BlockingPy submission

[T-Strojny]

Submitting Author: (@T-Strojny)
All current maintainers: @T-Strojny
Package Name: BlockingPy
One-Line Description of Package: Blocking records for record linkage and deduplication with Approximate Nearest Neighbor algorithms.;
Repository Link: https://github.com/ncn-foreigners/BlockingPy
Version submitted: v0.1.7
EiC: TBD
Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
JOSS DOI: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD

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Description

• Include a brief paragraph describing what your package does: BlockingPy is a package that speeds up record linkage and deduplication tasks by using Approximate Nearest Neighbor (ANN) algorithms to create blocks with candidate record pairs. When linking or deduplicating large datasets, comparing all possible record pairs becomes computationally infeasible. BlockingPy solves this by using ANN algorithms to quickly identify similar records while significantly reducing the number of required comparisons.

Scope

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  scope. (If you are unsure of which category you fit, we suggest you make a pre-submission inquiry):

  • Data retrieval
  • Data extraction
  • Data processing/munging
  • Data deposition
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  • Database interoperability

Domain Specific

• Geospatial
• Education

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  • Data processing/munging : BlockingPy transforms raw data into feature vectors and applies ANN algorithms and graphs to reduce the comparison space which enables scalable record linkage and deduplication.

  • Who is the target audience and what are scientific applications of this package?
    BlockingPy is targeted for data scientists, researchers, and analysts working with large datasets that require record matching or deduplication and need a scalable approach.

  • Are there other Python packages that accomplish the same thing? If so, how does yours differ?
    There are many packages around Record Linkage, however ours specializes in the blocking task and uses novel approach which is the use of ANN algorithms.

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1. Please fill out a pre-submission inquiry before submitting a data visualization package. ↩