Antares: A Scalable, Real-Time, Fault Tolerant Data Store for Spatial Analysis

Simmonds, R; Watson, Paul; Halliday, Jonathan

doi:10.1109/services.2015.24

Cited by 4 publications

(7 citation statements)

References 9 publications

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“…NoSQL databases are more suitable for SVD storage than SQL databases as they can directly store SVD in a common format such as GeoJSON, WKT (well-known text), or WKB (well-known binary). Although some NoSQL databases provide native spatial indexes for spatial-query processing, most of them are not well-designed for geospatial data [32] due to the limited spatial data types and query functions [10]. Therefore, vast efforts have been made for building spatial-query processing approaches for NoSQL databases.…”

Section: Spatial-query Processing On Nosql Databasesmentioning

confidence: 99%

“…In most cases, the R-tree family was used as a disk-based spatial index. In NoSQL databases, the R-tree was usually flattened as basic data elements in a NoSQL table, such as documents in MongoDB [27] and rows in HBase [29,30], or stored in an external storage to support spatial-query processing for NoSQL databases [31,32]. Such disk-based usage of the R-tree family suffers from notable latency in slow index loading and searching on disk.…”

Section: Introductionmentioning

confidence: 99%

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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: Spatial-query Processing On Nosql Databasesmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Collecting open geospatial datasets in a traditional relational database management system (RDBMS) requires a lot of work related to schema design and data import, where both attributes and geometries have to be mapped, translated, and converted [14,15]. Relational databases have also some advantages compared to NoSQL databases that provide standard ACID properties (atomicity, consistency, isolation, and durability) that maintain the integrity of the database system when performing concurrent operations on it [16,17].…”

Section: Related Workmentioning

confidence: 99%

Benchmarking geospatial database on Kubernetes cluster

Sharma

Bansal

Chugh

et al. 2021

EURASIP J. Adv. Signal Process.

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Kubernetes is an open-source container orchestration system for automating container application operations and has been considered to deploy various kinds of container workloads. Traditional geo-databases face frequent scalability issues while dealing with dense and complex spatial data. Despite plenty of research work in the comparison of relational and NoSQL databases in handling geospatial data, there is a shortage of existing knowledge about the performance of geo-database in a clustered environment like Kubernetes. This paper presents benchmarking of PostgreSQL/PostGIS geospatial databases operating on a clustered environment against non-clustered environments. The benchmarking process considers the average execution times of geospatial structured query language (SQL) queries on multiple hardware configurations to compare the environments based on handling computationally expensive queries involving SQL operations and PostGIS functions. The geospatial queries operate on data imported from OpenStreetMap into PostgreSQL/PostGIS. The clustered environment powered by Kubernetes demonstrated promising improvements in the average execution times of computationally expensive geospatial SQL queries on all considered hardware configurations compared to their average execution times in non-clustered environments.

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“…In addition to these, more recently some document‐oriented databases, such as MongoDB (Makris et al, 2019) and Solr (Simmonds, Watson, & Halliday, 2015), have also received updates to be able to deal with big geospatial data.…”

Section: Introductionmentioning

confidence: 99%

“…It is referred to as a public domain geocode system which encodes a geographic location as a short string, using a hierarchical spatial data structure which subdivides space into buckets of grid shape, providing a z ‐order traversal of rectangles covering the Earth at each resolution. Among the databases that use GeoHash in their spatial indexing approaches are MongoDB (Makris et al, 2019) and Solr (Simmonds et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A proposal to minimize the cost of processing big geospatial data in public cloud providers

Bachiega

Holanda

Araújo

2021

Transactions in GIS

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Spatial data represent abstractions of real‐world entities and can be obtained in various ways. They have properties that differentiate them from other types of data, such as a complex structure and dynamism. In recent years with the increasing volume of spatial data, referred to as “big geospatial data,” some tools have been developed to process such data efficiently, such as SpatialHadoop. The use of appropriate data indices based on the data set to be processed, as well as queries and operations to be performed, is essential for the optimal performance of these applications. In particular, since public cloud providers’ charges are based on the resources used, it is imperative to optimize application execution in order to avoid unnecessary expense. This article proposes the use of six conditions that seek to minimize the cost of processing big geospatial data in public cloud providers. The tests performed demonstrate that the use of these conditions and the choice of the lowest‐cost provider can reduce the total processing cost by up to 41%.

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Antares: A Scalable, Real-Time, Fault Tolerant Data Store for Spatial Analysis

Cited by 4 publications

References 9 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

Benchmarking geospatial database on Kubernetes cluster

A proposal to minimize the cost of processing big geospatial data in public cloud providers

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