  Combining the results of different search engines in order to improve upon
their performance has been the subject of many research papers. This has become
known as the "Data Fusion" task, and has great promise in dealing with the vast
quantity of unstructured textual data that is a feature of many Big Data
scenarios. However, no universally-accepted evaluation methodology has emerged
in the community. This makes it difficult to make meaningful comparisons
between the various proposed techniques from reading the literature alone.
Variations in the datasets, metrics, and baseline results have all contributed
to this difficulty.
  This paper argues that a more unified approach is required, and that a
centralised software platform should be developed to aid researchers in making
comparisons between their algorithms and others. The desirable qualities of
such a system have been identified and proposed, and an early prototype has
been developed. Re-implementing algorithms published by other researchers is a
great burden on those proposing new techniques. The prototype system has the
potential to greatly reduce this burden and thus encourage more comparable
results being generated and published more easily.
