Turbocharging geospatial visualization dashboards via a materialized sampling cube approach

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper, we present a middleware framework that runs on top of a SQL data system with the purpose of increasing the interactivity of geospatial visualization dashboards. The proposed system adopts a sampling cube approach that stores pre-materialized spatial samples and allows users to define their own accuracy loss function such that the produced samples can be used for various user-defined visualization tasks. The system ensures that the difference between the sample fed into the visualization dashboard and the raw query answer never exceeds the user-specified loss threshold. To reduce the number of cells in the sampling cube and hence mitigate the initialization time and memory utilization, the system employs two main strategies: (1) a partially materialized cube to only materialize local samples of those queries for which the global sample (the sample drawn from the entire dataset) exceeds the required accuracy loss threshold. (2) a sample selection technique that finds similarities between different local samples and only persists a few representative samples. Based on the extensive experimental evaluation, Tabula can bring down the total data-to-visualization time (including both data-system and visualization times) of a heat map generated over 700 million taxi rides to 600 milliseconds with 250 meters user-defined accuracy loss. Besides, Tabula costs up to two orders of magnitude less memory footprint (e.g., only 800 MB for the running example) and one order of magnitude less initialization time than the fully materialized sampling cube.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020
PublisherIEEE Computer Society
Pages1165-1176
Number of pages12
ISBN (Electronic)9781728129037
DOIs
StatePublished - Apr 2020
Event36th IEEE International Conference on Data Engineering, ICDE 2020 - Dallas, United States
Duration: Apr 20 2020Apr 24 2020

Publication series

NameProceedings - International Conference on Data Engineering
Volume2020-April
ISSN (Print)1084-4627

Conference

Conference36th IEEE International Conference on Data Engineering, ICDE 2020
Country/TerritoryUnited States
CityDallas
Period4/20/204/24/20

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

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