A novel algorithm and implementation of real-time identification and tracking of blob-filaments in fusion reactor data is presented. Similar spatio-temporal features are important in many other applications, for example, ignition kernels in combustion and tumor cells in a medical image. This work presents an approach for extracting these features by dividing the overall task into three steps: local identification of feature cells, grouping feature cells into extended feature, and tracking movement of feature through overlapping in space. Through our extensive work in parallelization, we demonstrate that this approach can effectively make use of a large number of compute nodes to detect and track blob-filaments in real time in fusion plasma. On a set of 30GB fusion simulation data, we observed linear speedup on 1024 processes and completed blob detection in less than three milliseconds using Edison, a Cray XC30 system at NERSC.
The problem is to design an efficient and effective approach to detect and track different shapes of region outliers simultaneously in fusion plasma data streams. By identifying and monitoring these blob-filaments (region outliers), scientists can gain a better understanding about this phenomena. In addition, a data stream is an ordered sequence of data that arrives continuously and has to be processed online. Due to the high arrival rate of data, the blob detection must finish processing before the next data chunk arrives @cite_59 . Therefore, another critical problem is to develop a high-performance blob detection approach in order to meet the real-time requirements.
@cite_59
2039841137
In applications, such as sensor networks and power usage monitoring, data are in the form of streams, each of which is an infinite sequence of data points with explicit or implicit timestamps and has special characteristics, such as transiency, uncertainty, dynamic data distribution, multidimensionality, and dynamic relationship. These characteristics introduce new research issues that make outlier detection for stream data more challenging than that for regular (non-stream) data. This paper discusses those research issues for applications where data come from a single stream as well as multiple streams.