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Leonardi L., Orlando S., Raffaetą A., Roncato A., Silvestri C. Frequent spatio-temporal patterns in trajectory data warehouses. In: SAC 2009 - ACM Symposium on Applied Computing (Honolulu, Hawaii, USA, 9 -12 Marzo 2009). Proceedings, pp. 1433 - 1440. ACM, 2009.
 
 
Abstract
(English)
In this paper we present an approach for storing and aggregating spatio-temporal patterns by using a Trajectory Data Warehouse (TDW). In particular, our aim is to allow the analysts to quickly evaluate frequent patterns mined from trajectories of moving objects occurring in a specific spatial zone and during a given temporal interval. We resort to a TDW, based on a data cube model, having spatial and temporal dimensions, discretized according to a hierarchy of regular grids, and whose facts are sets of trajectories which intersect the spatio-temporal cells of the cube. The idea is to enrich such a TDW with a new measure: frequent patterns obtained from a data-mining process on trajectories. As a consequence these patterns can be analysed by the user at various levels of granularity by means of OLAP queries. The research issues discussed in this paper are (1) the extraction/ mining of the patterns to be stored in each cell, which requires an adequate projection phase of trajectories before mining; (2) the spatio-temporal aggregation of patterns to answer roll-up queries, which poses many problems due to the holistic nature of the aggregation function.
URL: http://portal.acm.org/toc.cfm?id=1529282&type=proceeding&coll=GUIDE&dl=GUIDE&CFID=38295711&CFTOKEN=92824839
DOI: http://doi.acm.org/10.1145/1529282.1529603
Subject Data Warehouse
Mobile Objects
Trajectories
Frequent patterns
H.2.8 Database Applications


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