  Spatial objects often come with textual information, such as Points of
Interest (POIs) with their descriptions, which are referred to as geo-textual
data. To retrieve such data, spatial keyword queries that take into account
both spatial proximity and textual relevance have been extensively studied.
Existing indexes designed for spatial keyword queries are mostly built based on
the geo-textual data without considering the distribution of queries already
received. However, previous studies have shown that utilizing the known query
distribution can improve the index structure for future query processing. In
this paper, we propose WISK, a learned index for spatial keyword queries, which
self-adapts for optimizing querying costs given a query workload. One key
challenge is how to utilize both structured spatial attributes and unstructured
textual information during learning the index. We first divide the data objects
into partitions, aiming to minimize the processing costs of the given query
workload. We prove the NP-hardness of the partitioning problem and propose a
machine learning model to find the optimal partitions. Then, to achieve more
pruning power, we build a hierarchical structure based on the generated
partitions in a bottom-up manner with a reinforcement learning-based approach.
We conduct extensive experiments on real-world datasets and query workloads
with various distributions, and the results show that WISK outperforms all
competitors, achieving up to 8x speedup in querying time with comparable
storage overhead.

Databases;Machine Learning