18 research outputs found
Incremental Principal Component Analysis Based Outliers Detection Methods for Spatiotemporal Data Streams
In this paper, we address outliers in spatiotemporal data streams obtained from sensors placed across geographically distributed locations. Outliers may appear in such sensor data due to various reasons such as instrumental error and environmental change. Real-time detection of these outliers is essential to prevent propagation of errors in subsequent analyses and results. Incremental Principal Component Analysis (IPCA) is one possible approach for detecting outliers in such type of spatiotemporal data streams. IPCA has been widely used in many real-time applications such as credit card fraud detection, pattern recognition, and image analysis. However, the suitability of applying IPCA for outlier detection in spatiotemporal data streams is unknown and needs to be investigated. To fill this research gap, this paper contributes by presenting two new IPCA-based outlier detection methods and performing a comparative analysis with the existing IPCA-based outlier detection methods to assess their suitability for spatiotemporal sensor data streams
A Distributed and Incremental SVD Algorithm for Agglomerative Data Analysis on Large Networks
In this paper, we show that the SVD of a matrix can be constructed
efficiently in a hierarchical approach. Our algorithm is proven to recover the
singular values and left singular vectors if the rank of the input matrix
is known. Further, the hierarchical algorithm can be used to recover the
largest singular values and left singular vectors with bounded error. We also
show that the proposed method is stable with respect to roundoff errors or
corruption of the original matrix entries. Numerical experiments validate the
proposed algorithms and parallel cost analysis
Practical global illumination for interactive particle visualization
ManuscriptParticle-based simulation methods are used to model a wide range of complex phenomena and to solve time-dependent problems of various scales. Effective visualizations of the resulting state will communicate subtle changes in the three-dimensional structure, spatial organization, and qualitative trends within a simulation as it evolves. We present two algorithms targeting upcoming, highly parallel multicore desktop systems to enable interactive navigation and exploration of large particle datasets with global illumination effects. Monte Carlo path tracing and texture mapping are used to capture computationally expensive illumination effects such as soft shadows and diffuse interreflection. The first approach is based on precomputation of luminance textures and removes expensive illumination calculations from the interactive rendering pipeline. The second approach is based on dynamic luminance texture generation and decouples interactive rendering from the computation of global illumination effects. These algorithms provide visual cues that enhance the ability to perform analysis and feature detection tasks while interrogating the data at interactive rates. We explore the performance of these algorithms and demonstrate their effectiveness using several large datasets