Mining Long, Sharable Patterns in Trajectories of Moving Objects
2006 (English)In: Proceedings of the Third Workshop on Spatio-Temporal Database Management, STDBM 06, September 11, 2006, Seoul, South Korea / [ed] Christophe Claramunt and Ki-Joune Li and Simonas Šaltenis, CEUR , 2006, 49-58 p.Conference paper (Refereed)
The efficient analysis of spatio–temporal data, generated by moving objects, is an es-sential requirement for intelligent locationbased services. Spatio-temporal rules can befound by constructing spatio–temporal baskets, from which traditional association rulemining methods can discover spatio–temporal rules. When the items in the baskets arespatio–temporal identifiers and are derived from trajectories of moving objects, the discovered rules represent frequently travelled routes. For some applications, e.g., an intelligent ridesharing application, these frequent routes are only interesting if they are long and sharable, i.e., can potentially be shared by several users. This paper presents a database projection based method for efficiently extracting such long, sharable requent routes.The method prunes the search space by making use of the minimum length and sharable requirements and avoids the generation of the exponential number of subroutes of long routes. A SQL–based implementation is described, and experiments on real life data show the effectiveness of the method.
Place, publisher, year, edition, pages
CEUR , 2006. 49-58 p.
, CEUR Workshop Proceedings, ISSN 1613-0073 ; 174
spatio-temporal data mining, sharable frequent routes, SQL-based itemset mining
IdentifiersURN: urn:nbn:se:kth:diva-86103OAI: oai:DiVA.org:kth-86103DiVA: diva2:500437
The Third Workshop on Spatio-Temporal Database Management, STDBM 06, September 11, 2006, Seoul, South Korea
QC 201202282012-02-132012-02-132012-02-28Bibliographically approved