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Title: Compression-based integral curve data reuse framework for flow visualization

Currently, by default, integral curves are repeatedly re-computed in different flow visualization applications, such as FTLE field computation, source-destination queries, etc., leading to unnecessary resource cost. We present a compression-based data reuse framework for integral curves, to greatly reduce their retrieval cost, especially in a resource-limited environment. In our design, a hierarchical and hybrid compression scheme is proposed to balance three objectives, including high compression ratio, controllable error, and low decompression cost. Specifically, we use and combine digitized curve sparse representation, floating-point data compression, and octree space partitioning to adaptively achieve the objectives. Results have shown that our data reuse framework could acquire tens of times acceleration in the resource-limited environment compared to on-the-fly particle tracing, and keep controllable information loss. Moreover, our method could provide fast integral curve retrieval for more complex data, such as unstructured mesh data.
Authors:
ORCiD logo [1] ;  [2] ;  [3] ;  [4] ;  [1]
  1. Peking Univ., Beijing (China). Key Lab. of Machine Perception (Ministry of Education), and School of EECS
  2. Tianjin Univ., Tianjin (China). School of Software
  3. Argonne National Lab. (ANL), Argonne, IL (United States). Mathematics and Computer Science Division
  4. Kyushu Univ. (Japan). Research Inst. for Information Technology; RIKEN, Kobe (Japan). Advanced Inst. for Computational Science
Publication Date:
Grant/Contract Number:
AC02-06CH11357; XDA05040205
Type:
Accepted Manuscript
Journal Name:
Journal of Visualization
Additional Journal Information:
Journal Volume: 20; Journal Issue: 4; Journal ID: ISSN 1343-8875
Publisher:
Springer Nature
Research Org:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org:
National Natural Science Foundation of China (NNSFC); Chinese Academy of Sciences (CAS); USDOE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; data compression; flow lines; flow visualization; high performance visualization; information retrieval; integral curves
OSTI Identifier:
1421954

Hong, Fan, Bi, Chongke, Guo, Hanqi, Ono, Kenji, and Yuan, Xiaoru. Compression-based integral curve data reuse framework for flow visualization. United States: N. p., Web. doi:10.1007/s12650-017-0428-4.
Hong, Fan, Bi, Chongke, Guo, Hanqi, Ono, Kenji, & Yuan, Xiaoru. Compression-based integral curve data reuse framework for flow visualization. United States. doi:10.1007/s12650-017-0428-4.
Hong, Fan, Bi, Chongke, Guo, Hanqi, Ono, Kenji, and Yuan, Xiaoru. 2017. "Compression-based integral curve data reuse framework for flow visualization". United States. doi:10.1007/s12650-017-0428-4. https://www.osti.gov/servlets/purl/1421954.
@article{osti_1421954,
title = {Compression-based integral curve data reuse framework for flow visualization},
author = {Hong, Fan and Bi, Chongke and Guo, Hanqi and Ono, Kenji and Yuan, Xiaoru},
abstractNote = {Currently, by default, integral curves are repeatedly re-computed in different flow visualization applications, such as FTLE field computation, source-destination queries, etc., leading to unnecessary resource cost. We present a compression-based data reuse framework for integral curves, to greatly reduce their retrieval cost, especially in a resource-limited environment. In our design, a hierarchical and hybrid compression scheme is proposed to balance three objectives, including high compression ratio, controllable error, and low decompression cost. Specifically, we use and combine digitized curve sparse representation, floating-point data compression, and octree space partitioning to adaptively achieve the objectives. Results have shown that our data reuse framework could acquire tens of times acceleration in the resource-limited environment compared to on-the-fly particle tracing, and keep controllable information loss. Moreover, our method could provide fast integral curve retrieval for more complex data, such as unstructured mesh data.},
doi = {10.1007/s12650-017-0428-4},
journal = {Journal of Visualization},
number = 4,
volume = 20,
place = {United States},
year = {2017},
month = {5}
}