3,404 research outputs found
A survey of temporal knowledge discovery paradigms and methods
With the increase in the size of data sets, data mining has recently become an important research topic and is receiving substantial interest from both academia and industry. At the same time, interest in temporal databases has been increasing and a growing number of both prototype and implemented systems are using an enhanced temporal understanding to explain aspects of behavior associated with the implicit time-varying nature of the universe. This paper investigates the confluence of these two areas, surveys the work to date, and explores the issues involved and the outstanding problems in temporal data mining
A Survey on Behavioral Pattern Mining from Sensor Data in Internet of Things
The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services. The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas. For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes). However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time. In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks. This paper presents the technical challenges that need to be considered for mining sensor data. It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared. We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area. © 2013 IEEE
Probing turbulent superstructures in Rayleigh-B\'{e}nard convection by Lagrangian trajectory clusters
We analyze large-scale patterns in three-dimensional turbulent convection in
a horizontally extended square convection cell by Lagrangian particle
trajectories calculated in direct numerical simulations. A simulation run at a
Prandtl number Pr , a Rayleigh number Ra , and an aspect ratio
is therefore considered. These large-scale structures, which are
denoted as turbulent superstructures of convection, are detected by the
spectrum of the graph Laplacian matrix. Our investigation, which follows
Hadjighasem {\it et al.}, Phys. Rev. E {\bf 93}, 063107 (2016), builds a
weighted and undirected graph from the trajectory points of Lagrangian
particles. Weights at the edges of the graph are determined by a mean dynamical
distance between different particle trajectories. It is demonstrated that the
resulting trajectory clusters, which are obtained by a subsequent -means
clustering, coincide with the superstructures in the Eulerian frame of
reference. Furthermore, the characteristic times and lengths
of the superstructures in the Lagrangian frame of reference agree
very well with their Eulerian counterparts, and ,
respectively. This trajectory-based clustering is found to work for times
. Longer time periods require a
change of the analysis method to a density-based trajectory clustering by means
of time-averaged Lagrangian pseudo-trajectories, which is applied in this
context for the first time. A small coherent subset of the pseudo-trajectories
is obtained in this way consisting of those Lagrangian particles that are
trapped for long times in the core of the superstructure circulation rolls and
are thus not subject to ongoing turbulent dispersion.Comment: 12 pages, 7 downsized figures, to appear in Phys. Rev. Fluid
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