Open Source Java Synthetic Data Generation Software

benerator is a framework for creating realistic and valid high-volume test data, used for load and performance testing and showcase setup. Data is generated from an easily configurable metadata model and exported to databases, XML, CSV or flat files.

With growing amount of information from multiple sources it has become very hard to relate information to the correct real life entities. Record matching software try to solve this by machine learning techniques. To do this effectively, its necessary to train the record matcher with proper test data which is identical to real life data. Hence, there is a need for a data generator to create the synthetic data to be used for evaluating the quality and capability of record matching software. A data generator creates qualitative test data considering various the real life data glitches entered through various means like human data entry, voice dictation and data scanning. The data generation process is done in many steps like org data creation, data grouping, pair generation, data mutation and matching data patterns. Data generator also mangles field values of generated test data to achieve data errors and co-relate them in real life contexts like Family, Households, Organizations etc

DATA Gen™ Test Data Generator offers facilities to automate the task of creating test data for new or existing data bases. It helps lower the programming effort required, while reducing manual test data generation errors and the ripple effect that they cause on production systems, users and maintenance.

JRandO is a test data generator or better test object generator framework. It can be used in JUnit tests or in performance test (for e.g. using JMeter). It may also be useful in anonymization of data or in a simulation environment.

SyntheaTM is an open-source, synthetic patient generator that models the medical history of synthetic patients. Our mission is to provide high-quality, synthetic, realistic but not real, patient data and associated health records covering every aspect of healthcare. The resulting data is free from cost, privacy, and security restrictions, enabling research with Health IT data that is otherwise legally or practically unavailable. The models used to generate synthetic patients are informed by numerous academic publications. Our synthetic populations provide insight into the validity of this research and encourage future studies in population health. Synthetic data establishes a risk-free environment for Health IT development and experimentation. This includes the evaluation of new treatment models, care management systems, clinical decision support, and more.

A Synthetic Data Generator for producing mixed datasets described by relevant, irrelevant, and redundant features.

This software allows for a user to generate test data. This is useful for testing Hadoop or other data processing clusters.

Platform independent, flexible, easy-to-use and free test data generator for database developers and testers, written in pure Java.