35,489 research outputs found
Functional Brain Imaging with Multi-Objective Multi-Modal Evolutionary Optimization
Functional brain imaging is a source of spatio-temporal data mining problems.
A new framework hybridizing multi-objective and multi-modal optimization is
proposed to formalize these data mining problems, and addressed through
Evolutionary Computation (EC). The merits of EC for spatio-temporal data mining
are demonstrated as the approach facilitates the modelling of the experts'
requirements, and flexibly accommodates their changing goals
STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-19
Human mobility estimation is crucial during the COVID-19 pandemic due to its
significant guidance for policymakers to make non-pharmaceutical interventions.
While deep learning approaches outperform conventional estimation techniques on
tasks with abundant training data, the continuously evolving pandemic poses a
significant challenge to solving this problem due to data nonstationarity,
limited observations, and complex social contexts. Prior works on mobility
estimation either focus on a single city or lack the ability to model the
spatio-temporal dependencies across cities and time periods. To address these
issues, we make the first attempt to tackle the cross-city human mobility
estimation problem through a deep meta-generative framework. We propose a
Spatio-Temporal Meta-Generative Adversarial Network (STORM-GAN) model that
estimates dynamic human mobility responses under a set of social and policy
conditions related to COVID-19. Facilitated by a novel spatio-temporal
task-based graph (STTG) embedding, STORM-GAN is capable of learning shared
knowledge from a spatio-temporal distribution of estimation tasks and quickly
adapting to new cities and time periods with limited training samples. The STTG
embedding component is designed to capture the similarities among cities to
mitigate cross-task heterogeneity. Experimental results on real-world data show
that the proposed approach can greatly improve estimation performance and
out-perform baselines.Comment: Accepted at the 22nd IEEE International Conference on Data Mining
(ICDM 2022) Full Pape
From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles
The inference of network topologies from relational data is an important
problem in data analysis. Exemplary applications include the reconstruction of
social ties from data on human interactions, the inference of gene
co-expression networks from DNA microarray data, or the learning of semantic
relationships based on co-occurrences of words in documents. Solving these
problems requires techniques to infer significant links in noisy relational
data. In this short paper, we propose a new statistical modeling framework to
address this challenge. It builds on generalized hypergeometric ensembles, a
class of generative stochastic models that give rise to analytically tractable
probability spaces of directed, multi-edge graphs. We show how this framework
can be used to assess the significance of links in noisy relational data. We
illustrate our method in two data sets capturing spatio-temporal proximity
relations between actors in a social system. The results show that our
analytical framework provides a new approach to infer significant links from
relational data, with interesting perspectives for the mining of data on social
systems.Comment: 10 pages, 8 figures, accepted at SocInfo201
Adapted K-Nearest Neighbors for Detecting Anomalies on Spatio–Temporal Traffic Flow
Outlier detection is an extensive research area, which has been intensively studied in several domains such as biological sciences, medical diagnosis, surveillance, and traffic anomaly detection. This paper explores advances in the outlier detection area by finding anomalies in spatio-temporal urban traffic flow. It proposes a new approach by considering the distribution of the flows in a given time interval. The flow distribution probability (FDP) databases are first constructed from the traffic flows by considering both spatial and temporal information. The outlier detection mechanism is then applied to the coming flow distribution probabilities, the inliers are stored to enrich the FDP databases, while the outliers are excluded from the FDP databases. Moreover, a k-nearest neighbor for distance-based outlier detection is investigated and adopted for FDP outlier detection. To validate the proposed framework, real data from Odense traffic flow case are evaluated at ten locations. The results reveal that the proposed framework is able to detect the real distribution of flow outliers. Another experiment has been carried out on Beijing data, the results show that our approach outperforms the baseline algorithms for high-urban traffic flow
A Framework for Spatio-Temporal Data Analysis and Hypothesis Exploration
We present a general framework for pattern discovery and hypothesis exploration in spatio-temporal data sets that is based on delay-embedding. This is a remarkable method of nonlinear time-series analysis that allows the full phase-space behaviour of a system to be reconstructed from only a single observable (accessible variable). Recent extensions to the theory that focus on a probabilistic interpretation extend its scope and allow practical application to noisy, uncertain and high-dimensional systems. The framework uses these extensions to aid alignment of spatio-temporal sub-models (hypotheses) to empirical data - for example satellite images plus remote-sensing - and to explore modifications consistent with this alignment. The novel aspect of the work is a mechanism for linking global and local dynamics using a holistic spatio-temporal feedback loop. An example framework is devised for an urban based application, transit centric developments, and its utility is demonstrated with real data
Clustering Customer Shopping Trips With Network Structure
Moving objects can be tracked with sensors such as RFID tags or GPS devices. Their movement can be represented as sequences of time-stamped locations. Studying such spatio-temporal movement sequences to discover spatial sequential patterns holds promises in many real-world settings. A few interesting applications are customer shopping traverse pattern discovery, vehicle traveling pattern discovery, and route prediction. Traditional spatial data mining algorithms suitable for the Euclidean space are not directly applicable in these settings. We propose a new algorithm to cluster movement paths such as shopping trips for pattern discovery. In our work, we represent the spatio-temporal series as sequences of discrete locations following a pre-defined network. We incorporate a modified version of the Longest Common Subsequence (LCS) algorithm with the network structure to measure the similarity of movement paths. With such spatial networks we implicitly address the existence of spatial obstructs as well. Experiments were performed on both hand-collected real-life trips and simulated trips in grocery shopping. The initial evaluation results show that our proposed approach, called Net-LCSS, can be used to support effective and efficient clustering for shopping trip pattern discovery
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