Scalable Processing of Spatial-Keyword Queries

Mahmood, Ahmed; Aref, Walid G.

doi:10.1007/978-3-031-01867-1

Cited by 3 publications

(1 citation statement)

References 158 publications

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“…However, state-of-the-art spatial keyword indexes still have several drawbacks. First, as shown in previous studies [11,13,40], no existing index can work efficiently for all data distributions, and there is no single approach that dominates all others. Second, traditional indexes may have some parameters to be set with fixed values across the entire input data space.…”

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confidence: 99%

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

Sheng¹,

Cao²,

Fang³

et al. 2023

Preprint

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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 8× speedup in querying time with comparable storage overhead.

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confidence: 99%

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

Sheng¹,

Cao²,

Fang³

et al. 2023

Preprint

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Location- and keyword-based querying of geo-textual data: a survey

2021

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With the broad adoption of mobile devices, notably smartphones, keyword-based search for content has seen increasing use by mobile users, who are often interested in content related to their geographical location. We have also witnessed a proliferation of geo-textual content that encompasses both textual and geographical information. Examples include geo-tagged microblog posts, yellow pages, and web pages related to entities with physical locations. Over the past decade, substantial research has been conducted on integrating location into keyword-based querying of geo-textual content in settings where the underlying data is assumed to be either relatively static or is assumed to stream into a system that maintains a set of continuous queries. This paper offers a survey of both the research problems studied and the solutions proposed in these two settings. As such, it aims to offer the reader a first understanding of key concepts and techniques, and

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Topo‐MSJ: Search for groups of POIs with qualitative processing of spatial regions

Rafael,

Pontes,

de Oliveira

et al. 2024

Transactions in GIS

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To alleviate challenges related to mobility, travel planning, and time management, individuals commonly encounter the necessity of locating points of interest (POIs) that either share the same physical building or are situated within interconnected buildings. However, existing search engines face difficulties in retrieving groups of places based on keywords and topological relations among their respective regions, for instance, when a user wants to find a residential building that is connected to a green area. This is primarily due to their limited consideration of POIs as mere points in space, rather than recognizing the polygonal geometries of their boundaries. In this work, we present a spatial search solution based on textual and topological query parameters for efficiently retrieving groups of POIs whose neighborhoods and boundaries satisfy specific restrictions. To perform this type of search efficiently, we propose the Topo‐MSJ algorithm, building upon the well‐established “Multi‐Star‐Join” (MSJ) algorithm, by including an efficient approach to handle queries with topological requirements. To assess the effectiveness of our solution, we conduct a performance evaluation by comparing the execution time of Topo‐MSJ with equivalent spatial SQL queries. The experimental analysis, performed on real datasets, reveals that Topo‐MSJ exhibits a faster execution time compared with equivalent SQL queries, additionally providing a simplified spatial pattern query notation.

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Scalable Processing of Spatial-Keyword Queries

Cited by 3 publications

References 158 publications

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

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

Location- and keyword-based querying of geo-textual data: a survey

Topo‐MSJ: Search for groups of POIs with qualitative processing of spatial regions

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