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Cartesian decomposition in data analysis. / Emelyanov, Pavel; Ponomaryov, Denis.

Proceedings - 2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 55-60 8071964.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Emelyanov, P & Ponomaryov, D 2017, Cartesian decomposition in data analysis. in Proceedings - 2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017., 8071964, Institute of Electrical and Electronics Engineers Inc., pp. 55-60, 2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017, Novosibirsk, Akademgorodok, Russian Federation, 12.04.2017. https://doi.org/10.1109/SSDSE.2017.8071964

APA

Emelyanov, P., & Ponomaryov, D. (2017). Cartesian decomposition in data analysis. In Proceedings - 2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017 (pp. 55-60). [8071964] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSDSE.2017.8071964

Vancouver

Emelyanov P, Ponomaryov D. Cartesian decomposition in data analysis. In Proceedings - 2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 55-60. 8071964 doi: 10.1109/SSDSE.2017.8071964

Author

Emelyanov, Pavel ; Ponomaryov, Denis. / Cartesian decomposition in data analysis. Proceedings - 2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 55-60

BibTeX

@inproceedings{4ff59076afd040e8803a2c9851a5e849,
title = "Cartesian decomposition in data analysis",
abstract = "We consider the Cartesian decomposition of relational data sets, i.e. the problem of finding two or several data sets such that their unordered Cartesian product equals the source set. In terms of relational databases, this means reversing the SQL CROSS JOIN operator. We describe a polytime algorithm for computing a Cartesian decomposition based on factorization of boolean polynomials. We provide an implementation of the algorithm in Transact SQL and discuss some generalizations of the Cartesian decomposition.",
keywords = "Data Analysis, Databases, Partitioning Algorithms",
author = "Pavel Emelyanov and Denis Ponomaryov",
year = "2017",
month = oct,
day = "18",
doi = "10.1109/SSDSE.2017.8071964",
language = "English",
pages = "55--60",
booktitle = "Proceedings - 2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",
note = "2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017 ; Conference date: 12-04-2017 Through 13-04-2017",

}

RIS

TY - GEN

T1 - Cartesian decomposition in data analysis

AU - Emelyanov, Pavel

AU - Ponomaryov, Denis

PY - 2017/10/18

Y1 - 2017/10/18

N2 - We consider the Cartesian decomposition of relational data sets, i.e. the problem of finding two or several data sets such that their unordered Cartesian product equals the source set. In terms of relational databases, this means reversing the SQL CROSS JOIN operator. We describe a polytime algorithm for computing a Cartesian decomposition based on factorization of boolean polynomials. We provide an implementation of the algorithm in Transact SQL and discuss some generalizations of the Cartesian decomposition.

AB - We consider the Cartesian decomposition of relational data sets, i.e. the problem of finding two or several data sets such that their unordered Cartesian product equals the source set. In terms of relational databases, this means reversing the SQL CROSS JOIN operator. We describe a polytime algorithm for computing a Cartesian decomposition based on factorization of boolean polynomials. We provide an implementation of the algorithm in Transact SQL and discuss some generalizations of the Cartesian decomposition.

KW - Data Analysis

KW - Databases

KW - Partitioning Algorithms

UR - http://www.scopus.com/inward/record.url?scp=85040374812&partnerID=8YFLogxK

UR - https://elibrary.ru/item.asp?id=35517614

U2 - 10.1109/SSDSE.2017.8071964

DO - 10.1109/SSDSE.2017.8071964

M3 - Conference contribution

AN - SCOPUS:85040374812

SP - 55

EP - 60

BT - Proceedings - 2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017

Y2 - 12 April 2017 through 13 April 2017

ER -

ID: 9642016