TY - JOUR
T1 - GPU rasterization for realtime spatial aggregation over arbitrary polygons
AU - Zacharatou, Eleni Tzirita
AU - Doraiswamy, Harish
AU - Ailamaki, Anastasia
AU - Silva, Cláudio T.
AU - Freire, Juliana
N1 - Funding Information:
This work was supported in part by: the Moore-Sloan Data Science Environment at NYU; NASA; DOE; NSF awards CNS-1229185, CCF-1533564, CNS-1544753, CNS-1730396, and OAC 1640864; EU Horizon 2020, GA No 720270 (HBP SGA1); and the EU FP7 (ERC-2013-CoG), GA No 617508 (ViDa). J. Freire and C. T. Silva are partially supported by the DARPA MEMEX and D3M programs. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of DARPA
Publisher Copyright:
© 2017 VLDB Endowment.
PY - 2017/11/1
Y1 - 2017/11/1
N2 - Visual exploration of spatial data relies heavily on spatial aggregation queries that slice and summarize the data over different regions. These queries comprise computationally-intensive point-inpolygon tests that associate data points to polygonal regions, challenging the responsiveness of visualization tools. This challenge is compounded by the sheer amounts of data, requiring a large number of such tests to be performed. Traditional pre-aggregation approaches are unsuitable in this setting since they fix the query constraints and support only rectangular regions. On the other hand, query constraints are defined interactively in visual analytics systems, and polygons can be of arbitrary shapes. In this paper, we convert a spatial aggregation query into a set of drawing operations on a canvas and leverage the rendering pipeline of the graphics hardware (GPU) to enable interactive response times. Our technique trades-off accuracy for response time by adjusting the canvas resolution, and can even provide accurate results when combined with a polygon index. We evaluate our technique on two large real-world data sets, exhibiting superior performance compared to index-based approaches.
AB - Visual exploration of spatial data relies heavily on spatial aggregation queries that slice and summarize the data over different regions. These queries comprise computationally-intensive point-inpolygon tests that associate data points to polygonal regions, challenging the responsiveness of visualization tools. This challenge is compounded by the sheer amounts of data, requiring a large number of such tests to be performed. Traditional pre-aggregation approaches are unsuitable in this setting since they fix the query constraints and support only rectangular regions. On the other hand, query constraints are defined interactively in visual analytics systems, and polygons can be of arbitrary shapes. In this paper, we convert a spatial aggregation query into a set of drawing operations on a canvas and leverage the rendering pipeline of the graphics hardware (GPU) to enable interactive response times. Our technique trades-off accuracy for response time by adjusting the canvas resolution, and can even provide accurate results when combined with a polygon index. We evaluate our technique on two large real-world data sets, exhibiting superior performance compared to index-based approaches.
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U2 - 10.14778/3157794.3157803
DO - 10.14778/3157794.3157803
M3 - Conference article
AN - SCOPUS:85048775853
VL - 11
SP - 352
EP - 365
JO - Proceedings of the VLDB Endowment
JF - Proceedings of the VLDB Endowment
SN - 2150-8097
IS - 3
T2 - 44th International Conference on Very Large Data Bases, VLDB 2018
Y2 - 27 August 2018 through 31 August 2018
ER -