  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).
