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.

本文提出 WISK，一种适用于空间关键词查询的自适应的学习式索引，通过结合机器学习模型，将结构化空间属性和非结构化文本信息相结合，实现了针对查询工作负载的优化查询成本，并且在真实数据和查询工作负载中获得了更快的查询时间和存储开销.

WISK：一种面向工作负载的学习索引，用于空间关键字查询

WISK: A Workload-aware Learned Index for Spatial Keyword Queries

Large-scale knowledge graphs are increasingly common in many domains. Their
large sizes often exceed the limits of systems storing the graphs in a
centralized data store, especially if placed in main memory. To overcome this,
large knowledge graphs need to be partitioned into multiple sub-graphs and
placed in nodes in a distributed system. But querying these fragmented
sub-graphs poses new challenges, such as increased communication costs, due to
distributed joins involving cut edges. To combat these problems, a good
partitioning should reduce the edge cuts while considering a given query
workload. However, a partitioned graph needs to be continually re-partitioned
to accommodate changes in the query workload and maintain a good average
processing time. In this paper, an adaptive partitioning method for large-scale
knowledge graphs is introduced, which adapts the partitioning in response to
changes in the query workload. Our evaluation demonstrates that the performance
of processing time for queries is improved after dynamically adapting the
partitioning of knowledge graph triples.

本篇文章提出了一种自适应分区方法，可应对大规模知识图谱中查询工作量变化的需求，通过动态适应知识图谱三元组的分区，可以提高查询处理时间的性能。