Continuous nearest-neighbor queries with location uncertainty

Sistla, A. Prasad; Wolfson, Ouri; Xu, Bo

doi:10.1007/s00778-014-0361-2

Cited by 7 publications

(2 citation statements)

References 27 publications

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“…Other common problem settings include objects with uncertain locations [Sistla et al 2015], queries with privacy considerations [Hashem et al 2013], and queries in indoor settings [Yang et al 2010;Afyouni et al 2012Afyouni et al , 2014. CR queries with both a moving query object and moving data objects have not been considered in the context of safe region based query processing.…”

Section: Future Developmentsmentioning

confidence: 99%

Continuous Spatial Query Processing

Zhang

Jensen

et al. 2018

ACM Comput. Surv.

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In the past decade, positioning system-enabled devices such as smartphones have become most prevalent. This functionality brings the increasing popularity of location-based services in business as well as daily applications such as navigation, targeted advertising, and location-based social networking. Continuous spatial queries serve as a building block for location-based services. As an example, an Uber driver may want to be kept aware of the nearest customers or service stations. Continuous spatial queries require updates to the query result as the query or data objects are moving. This poses challenges to the query efficiency, which is crucial to the user experience of a service. A large number of approaches address this efficiency issue using the concept of safe region . A safe region is a region within which arbitrary movement of an object leaves the query result unchanged. Such a region helps reduce the frequency of query result update and hence improves query efficiency. As a result, safe region-based approaches have been popular for processing various types of continuous spatial queries. Safe regions have interesting theoretical properties and are worth in-depth analysis. We provide a comparative study of safe region-based approaches. We describe how safe regions are computed for different types of continuous spatial queries, showing how they improve query efficiency. We compare the different safe region-based approaches and discuss possible further improvements.

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Section: Future Developmentsmentioning

confidence: 99%

Continuous Spatial Query Processing

Zhang

Jensen

et al. 2018

ACM Comput. Surv.

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“…The above variables and parameters could be considered as attributes of an uncertain data, as their values may have some uncertainty due to device instability, transmission delay, power failure, or many other reasons. Furthermore, some databases for special needs, such as privacy protection, it is necessary to generalize some sensitive dimensions of accurate data 1-5 . In the above scenarios, uncertain data is more suitable for describing data objects than accurate data 6-8 .…”

Section: Introductionmentioning

confidence: 99%

LSR‐forest: An locality sensitive hashing‐based approximate k‐nearest neighbor query algorithm on high‐dimensional uncertain data

Wang

Qian

Yang

et al. 2020

Concurrency and Computation

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Summary Uncertain data is widely used in many practical applications, such as data cleaning, location‐based services, privacy protection, and so on. With the development of technology, data has a tendency to high‐dimensionality. The most common indexes for nearest neighbor search on uncertain data are the R‐Tree and the KD‐Tree. These indexes will inevitably bring about “curse of dimension.” Focus on this problem, article proposes a new hash algorithm, called the LSR‐forest, which based on locality sensitive hashing and R‐Tree, to solve the high‐dimensional uncertain data approximate neighbor search problem. The LSR‐forest can hash similar high‐dimensional uncertain data into a same bucket with a high probability, and then constructs multiple R‐Tree‐based indexes for hashed buckets. When querying, it is possible to judge neighbors by checking the data in the hypercube which the query point is in. One can also adjust the query range automatically by different parameter of k. Many experiments on different datasets are presented in this article. The results show that LSR‐forest has better effectiveness and efficiency than R‐Tree on high‐dimensional datasets.

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