Paper Info
Title
Detecting Change in Snapshot Sequences
Abstract
Wireless sensor networks are deployed to monitor dynamic geographic phenomena, or objects, over space and time. This paper presents a new spatiotemporal data model for dynamic areal objects in sensor networks. Our model supports for the first time the analysis of change in sequences of snapshots that are captured by different granularity of observations, and our model allows both incremental and non-incremental changes. This paper focuses on detecting qualitative spatial changes, such as merge and split of areal objects. A decentralized algorithm is developed, such that spatial changes can be efficiently detected by in-network aggregation of decentralized datasets.
Year
DOI
Venue
2010
10.1007/978-3-642-15300-6_16
Geographic Information Science
Keywords
Field
DocType
areal object,decentralized datasets,spatial change,wireless sensor network,new spatiotemporal data model,dynamic geographic phenomenon,sensor network,qualitative spatial change,spatiotemporal data models,de- centralized algorithms,qualitative spatial changes.,detecting change,dynamic areal object,wireless sensor networks,decentralized algorithm,snapshot sequence,data model
Data mining,Computer science,Real-time computing,Mobile wireless sensor network,Granularity,Merge (version control),Wireless sensor network,Data model,Snapshot (computer storage)
Conference
Volume
ISSN
ISBN
6292
0302-9743
3-642-15299-6
Citations 
PageRank 
References 
5
0.44
15
Authors
2
Name
Order
Citations
PageRank
Mingzheng Shi1241.54
Stephan Winter264345.20