Customers of analytic software tools use the software to analyse their datasets. Such customers may encounter performance related issues that are data dependent and which occur only when very large volumes of data are analysed. Customers are often not able to provide the dataset to the supplier of the analytic software to help in the investigation of issues, due to confidentiality concerns and/or due to the difficulty in transporting the quantity of the data. Without access to the customer's actual datasets, the suppliers of analytic software tools may find it difficult to reproduce or understand the problems being experienced by the customer.
Suppliers of analytic software tools may attempt to replicate the issues encountered by the customer by using other similar datasets or by trying to generate synthetic data which resembles the customer's data. Often the success of this approach depends on how accurately the synthetic data resembles the original data. One known strategy is to guide the generation process using a data mining model built by the customer on their dataset. The data mining model does not contain any of actual data, but does describe rules, patterns and/or conditions that the actual data has been found to obey generally. Such models are typically small in size, and can usually be considered by a customer to contain no confidential information, so therefore they can be passed to the analytic software supplier.
One such approach is described in a paper by Eno and Thompson based on C&RT decision trees, a type of data mining model, entitled “Generating Synthetic Data to Match Data Mining Patterns”, IEEE Internet Computing, June 2008, see http://csce.uark.edu/˜cwt/DOCS/2008-06—IEEE-Internet-Computing—Reverse-Data-Mining—Eno-Thompson.pdf. A straightforward application of these approaches relies on the software supplier's test environment having the capacity to store large volumes of data to match those of a typical customer system. Also, the supplier may need to perform testing on behalf of many customers at the same time, leading to a likely bottleneck on storage resource.