complexity of the matching process becomes one of the major challenges for record linkage and deduplication. In recent decades, several blocking, indexing and filtering techniques have been developed. Their purpose is to reduce the number of record pairs to be compared by removing obvious non-matching pairs in the deduplication process, while maintaining high quality of matching. Currently developed algorithms and traditional techniques are not efficient, using methods that still lose significant proportion of true matches when removing comparison pairs. This paper proposes more efficient algorithms for removing non-matching pairs, with an explicitly proven mathematical lower bound on recently used stateof-the-art approximate string matching method - Fuzzy Jaccard Similarity. The algorithm is also much more efficient in classification using Density-based spatial clustering of applications with noise (DBSCAN) in log-linear time complexity O(|E| log(|E|)).

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Good Practices and New Perspectives in Information Systems and Technologies : WorldCIST 2024, Volume 6

ISBN

978-3-031-60327-3

ISSN

2367-3370

e-ISSN

2367-3389

Number of pages

11

Pages from-to

"181 "- 191

Publisher name

Springer Nature Switzerland AG

Place of publication

Cham

Event location

Lodž

Event date

Mar 26, 2024

Type of event by nationality

EUR - Evropská akce

UT code for WoS article

001267244400018