Distributed Processing of Location-Based Aggregate Queries Using MapReduce
The <i>location-based aggregate queries</i>, consisting of the <i>shortest average distance query</i> (<i>SAvgDQ</i>), the <i>shortest minimal distance query</i> (<i>SMinDQ</i>), the <i>shortest maximal distance query</i> (<i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-08-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/8/9/370 |
| _version_ | 1818422194804359168 |
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| author | Yuan-Ko Huang |
| author_facet | Yuan-Ko Huang |
| author_sort | Yuan-Ko Huang |
| collection | DOAJ |
| description | The <i>location-based aggregate queries</i>, consisting of the <i>shortest average distance query</i> (<i>SAvgDQ</i>), the <i>shortest minimal distance query</i> (<i>SMinDQ</i>), the <i>shortest maximal distance query</i> (<i>SMaxDQ</i>), and the <i>shortest sum distance query</i> (<i>SSumDQ</i>) are new types of location-based queries. Such queries can be used to provide the user with useful object information by considering both the spatial closeness of objects to the query object and the neighboring relationship between objects. Due to a large amount of location-based aggregate queries that need to be evaluated concurrently, the centralized processing system would suffer a heavy query load, leading eventually to poor performance. As a result, in this paper, we focus on developing the distributed processing technique to answer multiple location-based aggregate queries, based on the <i>MapReduce</i> platform. We first design a grid structure to manage information of objects by taking into account the storage balance, and then develop a distributed processing algorithm, namely the <i>MapReduce-based aggregate query algorithm</i> (<i>MRAggQ algorithm</i>), to efficiently process the location-based aggregate queries in a distributed manner. Extensive experiments using synthetic and real datasets are conducted to demonstrate the scalability and the efficiency of the proposed processing algorithm. |
| first_indexed | 2024-12-14T13:22:23Z |
| format | Article |
| id | doaj.art-307973235865470a821be870b8927866 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-12-14T13:22:23Z |
| publishDate | 2019-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-307973235865470a821be870b89278662022-12-21T22:59:54ZengMDPI AGISPRS International Journal of Geo-Information2220-99642019-08-018937010.3390/ijgi8090370ijgi8090370Distributed Processing of Location-Based Aggregate Queries Using MapReduceYuan-Ko Huang0Department of Maritime Information and Technology, National Kaohsiung University of Science and Technology, 80543 Kaohsiung City, TaiwanThe <i>location-based aggregate queries</i>, consisting of the <i>shortest average distance query</i> (<i>SAvgDQ</i>), the <i>shortest minimal distance query</i> (<i>SMinDQ</i>), the <i>shortest maximal distance query</i> (<i>SMaxDQ</i>), and the <i>shortest sum distance query</i> (<i>SSumDQ</i>) are new types of location-based queries. Such queries can be used to provide the user with useful object information by considering both the spatial closeness of objects to the query object and the neighboring relationship between objects. Due to a large amount of location-based aggregate queries that need to be evaluated concurrently, the centralized processing system would suffer a heavy query load, leading eventually to poor performance. As a result, in this paper, we focus on developing the distributed processing technique to answer multiple location-based aggregate queries, based on the <i>MapReduce</i> platform. We first design a grid structure to manage information of objects by taking into account the storage balance, and then develop a distributed processing algorithm, namely the <i>MapReduce-based aggregate query algorithm</i> (<i>MRAggQ algorithm</i>), to efficiently process the location-based aggregate queries in a distributed manner. Extensive experiments using synthetic and real datasets are conducted to demonstrate the scalability and the efficiency of the proposed processing algorithm.https://www.mdpi.com/2220-9964/8/9/370location-based aggregate queriesdistributed processing techniqueMapReducegrid structureMapReduce-based aggregate query algorithm |
| spellingShingle | Yuan-Ko Huang Distributed Processing of Location-Based Aggregate Queries Using MapReduce ISPRS International Journal of Geo-Information location-based aggregate queries distributed processing technique MapReduce grid structure MapReduce-based aggregate query algorithm |
| title | Distributed Processing of Location-Based Aggregate Queries Using MapReduce |
| title_full | Distributed Processing of Location-Based Aggregate Queries Using MapReduce |
| title_fullStr | Distributed Processing of Location-Based Aggregate Queries Using MapReduce |
| title_full_unstemmed | Distributed Processing of Location-Based Aggregate Queries Using MapReduce |
| title_short | Distributed Processing of Location-Based Aggregate Queries Using MapReduce |
| title_sort | distributed processing of location based aggregate queries using mapreduce |
| topic | location-based aggregate queries distributed processing technique MapReduce grid structure MapReduce-based aggregate query algorithm |
| url | https://www.mdpi.com/2220-9964/8/9/370 |
| work_keys_str_mv | AT yuankohuang distributedprocessingoflocationbasedaggregatequeriesusingmapreduce |