SP-Phoenix: A Massive Spatial Point Data Management System Based on Phoenix

Li, Longhai; Li, Wendong; Zhong, Zhaoyu; Huang, Chengqiang

doi:10.1109/hpcc/smartcity/dss.2018.00266

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Recent advancements in the SSCP-based secondary index mechanism and transaction tools such as Apache Phoenix provide insight into improving NoSQL spatial-query processing. SP-Phoenix extends Phoenix with geohash-based spatial indexes for massive spatial points management on HBase [50]. Open-source spatial tools for NoSQL databases such as Geomesa [19] and its extensions [18,51] improve the spatial-query efficiency based on their space-filling-curve indexes with an SSCP framework.…”

Section: Nosql Databases and Hbasementioning

confidence: 99%

Improving NoSQL Spatial-Query Processing with Server-Side In-Memory R*-Tree Indexes for Spatial Vector Data

Sun

Jin

2023

Sustainability

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Geospatial databases are basic tools to collect, index, and manage georeferenced data indicators in sustainability research for efficient, long-term analysis. NoSQL databases are increasingly applied to manage the ever-growing massive spatial vector data (SVD) with their changeable data schemas, agile scalability, and fast query response time. Spatial queries are basic operations in geospatial databases. According to Green information technology, an efficient spatial index can accelerate query processing and save power consumption for ubiquitous spatial applications. Current solutions tend to pursue it by indexing spatial objects with space-filling curves or geohash on NoSQL databases. As for the performance-wise R-tree family, they are mainly used in slow disk-based spatial access methods on NoSQL databases that incur high loading and searching costs. Therefore, performing spatial queries efficiently with the R-tree family on NoSQL databases remains a challenge. In this paper, an in-memory balanced and distributed R*-tree index named the BDRST index is proposed and implemented on HBase for efficient spatial-query processing of massive SVD. The BDRST index stores and distributes serialized R*-trees to HBase regions in association with SVD partitions in the same table. Moreover, an efficient optimized server-side parallel processing framework is presented for real-time R*-tree instantiation and query processing. Through extensive experiments on real-world land-use data sets, the performance of our method is tested, including index building, index quality, spatial queries, and applications. Our proposed method outperforms other state-of-the-art solutions, saving between 27.36% and 95.94% on average execution time for the above operations. Experimental results show the capability of the BDRST index to support spatial queries over large-scale SVD, and our method provides a solution for efficient sustainability research that involves massive georeferenced data.

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Section: Nosql Databases and Hbasementioning

confidence: 99%

Improving NoSQL Spatial-Query Processing with Server-Side In-Memory R*-Tree Indexes for Spatial Vector Data

Sun

Jin

2023

Sustainability

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“…HBase does not have special geospatial functions to support geospatial data storage and querying [62]. However, researchers developed some methods and applications for processing geospatial data in HBase [63][64][65][66][67][68][69][70], such as a geographical database with geohash-based spatial indexes [63,71], big spatial data processing with Apache Spark [72], a geospatial data model [64], and a new spatial query method based on primary keys' indexing [73]. Additionally, an open source suite of tools, GeoMesa, was designed to implement large-scale geospatial analytics and querying in the cloud or in conjunction with the HBase and Cassandra databases [74].…”

Section: Apache Hbasementioning

confidence: 99%

State-of-the-Art Geospatial Information Processing in NoSQL Databases

Guo

Onstein

2020

IJGI

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Geospatial information has been indispensable for many application fields, including traffic planning, urban planning, and energy management. Geospatial data are mainly stored in relational databases that have been developed over several decades, and most geographic information applications are desktop applications. With the arrival of big data, geospatial information applications are also being modified into, e.g., mobile platforms and Geospatial Web Services, which require changeable data schemas, faster query response times, and more flexible scalability than traditional spatial relational databases currently have. To respond to these new requirements, NoSQL (Not only SQL) databases are now being adopted for geospatial data storage, management, and queries. This paper reviews state-of-the-art geospatial data processing in the 10 most popular NoSQL databases. We summarize the supported geometry objects, main geometry functions, spatial indexes, query languages, and data formats of these 10 NoSQL databases. Moreover, the pros and cons of these NoSQL databases are analyzed in terms of geospatial data processing. A literature review and analysis showed that current document databases may be more suitable for massive geospatial data processing than are other NoSQL databases due to their comprehensive support for geometry objects and data formats and their performance, geospatial functions, index methods, and academic development. However, depending on the application scenarios, graph databases, key-value, and wide column databases have their own advantages.

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A Massive Data Retrieval Method for Power Information Collection Systems

Wang¹,

Hong²,

et al. 2022

Big Data – BigData 2022

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SP-Phoenix: A Massive Spatial Point Data Management System Based on Phoenix

Cited by 3 publications

References 5 publications

Improving NoSQL Spatial-Query Processing with Server-Side In-Memory R*-Tree Indexes for Spatial Vector Data

Improving NoSQL Spatial-Query Processing with Server-Side In-Memory R*-Tree Indexes for Spatial Vector Data

State-of-the-Art Geospatial Information Processing in NoSQL Databases

A Massive Data Retrieval Method for Power Information Collection Systems

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