Abstract
We assume a data set that is vertically decomposed among several servers, and a client that wishes to compute the skyline by obtaining the minimum number of points. Existing solutions for this problem are restricted to the case where each server maintains exactly one dimension. This paper proposes a general solution for vertical decompositions of arbitrary dimensionality. We first investigate some interesting problem characteristics regarding the pruning power of points. Then, we introduce vertical partition skyline (VPS), an algorithmic framework that includes two steps. Phase 1 searches for an anchor point (P anc) that dominates, and hence eliminates, a large number of records. Starting with (Panc), Phase 2 constructs incrementally a pruning area using an interesting union-intersection property of dominance regions. Servers do not transmit points that fall within the pruning area in their local subspace. Our experiments confirm the effectiveness of the proposed methods under various settings.
| Original language | English |
|---|---|
| Article number | 6109261 |
| Pages (from-to) | 850-862 |
| Number of pages | 13 |
| Journal | IEEE Transactions on Knowledge and Data Engineering |
| Volume | 25 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2013 |
| Externally published | Yes |
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Keywords
- Distributed skyline
- query processing
- vertical partitioning
ASJC Scopus subject areas
- Information Systems
- Computer Science Applications
- Computational Theory and Mathematics
Cite this
Skyline processing on distributed vertical decompositions. / Trimponias, George; Bartolini, Ilaria; Papadias, Dimitris; Yang, Yin.
In: IEEE Transactions on Knowledge and Data Engineering, Vol. 25, No. 4, 6109261, 2013, p. 850-862.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Skyline processing on distributed vertical decompositions
AU - Trimponias, George
AU - Bartolini, Ilaria
AU - Papadias, Dimitris
AU - Yang, Yin
PY - 2013
Y1 - 2013
N2 - We assume a data set that is vertically decomposed among several servers, and a client that wishes to compute the skyline by obtaining the minimum number of points. Existing solutions for this problem are restricted to the case where each server maintains exactly one dimension. This paper proposes a general solution for vertical decompositions of arbitrary dimensionality. We first investigate some interesting problem characteristics regarding the pruning power of points. Then, we introduce vertical partition skyline (VPS), an algorithmic framework that includes two steps. Phase 1 searches for an anchor point (P anc) that dominates, and hence eliminates, a large number of records. Starting with (Panc), Phase 2 constructs incrementally a pruning area using an interesting union-intersection property of dominance regions. Servers do not transmit points that fall within the pruning area in their local subspace. Our experiments confirm the effectiveness of the proposed methods under various settings.
AB - We assume a data set that is vertically decomposed among several servers, and a client that wishes to compute the skyline by obtaining the minimum number of points. Existing solutions for this problem are restricted to the case where each server maintains exactly one dimension. This paper proposes a general solution for vertical decompositions of arbitrary dimensionality. We first investigate some interesting problem characteristics regarding the pruning power of points. Then, we introduce vertical partition skyline (VPS), an algorithmic framework that includes two steps. Phase 1 searches for an anchor point (P anc) that dominates, and hence eliminates, a large number of records. Starting with (Panc), Phase 2 constructs incrementally a pruning area using an interesting union-intersection property of dominance regions. Servers do not transmit points that fall within the pruning area in their local subspace. Our experiments confirm the effectiveness of the proposed methods under various settings.
KW - Distributed skyline
KW - query processing
KW - vertical partitioning
UR - http://www.scopus.com/inward/record.url?scp=84874620275&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84874620275&partnerID=8YFLogxK
U2 - 10.1109/TKDE.2011.266
DO - 10.1109/TKDE.2011.266
M3 - Article
VL - 25
SP - 850
EP - 862
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
SN - 1041-4347
IS - 4
M1 - 6109261
ER -