Abstract
We develop an algorithm for efficiently joining relations on interval-based attributes with overlap predicates, which, for example, are commonly found in temporal databases. Using a new data structure and a lazy evaluation technique, we are able to achieve impressive performance gains by optimizing memory accesses exploiting features of modern CPU architectures. In an experimental evaluation with real-world datasets our algorithm is able to outperform the state-of-the-art by an order of magnitude.