TY - GEN
T1 - The tornado model
T2 - 12th International Conference on Database Systems for Advanced Applications, DASFAA 2007
AU - Yu, Byunggu
AU - Kim, Seon Ho
AU - Alkobaisi, Shayma
AU - Bae, Wan D.
AU - Bailey, Thomas
PY - 2007
Y1 - 2007
N2 - To support emerging database applications that deal with continuously changing (or moving) data objects (CCDOs), such as vehicles, RFIDs, and multi-stimuli sensors, one requires an efficient data management system that can store, update, and retrieve large sets of CCDOs. Although actual CCDOs can continuously change over time, computer systems cannot deal with continuously occurring infinitesimal changes. Thus, in the data management system, each object's spatiotemporal values are associated with a certain degree of uncertainty at virtually every point in time, and the queries are mostly processed over estimates characterizing the uncertainty. The smaller the uncertainty is, the better the query performance becomes. The paper proposes a sophisticated asymmetric uncertainty model, called the Tornado Model, which can effectively represent, process, and minimize the data uncertainty for a wide variety of CCDO database applications.
AB - To support emerging database applications that deal with continuously changing (or moving) data objects (CCDOs), such as vehicles, RFIDs, and multi-stimuli sensors, one requires an efficient data management system that can store, update, and retrieve large sets of CCDOs. Although actual CCDOs can continuously change over time, computer systems cannot deal with continuously occurring infinitesimal changes. Thus, in the data management system, each object's spatiotemporal values are associated with a certain degree of uncertainty at virtually every point in time, and the queries are mostly processed over estimates characterizing the uncertainty. The smaller the uncertainty is, the better the query performance becomes. The paper proposes a sophisticated asymmetric uncertainty model, called the Tornado Model, which can effectively represent, process, and minimize the data uncertainty for a wide variety of CCDO database applications.
KW - Spatiotemporal database
KW - Trajectory
KW - Uncertainty
UR - http://www.scopus.com/inward/record.url?scp=38049123941&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=38049123941&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-71703-4_53
DO - 10.1007/978-3-540-71703-4_53
M3 - Conference contribution
AN - SCOPUS:38049123941
SN - 9783540717027
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 624
EP - 636
BT - Advances in Databases
PB - Springer Verlag
Y2 - 9 April 2007 through 12 April 2007
ER -