ParSCL: A Parallel and Distributed Framework to Process All Nearest Neighbor Queries on a Road Network

The proliferation of current and next-generation mobile and sensing devices has increased at an alarming rate. With these state-of-the-art devices, the global positioning system ( GPS ) has made remote sensing and location tracking more viable. One such query is the All Nearest Neighbor ( ANN ) quer...

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Vydané v:IEEE access Ročník 11; s. 1
Hlavní autori: Bhandari, Aavash, Hamandawana, Prince, Attique, Muhammad, Cho, Hyung-Ju, Chung, Tae-Sun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
All nearest neighbor queries
Cluster computing
Distributed algorithms
distributed and parallel processing
Global positioning systems
GPS
Nearest neighbor methods
Object recognition
Optimization
Parallel processing
Partitioning algorithms
Queries
Query processing
Remote sensing
Roads
Sensors
Service stations
Spatial databases
spatial query processing
Transportation networks
ISSN:2169-3536, 2169-3536
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Shrnutí:The proliferation of current and next-generation mobile and sensing devices has increased at an alarming rate. With these state-of-the-art devices, the global positioning system ( GPS ) has made remote sensing and location tracking more viable. One such query is the All Nearest Neighbor ( ANN ) query, which extracts and returns all data objects that are in close vicinity to all query objects. An ANN is a combination of k-nearest neighbors ( kNN ), and join queries. Hence, ANN has useful for applications in different domains such as transportation optimization, locating safe zones, and ride-sharing. An example of its applications is, " find the nearest gas station for each car parking lot ". Because these applications are responsible for generating a massive number of query requests, a large amount of computation is required to return these query requests. As a single machine cannot meet this demand in this study, we propose a distributed query processing framework to process ANN queries using the Apache Spark framework. In an empirical study, our proposed framework achieved superior query efficiency and scalability compared to other methods and design alternatives.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3308684