Neighborhood Query Processing and Surrounding Objects Retrieval in Spatial Databases: Applications and Algorithms

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Author(s)
Islam, MS
Griffith University Author(s)
Year published
2021
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A nearest neighbourhood query (NHQ) retrieves the closest group of collocated objects from a spatial database for a given query location. On the other hand, a reverse nearest neighborhood query (RNHQ) returns all groups of collocated objects that find the given query nearer than any other competitors. Both NHQ and RNHQ queries might have many practical applications on mobile social networks, demand facility placement and smart urban planning. This paper also introduces another query, called direction-based spatial skyline query (DSQ), for retrieving surrounding objects from a spatial database for a given user location. The ...
View more >A nearest neighbourhood query (NHQ) retrieves the closest group of collocated objects from a spatial database for a given query location. On the other hand, a reverse nearest neighborhood query (RNHQ) returns all groups of collocated objects that find the given query nearer than any other competitors. Both NHQ and RNHQ queries might have many practical applications on mobile social networks, demand facility placement and smart urban planning. This paper also introduces another query, called direction-based spatial skyline query (DSQ), for retrieving surrounding objects from a spatial database for a given user location. The retrieved objects are not dominated by other data objects in the same direction w.r.t. the query. Like NHQ and RNHQ queries, retrieval of surrounding objects also has many applications such as nearby point-of-interests retrieval surrounding a user and digital gaming. This paper presents the challenges, algorithms, data indexing and data pruning techniques for processing NHQ, RNHQ and DSQ queries in spatial databases. Finally, encouraging experimental results and future research directions in NHQ, RNHQ, DSQ queries and their variants are discussed.
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View more >A nearest neighbourhood query (NHQ) retrieves the closest group of collocated objects from a spatial database for a given query location. On the other hand, a reverse nearest neighborhood query (RNHQ) returns all groups of collocated objects that find the given query nearer than any other competitors. Both NHQ and RNHQ queries might have many practical applications on mobile social networks, demand facility placement and smart urban planning. This paper also introduces another query, called direction-based spatial skyline query (DSQ), for retrieving surrounding objects from a spatial database for a given user location. The retrieved objects are not dominated by other data objects in the same direction w.r.t. the query. Like NHQ and RNHQ queries, retrieval of surrounding objects also has many applications such as nearby point-of-interests retrieval surrounding a user and digital gaming. This paper presents the challenges, algorithms, data indexing and data pruning techniques for processing NHQ, RNHQ and DSQ queries in spatial databases. Finally, encouraging experimental results and future research directions in NHQ, RNHQ, DSQ queries and their variants are discussed.
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Conference Title
Communications in Computer and Information Science
Volume
1373
Copyright Statement
© 2021 Springer-Verlag Berlin Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the conference's website for access to the definitive, published version.
Subject
Data management and data science not elsewhere classified
Spatial data and applications
Query processing and optimisation