TY - GEN
T1 - Spatial big-data challenges intersecting mobility and cloud computing
AU - Shekhar, Shashi
AU - Evans, Michael R.
AU - Gunturi, Viswanath
AU - Yang, Kwang Soo
PY - 2012
Y1 - 2012
N2 - Increasingly, location-aware datasets are of a size, variety, and update rate that exceeds the capability of spatial computing technologies. This paper addresses the emerging challenges posed by such datasets, which we call Spatial Big Data (SBD). SBD examples include trajectories of cell- phones and GPS devices, vehicle engine measurements, temporally detailed road maps, etc. SBD has the potential to transform society via next-generation routing services such as eco-routing. However, the envisaged SBD-based next-generation routing services pose several significant challenges for current routing techniques. SBD magnifies the impact of partial information and ambiguity of traditional routing queries specified by a start location and an end location. In addition, SBD challenges the assumption that a single algorithm utilizing a specific dataset is appropriate for all situations. The tremendous diversity of SBD sources substantially increases the diversity of solution methods. Newer algorithms may emerge as new SBD becomes available, creating the need for a exible architecture to rapidly integrate new datasets and associated algorithms.
AB - Increasingly, location-aware datasets are of a size, variety, and update rate that exceeds the capability of spatial computing technologies. This paper addresses the emerging challenges posed by such datasets, which we call Spatial Big Data (SBD). SBD examples include trajectories of cell- phones and GPS devices, vehicle engine measurements, temporally detailed road maps, etc. SBD has the potential to transform society via next-generation routing services such as eco-routing. However, the envisaged SBD-based next-generation routing services pose several significant challenges for current routing techniques. SBD magnifies the impact of partial information and ambiguity of traditional routing queries specified by a start location and an end location. In addition, SBD challenges the assumption that a single algorithm utilizing a specific dataset is appropriate for all situations. The tremendous diversity of SBD sources substantially increases the diversity of solution methods. Newer algorithms may emerge as new SBD becomes available, creating the need for a exible architecture to rapidly integrate new datasets and associated algorithms.
KW - Data mining
KW - Mobility services
KW - Spatial big data
UR - http://www.scopus.com/inward/record.url?scp=84863491669&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84863491669&partnerID=8YFLogxK
U2 - 10.1145/2258056.2258058
DO - 10.1145/2258056.2258058
M3 - Conference contribution
AN - SCOPUS:84863491669
SN - 9781450314428
T3 - MobiDE 2012 - Proceedings of the 11th ACM International Workshop on Data Engineering for Wireless and Mobile Access - In Conjunction with ACM SIGMOD / PODS 2012
SP - 1
EP - 6
BT - MobiDE 2012 - Proceedings of the 11th ACM International Workshop on Data Engineering for Wireless and Mobile Access - In Conjunction with ACM SIGMOD / PODS 2012
T2 - 11th ACM International Workshop on Data Engineering for Wireless and Mobile Access, MobiDE 2012 - In Conjunction with ACM SIGMOD / PODS 2012
Y2 - 20 May 2012 through 20 May 2012
ER -