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Distributed MapReduce processing of location-based aggregate queries in road networks

DISTRIBUTED AND PARALLEL DATABASES 2025年第1期43卷 1-29页

作者： Huang, Yuan-Ko Natl Kaohsiung Univ Sci & Technol Dept Comp Sci & Informat Engn Kaohsiung Taiwan

Recently, the research community focuses on processing various types of location-based queries (or LBQs for short) (e.g., the range and nearest neighbor queries) on spatial objects of the same type in road networks, in which the road distance from objects to the query object is an important metric for determining the query result and needs to be calculated based on the connectivity of the network. The LBQs can be used to find the spatial objects with better spatial closeness (i.e., the shorter road distance to the query object). In many applications, information about the neighboring relationship between different types of objects may be more attractive to the user. As such, the location-based aggregate queries (or LBAggQs for short) are proposed, which consider not only the spatial closeness of objects but also the neighboring relationship between objects. However, the processing techniques for the LBAggQs are mainly based on a centralized paradigm, and hence suffer from highly dynamic environments where a large number of LBAggQs is issued concurrently. In this paper, a distributed processing technique, based on the MapReduce platform, is developed to answer multiple LBAggQs. Three tables To,odist\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T<^>{dist}_{o,o}$$\end{document}, To,pdist\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T<^>{dist}_{o,p}$$\end{document}, and Tp,pdist\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T<^>{

关键词： location-based queries Road networks location-based aggregate queries MapReduce platform MRSAggDQ algorithm

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location-based aggregate queries for Heterogeneous Neighboring Objects

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IEEE ACCESS 2017年 5卷 4874-4886页

作者： Huang, Yuan-Ko Natl Kaohsiung Marine Univ Dept Maritime Informat & Technol Kaohsiung 80543 Taiwan

Currently, most of the processing techniques for the conventional location-based queries focus only on a single type of objects. However, in real-life applications, the user may be interested in obtaining information about different types of objects, in terms of their neighboring relationship. We term the different types of stationary objects closer to each other the heterogeneous neighboring objects (HNOs). Efficient processing of the location-based queries on the HNOs is more complicated than that on a single data source, because the neighboring relationship between the HNOs inevitably affects the query result. In this paper, we present useful and important location-based aggregate queries on the HNOs, which can provide useful object information by considering both the spatial closeness of objects to the query object and the neighboring relationship between objects. The location-based aggregate queries consist of four queries: the shortest average-distance (SAvgD) query, the shortest minimal-distance (SMinD) query, the shortest maximal-distance (SMaxD) query, and the shortest sum-distance (SSumD) query. To process the location based aggregate queries, we devise two heuristics, the HNOs-qualifying heuristic and the HNOs pruning heuristic, to efficiently determine the HNOs sets. According to different query types, we further propose four heuristics, the SAvgD-pruning heuristic, the SMinD-pruning heuristic, the SMaxD-pruning heuristic, and the SSumD-pruning heuristic, to effectively reduce the number of distance computations required for query processing. Comprehensive experiments are conducted to demonstrate the effectiveness of the heuristics and the efficiency of the proposed approaches.

关键词： location-based queries heterogeneous neighboring objects location-based aggregate queries spatial closeness neighboring relationship

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Processing location-based aggregate queries in Road Networks

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2020年第4期36卷 921-935页

作者： Huang, Yuan-Ko Natl Kaohsiung Univ Sci & Technol Dept Maritime Informat & Technol Kaohsiung 811 Taiwan

In recent years, the research community has introduced various methods for processing the location-based queries on a single type of objects in road networks. However, in real-life applications user may be interested in obtaining information about different types of objects, in terms of their neighboring relationship. The sets of different types of objects closer to each other are termed the heterogeneous neighboring object sets (or HNO sets for short). To provide users with object information by considering both the spatial closeness of objects to the query object and the neighboring relationship between objects, we present useful and important location-based aggregate queries on finding the HNO sets in road net-works. The location-based aggregate queries are the shortest average distance query (SAvgDQ), the shortest minimal distance query (SMinDQ), the shortest maximal distance query (SMaxDQ), and the shortest sum distance query (SSumDQ). We first utilize a grid index to manage information of data objects and road networks, and then propose the SAvgDQ, SMinDQ, SMaxDQ, and SSumDQ processing algorithms, which are combined with the grid index to efficiently process the four types of location-based aggregate queries, respectively. A comprehensive set of experiments is conducted to demonstrate the efficiency of the proposed processing algorithms using real road network datasets.

关键词： location-based queries road networks heterogeneous neighboring object sets location-based aggregate queries grid index

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