Histogram domain ordering for path selectivity estimation

We aim to improve the accuracy of path selectivity estimation in graph databases by intelligently ordering the domain of a histogram used for estimation. This problem has not, to our knowledge, received adequate attention in the research community. We present a novel framework for the systematic study of path ordering strategies in histogram construction and use. In this framework, we introduce new ordering strategies which we experimentally demonstrate lead to significant improvement of the accuracy of path selectivity estimation over current strategies. These positive results highlight the fundamental role that domain ordering plays in the design of effective histograms for efficient and scalable graph query processing.