TY - GEN
T1 - Interactive visual exploration of spatio-temporal urban data sets using urbane
AU - Doraiswamy, Harish
AU - Zacharatou, Eleni Tzirita
AU - Miranda, Fabio
AU - Lage, Marcos
AU - Ailamaki, Anastasia
AU - Silva, Cláudio T.
AU - Freire, Juliana
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/5/27
Y1 - 2018/5/27
N2 - The recent explosion in the number and size of spatio-temporal data sets from urban environments and social sensors creates new opportunities for data-driven approaches to understand and improve cities. Visual analytics systems like Urbane aim to empower domain experts to explore multiple data sets, at different time and space resolutions. Since these systems rely on computationallyintensive spatial aggregation queries that slice and summarize the data over different regions, an important challenge is how to attain interactivity. While traditional pre-aggregation approaches support interactive exploration, they are unsuitable in this setting because they do not support ad-hoc query constraints or polygons of arbitrary shapes. To address this limitation, we have recently proposed Raster Join, an approach that converts a spatial aggregation query into a set of drawing operations on a canvas and leverages the rendering pipeline of the graphics hardware (GPU). By doing so, Raster Join evaluates queries on the fly at interactive speeds on commodity laptops and desktops. In this demonstration, we showcase the efficiency of Raster Join by integrating it with Urbane and enabling interactivity. Demo visitors will interact with Urbane to filter and visualize several urban data sets over multiple resolutions.
AB - The recent explosion in the number and size of spatio-temporal data sets from urban environments and social sensors creates new opportunities for data-driven approaches to understand and improve cities. Visual analytics systems like Urbane aim to empower domain experts to explore multiple data sets, at different time and space resolutions. Since these systems rely on computationallyintensive spatial aggregation queries that slice and summarize the data over different regions, an important challenge is how to attain interactivity. While traditional pre-aggregation approaches support interactive exploration, they are unsuitable in this setting because they do not support ad-hoc query constraints or polygons of arbitrary shapes. To address this limitation, we have recently proposed Raster Join, an approach that converts a spatial aggregation query into a set of drawing operations on a canvas and leverages the rendering pipeline of the graphics hardware (GPU). By doing so, Raster Join evaluates queries on the fly at interactive speeds on commodity laptops and desktops. In this demonstration, we showcase the efficiency of Raster Join by integrating it with Urbane and enabling interactivity. Demo visitors will interact with Urbane to filter and visualize several urban data sets over multiple resolutions.
UR - http://www.scopus.com/inward/record.url?scp=85048803994&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048803994&partnerID=8YFLogxK
U2 - 10.1145/3183713.3193559
DO - 10.1145/3183713.3193559
M3 - Conference contribution
AN - SCOPUS:85048803994
T3 - Proceedings of the ACM SIGMOD International Conference on Management of Data
SP - 1693
EP - 1696
BT - SIGMOD 2018 - Proceedings of the 2018 International Conference on Management of Data
A2 - Das, Gautam
A2 - Jermaine, Christopher
A2 - Eldawy, Ahmed
A2 - Bernstein, Philip
PB - Association for Computing Machinery
T2 - 44th ACM SIGMOD International Conference on Management of Data, SIGMOD 2018
Y2 - 10 June 2018 through 15 June 2018
ER -