Abstract
We present techniques for computing small space representations of massive data streams. These are inspired by traditional wavelet-based approximations that consist of specific linear projections of the underlying data. We present general "sketch"-based methods for capturing various linear projections and use them to provide pointwise and rangesum estimation of data streams. These methods use small amounts of space and per-item time while streaming through the data and provide accurate representation as our experiments with real data streams show.
Original language | English (US) |
---|---|
Pages (from-to) | 541-554 |
Number of pages | 14 |
Journal | IEEE Transactions on Knowledge and Data Engineering |
Volume | 15 |
Issue number | 3 |
DOIs | |
State | Published - May 1 2003 |
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Keywords
- Approximate queries
- Data streams
- Randomized algorithms
- Wavelets
ASJC Scopus subject areas
- Information Systems
- Computer Science Applications
- Computational Theory and Mathematics
Cite this
One-pass wavelet decompositions of data streams. / Gilbert, Anna C.; Kotidis, Yannis; Muthukrishnan, Shanmugavelayutham; Strauss, Martin J.
In: IEEE Transactions on Knowledge and Data Engineering, Vol. 15, No. 3, 01.05.2003, p. 541-554.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - One-pass wavelet decompositions of data streams
AU - Gilbert, Anna C.
AU - Kotidis, Yannis
AU - Muthukrishnan, Shanmugavelayutham
AU - Strauss, Martin J.
PY - 2003/5/1
Y1 - 2003/5/1
N2 - We present techniques for computing small space representations of massive data streams. These are inspired by traditional wavelet-based approximations that consist of specific linear projections of the underlying data. We present general "sketch"-based methods for capturing various linear projections and use them to provide pointwise and rangesum estimation of data streams. These methods use small amounts of space and per-item time while streaming through the data and provide accurate representation as our experiments with real data streams show.
AB - We present techniques for computing small space representations of massive data streams. These are inspired by traditional wavelet-based approximations that consist of specific linear projections of the underlying data. We present general "sketch"-based methods for capturing various linear projections and use them to provide pointwise and rangesum estimation of data streams. These methods use small amounts of space and per-item time while streaming through the data and provide accurate representation as our experiments with real data streams show.
KW - Approximate queries
KW - Data streams
KW - Randomized algorithms
KW - Wavelets
UR - http://www.scopus.com/inward/record.url?scp=0037957085&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0037957085&partnerID=8YFLogxK
U2 - 10.1109/TKDE.2003.1198389
DO - 10.1109/TKDE.2003.1198389
M3 - Article
AN - SCOPUS:0037957085
VL - 15
SP - 541
EP - 554
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
SN - 1041-4347
IS - 3
ER -