Cardinality estimation is an important step in cost-based database query optimization. The accuracy of the estimates directly affects the ability of an optimizer to identify the most efficient query execution plan correctly. In this paper, we study cardinality estimation of LIKE-queries, i.e., queries that use the LIKE-operator to match a pattern with wildcards against string-valued attributes. While both traditional and machine-learning-based approaches have been proposed to tackle this problem, we argue that they all suffer from drawbacks. Most importantly, many state-of-the-art approaches are not designed for patterns that contain wildcards in-between characters. Based on past research on neural language models, we introduce the LIKE-Pattern Language Model (LPLM) that uses a new language and a novel probability distribution function to capture the semantics of general LIKE-patterns. We also propose a method to generate training data for our model. We demonstrate that our method outperforms state-of-the-art approaches in terms of precision (Q-error), while offering comparable runtime performance and memory requirements.

Processing shortest path queries is a basic operation in many graph problems. Both preprocessing-based and batch processing techniques have been proposed to speed up the computation of a single shortest path by amortizing its costs. However, both of these approaches suffer from limitations. The former techniques are prohibitively expensive in situations where the precomputed information needs to be updated frequently due to changes in the graph, while the latter require coordinates and cannot be used on non-spatial graphs. In this paper, we address both limitations and propose novel techniques for batch processing shortest paths queries using landmarks. We show how preprocessing can be avoided entirely by integrating the computation of landmark distances into query processing. Our experimental results demonstrate that our techniques outperform the state of the art on both spatial and non-spatial graphs with a maximum speedup of 3.61 × in online scenarios.