TY - GEN
T1 - Turbocharging geospatial visualization dashboards via a materialized sampling cube approach
AU - Yu, Jia
AU - Sarwat, Mohamed
N1 - Funding Information:
VIII. ACKNOWLEDGEMENT This work is supported by the National Science Foundation (NSF) under Grant 1845789.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - 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.
AB - 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.
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U2 - 10.1109/ICDE48307.2020.00105
DO - 10.1109/ICDE48307.2020.00105
M3 - Conference contribution
AN - SCOPUS:85085860266
T3 - Proceedings - International Conference on Data Engineering
SP - 1165
EP - 1176
BT - Proceedings - 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020
PB - IEEE Computer Society
T2 - 36th IEEE International Conference on Data Engineering, ICDE 2020
Y2 - 20 April 2020 through 24 April 2020
ER -