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Abstract: We consider the problem of joining massive datasets. We propose two techniques for minimizing disk I/O cost of join operations for both spatial and sequence data. Our techniques optimize the available buffer space using a global view of the datasets. We build a boolean matrix on the pages of the given datasets using a lower bounding distance predictor. The marked entries of this matrix represent candidate page pairs to be joined. Our first technique joins the marked pages iteratively. Our... (Update)
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...entries, r marked rows and c marked columns, then pm NLJ performs at least e rain r, c disk I Os for that cluster. Proof: omitted (cf. [24]) For the example in Figure 3, r = 3, c = 2, and e = 5. The total number of disk I Os is 5 rain 2, 3 = 7. Note that NLJ is the same...
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BibTeX entry: (Update)
T. Kahveci, C. A. Lang, and A. K. Singh. Joining massive highdimensional datasets. Technical Report 30, UCSB, 2002. http://citeseer.ist.psu.edu/article/kahveci03joining.html More
@inproceedings{ kahveci-joining,
author = "Tamer Kahveci and Christian Lang and Ambuj K Singh",
title = "Variable Length Queries for Time Series Data",
booktitle = "{ICDE}",
year = "2003",
url = "citeseer.ist.psu.edu/article/kahveci03joining.html",
url = "citeseer.nj.nec.com/kahveci03joining.html" }
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