Abstract
In this paper we present a new algorithm based on a weighted projection quantiles for fast and frugal real time quantile estimation of large sized high dimensional data clouds. We present a projection quantile regression algorithm for high dimensional data. Second, we present a fast algorithm for computing the depth of a point or a new observation in relation to any high-dimensional data cloud, and propose a ranking system for multivariate data. Third, we briefly describe a real time rapid monitoring scheme similar to statistical process monitoring, for actionable analytics with big data. We believe these algorithms would be very useful for real time analysis of high dimensional 'big data' sets including streaming data sets. The proposed algorithms would be of immense use in several application areas such as real time financial market analysis, real time remote health monitoring of patients using body area networked devices and real time pricing and inventory decisions in retail and manufacturing sector.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014 |
| Editors | Wo Chang, Jun Huan, Nick Cercone, Saumyadipta Pyne, Vasant Honavar, Jimmy Lin, Xiaohua Tony Hu, Charu Aggarwal, Bamshad Mobasher, Jian Pei, Raghunath Nambiar |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 64-71 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781479956654 |
| DOIs | |
| State | Published - Jan 1 2014 |
| Event | 2nd IEEE International Conference on Big Data, IEEE Big Data 2014 - Washington, United States Duration: Oct 27 2014 → Oct 30 2014 |
Publication series
| Name | Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014 |
|---|
Other
| Other | 2nd IEEE International Conference on Big Data, IEEE Big Data 2014 |
|---|---|
| Country | United States |
| City | Washington |
| Period | 10/27/14 → 10/30/14 |
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Keywords
- 'Big Data'
- body area network
- data depth estimation
- quantile regression
- real time analysis
- real time health monitoring
- weighted projection quantiles
Cite this
Fast algorithm for computing weighted projection quantiles and data depth for high-dimensional large data clouds. / Mukherjee, Ujjal Kumar; Chatterjee, Snigdhansu.
Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014. ed. / Wo Chang; Jun Huan; Nick Cercone; Saumyadipta Pyne; Vasant Honavar; Jimmy Lin; Xiaohua Tony Hu; Charu Aggarwal; Bamshad Mobasher; Jian Pei; Raghunath Nambiar. Institute of Electrical and Electronics Engineers Inc., 2014. p. 64-71 7004358 (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Fast algorithm for computing weighted projection quantiles and data depth for high-dimensional large data clouds
AU - Mukherjee, Ujjal Kumar
AU - Chatterjee, Snigdhansu
PY - 2014/1/1
Y1 - 2014/1/1
N2 - In this paper we present a new algorithm based on a weighted projection quantiles for fast and frugal real time quantile estimation of large sized high dimensional data clouds. We present a projection quantile regression algorithm for high dimensional data. Second, we present a fast algorithm for computing the depth of a point or a new observation in relation to any high-dimensional data cloud, and propose a ranking system for multivariate data. Third, we briefly describe a real time rapid monitoring scheme similar to statistical process monitoring, for actionable analytics with big data. We believe these algorithms would be very useful for real time analysis of high dimensional 'big data' sets including streaming data sets. The proposed algorithms would be of immense use in several application areas such as real time financial market analysis, real time remote health monitoring of patients using body area networked devices and real time pricing and inventory decisions in retail and manufacturing sector.
AB - In this paper we present a new algorithm based on a weighted projection quantiles for fast and frugal real time quantile estimation of large sized high dimensional data clouds. We present a projection quantile regression algorithm for high dimensional data. Second, we present a fast algorithm for computing the depth of a point or a new observation in relation to any high-dimensional data cloud, and propose a ranking system for multivariate data. Third, we briefly describe a real time rapid monitoring scheme similar to statistical process monitoring, for actionable analytics with big data. We believe these algorithms would be very useful for real time analysis of high dimensional 'big data' sets including streaming data sets. The proposed algorithms would be of immense use in several application areas such as real time financial market analysis, real time remote health monitoring of patients using body area networked devices and real time pricing and inventory decisions in retail and manufacturing sector.
KW - 'Big Data'
KW - body area network
KW - data depth estimation
KW - quantile regression
KW - real time analysis
KW - real time health monitoring
KW - weighted projection quantiles
UR - http://www.scopus.com/inward/record.url?scp=84988273584&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84988273584&partnerID=8YFLogxK
U2 - 10.1109/BigData.2014.7004358
DO - 10.1109/BigData.2014.7004358
M3 - Conference contribution
AN - SCOPUS:84988273584
T3 - Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014
SP - 64
EP - 71
BT - Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014
A2 - Chang, Wo
A2 - Huan, Jun
A2 - Cercone, Nick
A2 - Pyne, Saumyadipta
A2 - Honavar, Vasant
A2 - Lin, Jimmy
A2 - Hu, Xiaohua Tony
A2 - Aggarwal, Charu
A2 - Mobasher, Bamshad
A2 - Pei, Jian
A2 - Nambiar, Raghunath
PB - Institute of Electrical and Electronics Engineers Inc.
ER -