7 research outputs found
The Use of MPI and OpenMP Technologies for Subsequence Similarity Search in Very Large Time Series on Computer Cluster System with Nodes Based on the Intel Xeon Phi Knights Landing Many-core Processor
Nowadays, subsequence similarity search is required in a wide range of time
series mining applications: climate modeling, financial forecasts, medical
research, etc. In most of these applications, the Dynamic TimeWarping (DTW)
similarity measure is used since DTW is empirically confirmed as one of the
best similarity measure for most subject domains. Since the DTW measure has a
quadratic computational complexity w.r.t. the length of query subsequence, a
number of parallel algorithms for various many-core architectures have been
developed, namely FPGA, GPU, and Intel MIC. In this article, we propose a new
parallel algorithm for subsequence similarity search in very large time series
on computer cluster systems with nodes based on Intel Xeon Phi Knights Landing
(KNL) many-core processors. Computations are parallelized on two levels as
follows: through MPI at the level of all cluster nodes, and through OpenMP
within one cluster node. The algorithm involves additional data structures and
redundant computations, which make it possible to effectively use the
capabilities of vector computations on Phi KNL. Experimental evaluation of the
algorithm on real-world and synthetic datasets shows that it is highly
scalable.Comment: Accepted for publication in the "Numerical Methods and Programming"
journal (http://num-meth.srcc.msu.ru/english/, in Russian "Vychislitelnye
Metody i Programmirovanie"), in Russia
A line-profile based double partial fusion method for acquiring planning CT of oversized patients in radiation treatment
True 3D CT dataset for treatment planning of an oversized patient is difficult to acquire due to the bore size and field of view (FOV) reconstruction. This project aims to provide a simple approach to reconstruct true CT data for oversize patients using CT scanner with limited FOV by acquiring double partial CT (left and right side) images. An efficient line profile-based method has been developed to minimize the difference of the CT numbers in the overlapping region between the right and left images and to generate a complete true 3D CT dataset in the natural state. New image processing modules have been developed and integrated to the Insight Segmentation & Registration Toolkit (ITK 3.6) package. For example, different modules for image cropping, line profile generation, line profile matching, and optimized partial image fusion have been developed. The algorithm has been implemented for images containing the bony structure of the spine and tested on 3D CT planning datasets from both phantom and real patients with satisfactory results in both cases. The proposed optimized line profile-based partial registration method provides a simple and accurate method for acquiring a complete true 3D CT dataset for an oversized patient using CT scanning with small bore size, that can be used for accurate treatment planning
KV-match: A Subsequence Matching Approach Supporting Normalization and Time Warping [Extended Version]
The volume of time series data has exploded due to the popularity of new
applications, such as data center management and IoT. Subsequence matching is a
fundamental task in mining time series data. All index-based approaches only
consider raw subsequence matching (RSM) and do not support subsequence
normalization. UCR Suite can deal with normalized subsequence match problem
(NSM), but it needs to scan full time series. In this paper, we propose a novel
problem, named constrained normalized subsequence matching problem (cNSM),
which adds some constraints to NSM problem. The cNSM problem provides a knob to
flexibly control the degree of offset shifting and amplitude scaling, which
enables users to build the index to process the query. We propose a new index
structure, KV-index, and the matching algorithm, KV-match. With a single index,
our approach can support both RSM and cNSM problems under either ED or DTW
distance. KV-index is a key-value structure, which can be easily implemented on
local files or HBase tables. To support the query of arbitrary lengths, we
extend KV-match to KV-match, which utilizes multiple varied-length
indexes to process the query. We conduct extensive experiments on synthetic and
real-world datasets. The results verify the effectiveness and efficiency of our
approach.Comment: 13 page
Data semantic enrichment for complex event processing over IoT Data Streams
This thesis generalizes techniques for processing IoT data streams, semantically enrich data with contextual information, as well as complex event processing in IoT applications. A case study for ECG anomaly detection and signal classification was conducted to validate the knowledge foundation