27,973 research outputs found
An Emergent Space for Distributed Data with Hidden Internal Order through Manifold Learning
Manifold-learning techniques are routinely used in mining complex
spatiotemporal data to extract useful, parsimonious data
representations/parametrizations; these are, in turn, useful in nonlinear model
identification tasks. We focus here on the case of time series data that can
ultimately be modelled as a spatially distributed system (e.g. a partial
differential equation, PDE), but where we do not know the space in which this
PDE should be formulated. Hence, even the spatial coordinates for the
distributed system themselves need to be identified - to emerge from - the data
mining process. We will first validate this emergent space reconstruction for
time series sampled without space labels in known PDEs; this brings up the
issue of observability of physical space from temporal observation data, and
the transition from spatially resolved to lumped (order-parameter-based)
representations by tuning the scale of the data mining kernels. We will then
present actual emergent space discovery illustrations. Our illustrative
examples include chimera states (states of coexisting coherent and incoherent
dynamics), and chaotic as well as quasiperiodic spatiotemporal dynamics,
arising in partial differential equations and/or in heterogeneous networks. We
also discuss how data-driven spatial coordinates can be extracted in ways
invariant to the nature of the measuring instrument. Such gauge-invariant data
mining can go beyond the fusion of heterogeneous observations of the same
system, to the possible matching of apparently different systems
Discovery of Spatiotemporal Event Sequences
Finding frequent patterns plays a vital role in many analytics tasks such as finding itemsets, associations, correlations, and sequences. In recent decades, spatiotemporal frequent pattern mining has emerged with the main goal focused on developing data-driven analysis frameworks for understanding underlying spatial and temporal characteristics in massive datasets. In this thesis, we will focus on discovering spatiotemporal event sequences from large-scale region trajectory datasetes with event annotations. Spatiotemporal event sequences are the series of event types whose trajectory-based instances follow each other in spatiotemporal context. We introduce new data models for storing and processing evolving region trajectories, provide a novel framework for modeling spatiotemporal follow relationships, and present novel spatiotemporal event sequence mining algorithms
Spatiotemporal data analysis with chronological networks
The amount and size of spatiotemporal data sets from different domains have
been rapidly increasing in the last years, which demands the development of
robust and fast methods to analyze and extract information from them. In this
paper, we propose a network-based model for spatiotemporal data analysis called
chronnet. It consists of dividing a geometrical space into grid cells
represented by nodes connected chronologically. The main goal of this model is
to represent consecutive recurrent events between cells with strong links in
the network. This representation permits the use of network science and
graphing mining tools to extract information from spatiotemporal data. The
chronnet construction process is fast, which makes it suitable for large data
sets. In this paper, we describe how to use our model considering artificial
and real data. For this purpose, we propose an artificial spatiotemporal data
set generator to show how chronnets capture not just simple statistics, but
also frequent patterns, spatial changes, outliers, and spatiotemporal clusters.
Additionally, we analyze a real-world data set composed of global fire
detections, in which we describe the frequency of fire events, outlier fire
detections, and the seasonal activity, using a single chronnet
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