A Scalability Metric for Parallel Computations on Large, Growing Datasets (like the Web)

One of the greatest challenges facing computations on data crawled from the Web is the (in)ability to scale to such large quantities of data. While some computations are less challenged by this than others, inference on the Semantic Web is certainly limited in this regard. Parallelism has been employed to scale inference to larger datasets, but evaluations of recent works have fallen back on common parallel computing metrics that do not apply to this specific scalability challenge. In this position paper, the name data scaling is given to this scalability challenge, and the metric growth efficiency is defined.