Please use this identifier to cite or link to this item:
Title: Pattern mining in spatiotemporal database
Keywords: Spatiotemporal data, frequent pattern mining, mutation pattern, interaction pattern, trajectory pattern
Issue Date: 26-Feb-2010
Citation: SHENG CHANG (2010-02-26). Pattern mining in spatiotemporal database. ScholarBank@NUS Repository.
Abstract: Advances in sensing and satellite technologies and the rapid spread of moving devices generate a large volume of spatiotemporal data of different types and promote the development of spatiotemporal database, thereby arising an increasing need for discovering spatiotemporal patterns in spatiotemporal data. To date, although a lot of works have been proposed for mining patterns in spatiotemporal databases, there are some research areas that need further investigation. In this thesis, we focus on efficiently and effectively discovering the spatiotemporal patterns in three popular spatiotemporal data types: biological sequence data, snapshot data and moving object data. We outline our approaches as follows. First, we study the problem of mining mutation chains in biological sequences which are associated with location and time. We propose a mutation model where each biological sequence influences its spatiotemporal nearby biological sequences. We therefore define the notion of mutation chains and design an efficient algorithm to mine frequent mutation chains. Second, we tackle the problem of discovering localized and time-associated patterns in snapshot data. We propose an influence model where each object exerts an influence to its spatiotemporal nearby regions. Based on the influence model, we investigate this problems in two steps: We introduce the global Spatial Interaction Patterns (SIPs) on a single snapshot and propose a grid based influence model to mine the frequent SIPs. We further extend the SIPs to Geographical-specific Interaction Patterns (GIPs) and propose a quadtree based influence model and an efficient mining algorithm to mine frequent GIPs over time. Finally, we address the problem of discovering duration-aware trajectory patterns in moving object data for trajectory classification. The influences of moving objects to the regions are measured by the amount of time spent by the moving objects in the regions. Based on the influence, we introduce the duration-sensitive region rules and a top-down region partition approach to discover valid region rules. We also introduce the speed-differentiating path rule and propose a trajectory network to facilitate the mining of discriminative path rules. Two classifiers, TCF and TCRP, are built using the discovered region rules and path rules. Experiment results on real-world datasets show that both classifiers outperform the existing classifiers.
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
ShengC.pdf6.35 MBAdobe PDF



Page view(s)

checked on Apr 21, 2019


checked on Apr 21, 2019

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.