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Adaptive Join Operators for Result Rate Optimization on Streaming Inputs
August 2010 (vol. 22 no. 8)
pp. 1110-1125
ASCII Text
x 
 
Mihaela A. Bornea, Vasilis Vassalos, Yannis Kotidis, Antonios Deligiannakis, "Adaptive Join Operators for Result Rate Optimization on Streaming Inputs," IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 8, pp. 1110-1125, August, 2010.
 
 
BibTex
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@article{ 10.1109/TKDE.2010.64,
author = {Mihaela A. Bornea and Vasilis Vassalos and Yannis Kotidis and Antonios Deligiannakis},
title = {Adaptive Join Operators for Result Rate Optimization on Streaming Inputs},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {22},
number = {8},
issn = {1041-4347},
year = {2010},
pages = {1110-1125},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.64},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Adaptive Join Operators for Result Rate Optimization on Streaming Inputs
IS - 8
SN - 1041-4347
SP1110
EP1125
EPD - 1110-1125
A1 - Mihaela A. Bornea,
A1 - Vasilis Vassalos,
A1 - Yannis Kotidis,
A1 - Antonios Deligiannakis,
PY - 2010
KW - Query processing
KW - join
KW - DINER
KW - MINER
KW - streams.
VL - 22
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Mihaela A. Bornea, Athens University of Economics and Business, Athens, Greece
Vasilis Vassalos, Athens University of Economics and Business, Athens, Greece
Yannis Kotidis, Athens University of Economics and Business, Athens, Greece
Antonios Deligiannakis, Technical University of Crete, Chania, Greece
Adaptive join algorithms have recently attracted a lot of attention in emerging applications where data are provided by autonomous data sources through heterogeneous network environments. Their main advantage over traditional join techniques is that they can start producing join results as soon as the first input tuples are available, thus, improving pipelining by smoothing join result production and by masking source or network delays. In this paper, we first propose Double Index NEsted-loops Reactive join (DINER), a new adaptive two-way join algorithm for result rate maximization. DINER combines two key elements: an intuitive flushing policy that aims to increase the productivity of in-memory tuples in producing results during the online phase of the join, and a novel reentrant join technique that allows the algorithm to rapidly switch between processing in-memory and disk-resident tuples, thus, better exploiting temporary delays when new data are not available. We then extend the applicability of the proposed technique for a more challenging setup: handling more than two inputs. Multiple Index NEsted-loop Reactive join (MINER) is a multiway join operator that inherits its principles from DINER. Our experiments using real and synthetic data sets demonstrate that DINER outperforms previous adaptive join algorithms in producing result tuples at a significantly higher rate, while making better use of the available memory. Our experiments also shows that in the presence of multiple inputs, MINER manages to produce a high percentage of early results, outperforming existing techniques for adaptive multiway join.

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Index Terms:
Query processing, join, DINER, MINER, streams.
Citation:
Mihaela A. Bornea, Vasilis Vassalos, Yannis Kotidis, Antonios Deligiannakis, "Adaptive Join Operators for Result Rate Optimization on Streaming Inputs," IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 8, pp. 1110-1125, Aug. 2010, doi:10.1109/TKDE.2010.64
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