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

Chen, Lisi; Cong, Gao; Jensen, Christian S.

doi:10.1007/s00778-021-00661-w

Cited by 26 publications

(8 citation statements)

References 156 publications

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“…For example, POIs associated with other data sources (e.g., text) contain richer knowledge about locations and are utilized in urban event detection [42,47], human mobility modeling [3,40,41], etc. On the other hand, to better support the above applications, methods are proposed to improve the quality of data management of POIs, especially when combined with other data sources, including streaming scenarios [2], visualization [10], scalability [36] and efficient query retrieval [4,5]. These studies are orthogonal to our problem of mining relationships among POIs, and POI relationships can be used to improve on these applications, such as POI recommendation.…”

Section: Related Workmentioning

confidence: 99%

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Points-of-Interest Relationship Inference with Spatial-enriched Graph Neural Networks

Chen

Cong

et al. 2022

Preprint

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As a fundamental component in location-based services, inferring the relationship between points-of-interests (POIs) is very critical for service providers to offer good user experience to business owners and customers. Most of the existing methods for relationship inference are not targeted at POI, thus failing to capture unique spatial characteristics that have huge effects on POI relationships. In this work we propose PRIM to tackle POI relationship inference for multiple relation types. PRIM features four novel components, including a weighted relational graph neural network, category taxonomy integration, a self-attentive spatial context extractor, and a distance-specific scoring function. Extensive experiments on two real-world datasets show that PRIM achieves the best results compared to state-of-the-art baselines and it is robust against data sparsity and is applicable to unseen cases in practice.

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Section: Related Workmentioning

confidence: 99%

“…4 Analysis on datasets with different characteristics. We conduct experiments to evaluate the model performance on datasets with different characteristics.…”

mentioning

confidence: 99%

Points-of-Interest Relationship Inference with Spatial-enriched Graph Neural Networks

Chen

Cong

et al. 2022

Preprint

Self Cite

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“…emerged on the Web, and gradually formed large-scale spatio-textual data. Spatial keyword query is becoming a research hotspot in the fields of Location-Based Services (LBS), spatio-temporal data mining, POI recommendation and spatial database query [1][2][3][4]. For example, the LBS platforms such as Meituan, Ctrip, Twitter, and Google Maps all need the support of spatial keyword query technology to obtain spatio-textual objects that are close to the query location, semantically related to the query keywords and interesting to users.…”

Section: Introductionmentioning

confidence: 99%

Top-k approximate selection for typicality query results over spatio-textual data

Meng,

Zhang,

Huo

et al. 2023

Knowl Inf Syst

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Spatial keyword query is a classical query processing mode for spatio-textual data, which aims to provide users the spatio-textual objects with the highest spatial proximity and textual similarity to the given query. However, the top-k result objects obtained by using the spatial keyword query mode are often similar to each other while users hope that the system can pick top-k typicality results from the candidate query results in order to make users understand the representative features of the candidate result set. To deal with the problem of typicality analysis and typical object selection of spatio-textual data query results, a typicality evaluation and top-k approximate selection approach is proposed. First, the approach calculates the synthetic distances on dimensions of geographic location, textual semantics, and numeric attributes between all spatio-textual objects. And then, a hybrid index structure that can simultaneously support the location, text, and numeric multi-dimension matching is presented in order to expeditiously obtain the candidate query results. According to the synthetic distances between spatio-textual objects, a Gaussian kernel probability density estimation-based method for measuring the typicality of query results is proposed. To facilitate the query result analysis and top-k typical object selection, the Tournament strategybased and Local neighborhood-based top-k typical object approximate selection algorithms are presented, respectively. The experimental results demonstrated that the text semantic relevancy measuring method for spatio-textual objects is accurate and reasonable, and the Local neighborhood-based top-k typicality result approximate selection algorithm achieved both the low error rate and high execution efficiency.

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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.…”

mentioning

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

Cited by 26 publications

References 156 publications

Points-of-Interest Relationship Inference with Spatial-enriched Graph Neural Networks

Points-of-Interest Relationship Inference with Spatial-enriched Graph Neural Networks

Top-k approximate selection for typicality query results over spatio-textual data

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

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