Simulating Name-like Vectors for Testing Large-scale Entity Resolution

TL;DR

This paper introduces a simple, scalable, and efficient method for simulating name-like vectors to generate large datasets for testing large-scale entity resolution methods, addressing limitations of existing simulation tools.

Contribution

It proposes a novel vector-based simulation model that approximates name properties, enabling scalable testing of ER systems without detailed record simulation.

Findings

The simulation model is simple and fast.

It effectively mimics name properties for ER testing.

The approach reduces complexity and resource requirements.

Abstract

Accurate and efficient entity resolution (ER) has been a problem in data analysis and data mining projects for decades. In our work, we are interested in developing ER methods to handle big data. Good public datasets are restricted in this area and usually small in size. Simulation is one technique for generating datasets for testing. Existing simulation tools have problems of complexity, scalability and limitations of resampling. We address these problems by introducing a better way of simulating testing data for big data ER. Our proposed simulation model is simple, inexpensive and fast. We focus on avoiding the detail-level simulation of records using a simple vector representation. In this paper, we will discuss how to simulate simple vectors that approximate the properties of names (commonly used as identification keys).