TY - GEN
T1 - Portable Parallel Design of Weighted Multi-Dimensional Scaling for Real-Time Data Analysis
AU - Dash, Sajal
AU - Verma, Anshuman
AU - North, Chris
AU - Feng, Wu Chun
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Projecting a high-dimensional dataset onto a lower dimensional space can improve the efficiency of knowledge discovery and facilitate real-time data analysis. One technique for dimension reduction, weighted multi-dimensional scaling (WMDS), approximately preserves pairwise weighted distances during the transformation; but its O(f(n)d) algorithm impedes real-time performance on large datasets. Thus, we present CLARET, our fast and portable parallel WMDS tool that combines algorithmic concepts adapted and extended from the stochastic force-based MDS (SF-MDS) and Glimmer. To further improve Claret's performance for real-time data analysis, we propose a preprocessing step that computes approximate weighted Euclidean distances by combining a novel data mapping called stretching and Johnson Lindestrauss' lemma in O(log d) time in place of the original O(d) time. This preprocessing step reduces the complexity of WMDS from O(f(n)d) to O(f(n) log d), which for large d is a significant computational gain. Finally, we present a case study of Claret by integrating it into an interactive visualization tool called V2PI to facilitate real-time analytics. To ensure the quality of the projections, we propose a geometric shape matching-based alignment process and a quality metric.
AB - Projecting a high-dimensional dataset onto a lower dimensional space can improve the efficiency of knowledge discovery and facilitate real-time data analysis. One technique for dimension reduction, weighted multi-dimensional scaling (WMDS), approximately preserves pairwise weighted distances during the transformation; but its O(f(n)d) algorithm impedes real-time performance on large datasets. Thus, we present CLARET, our fast and portable parallel WMDS tool that combines algorithmic concepts adapted and extended from the stochastic force-based MDS (SF-MDS) and Glimmer. To further improve Claret's performance for real-time data analysis, we propose a preprocessing step that computes approximate weighted Euclidean distances by combining a novel data mapping called stretching and Johnson Lindestrauss' lemma in O(log d) time in place of the original O(d) time. This preprocessing step reduces the complexity of WMDS from O(f(n)d) to O(f(n) log d), which for large d is a significant computational gain. Finally, we present a case study of Claret by integrating it into an interactive visualization tool called V2PI to facilitate real-time analytics. To ensure the quality of the projections, we propose a geometric shape matching-based alignment process and a quality metric.
KW - OpenCL
KW - multi-dimensional scaling (MDS)
KW - visual analytics
KW - weighted multi-dimensional scaling (WMDS)
UR - http://www.scopus.com/inward/record.url?scp=85047627680&partnerID=8YFLogxK
U2 - 10.1109/HPCC-SmartCity-DSS.2017.2
DO - 10.1109/HPCC-SmartCity-DSS.2017.2
M3 - Conference contribution
AN - SCOPUS:85047627680
T3 - Proceedings - 2017 IEEE 19th Intl Conference on High Performance Computing and Communications, HPCC 2017, 2017 IEEE 15th Intl Conference on Smart City, SmartCity 2017 and 2017 IEEE 3rd Intl Conference on Data Science and Systems, DSS 2017
SP - 10
EP - 17
BT - Proceedings - 2017 IEEE 19th Intl Conference on High Performance Computing and Communications, HPCC 2017, 2017 IEEE 15th Intl Conference on Smart City, SmartCity 2017 and 2017 IEEE 3rd Intl Conference on Data Science and Systems, DSS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE Intl Conference on High Performance Computing and Communications, 15th IEEE Intl Conference on Smart City, and 3rd IEEE Intl Conference on Data Science and Systems, HPCC/SmartCity/DSS 2017
Y2 - 18 December 2017 through 20 December 2017
ER -