Big Spatial and Spatio-Temporal Data Analytics Systems

Velentzas, Polychronis; Corral, Antonio; Vassilakopoulos, Michael

doi:10.1007/978-3-662-62919-2_7

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…Recently, several spatial analytics systems (SASs) were proposed to support different type of spatial queries (e.g., range queries, nearest neighbor queries, and spatial joins) over large-scale spatial datasets on shared-nothing clusters in distributed environments. These SASs are mainly based on Hadoop MapReduce or Spark, and several surveys were recently published to describe and classify them [15][16][17][18]. The most representative Spark-based SASs are SpatialSpark [19], GeoSpark (currently Sedona) [4], Simba [20], LocationSpark [21], STARK [22], SparkGIS [23], Elcano [24] and Beast [25].…”

Section: Spatial Query Processing In Apache Sparkmentioning

confidence: 99%

Efficient Group K Nearest-Neighbor Spatial Query Processing in Apache Spark

Moutafis,

Mavrommatis,

Vassilakopoulos

et al. 2021

IJGI

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Aiming at the problem of spatial query processing in distributed computing systems, the design and implementation of new distributed spatial query algorithms is a current challenge. Apache Spark is a memory-based framework suitable for real-time and batch processing. Spark-based systems allow users to work on distributed in-memory data, without worrying about the data distribution mechanism and fault-tolerance. Given two datasets of points (called Query and Training), the group K nearest-neighbor (GKNN) query retrieves (K) points of the Training with the smallest sum of distances to every point of the Query. This spatial query has been actively studied in centralized environments and several performance improving techniques and pruning heuristics have been also proposed, while, a distributed algorithm in Apache Hadoop was recently proposed by our team. Since, in general, Apache Hadoop exhibits lower performance than Spark, in this paper, we present the first distributed GKNN query algorithm in Apache Spark and compare it against the one in Apache Hadoop. This algorithm incorporates programming features and facilities that are specific to Apache Spark. Moreover, techniques that improve performance and are applicable in Apache Spark are also incorporated. The results of an extensive set of experiments with real-world spatial datasets are presented, demonstrating that our Apache Spark GKNN solution, with its improvements, is efficient and a clear winner in comparison to processing this query in Apache Hadoop.

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