Developing a Real-Time Traffic Reporting and Forecasting Back-End System

Toliopoulos, Theodoros; Nikolaidis, Nikodimos; Michailidou, Anna-Valentini; Seitaridis, Andreas; Gounaris, Anastasios; Bassiliades, Nick; Georgiadis, Apostolos; Liotopoulos, Fotis K.

doi:10.1007/978-3-030-50316-1_4

Cited by 4 publications

(1 citation statement)

References 10 publications

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“…scale and completed 10 billion rides in 2018 [46]. The unprecedented rate of generation of location data has led to a considerable amount of research efforts that have been focused on, systems that scale out [1,2,8,9,14,39,40,41,48,50,51], databases [12,26,27,31,33], improving spatial query processing [11,18,19,20,35,42,43,44,53,34], or leveraging modern hardware and compiling techniques [6,7,37,36,38,52], to handle the increasing demands of applications today.…”

Section: Introductionmentioning

confidence: 99%

The Case for Learned Spatial Indexes

Pandey,

van Renen,

Kipf

et al. 2020

Preprint

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Spatial data is ubiquitous. Massive amounts of data are generated every day from billions of GPS-enabled devices such as cell phones, cars, sensors, and various consumerbased applications such as Uber, Tinder, location-tagged posts in Facebook, Twitter, Instagram, etc. This exponential growth in spatial data has led the research community to focus on building systems and applications that can process spatial data efficiently. In the meantime, recent research has introduced learned index structures. In this work, we use techniques proposed from a state-of-the art learned multi-dimensional index structure (namely, Flood) and apply them to five classical multi-dimensional indexes to be able to answer spatial range queries. By tuning each partitioning technique for optimal performance, we show that (i) machine learned search within a partition is faster by 11.79% to 39.51% than binary search when using filtering on one dimension, (ii) the bottleneck for tree structures is index lookup, which could potentially be improved by linearizing the indexed partitions (iii) filtering on one dimension and refining using machine learned indexes is 1.23x to 1.83x times faster than closest competitor which filters on two dimensions, and (iv) learned indexes can have a significant impact on the performance of low selectivity queries while being less effective under higher selectivities.

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Section: Introductionmentioning

confidence: 99%

The Case for Learned Spatial Indexes

Pandey,

van Renen,

Kipf

et al. 2020

Preprint

View full text Add to dashboard Cite

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How Good Are Modern Spatial Libraries?

et al. 2020

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Many applications today like Uber, Yelp, Tinder, etc. rely on spatial data or locations from its users. These applications and services either build their own spatial data management systems or rely on existing solutions. JTS Topology Suite (JTS), its C++ port GEOS, Google S2, ESRI Geometry API, and Java Spatial Index (JSI) are some of the spatial processing libraries that these systems build upon. These applications and services depend on indexing capabilities available in these libraries for high-performance spatial query processing. In this work, we compare these libraries qualitatively and quantitatively based on four different spatial queries using two real world datasets. We also compare these libraries with an open-source implementation of the Vantage Point Tree—an index structure that has been well studied in image retrieval and nearest-neighbor search algorithms for high-dimensional data. We found that Vantage Point Trees are very competitive and even outperform the aforementioned libraries in two queries.

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