Standard
Workload-independent data-driven vertical partitioning. / Bobrov, Nikita; Chernishev, George; Novikov, Boris.
New Trends in Databases and Information Systems - ADBIS 2017 Short Papers and Workshops AMSD, BigNovelTI, DAS, SW4CH, DC, Proceedings. ed. / Jerome Darmont; Marite Kirikova; Kjetil Norvag; Robert Wrembel; George A. Papadopoulos; Johann Gamper; Stefano Rizzi. Springer Nature, 2017. p. 275-284 (Communications in Computer and Information Science; Vol. 767).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Harvard
Bobrov, N
, Chernishev, G & Novikov, B 2017,
Workload-independent data-driven vertical partitioning. in J Darmont, M Kirikova, K Norvag, R Wrembel, GA Papadopoulos, J Gamper & S Rizzi (eds),
New Trends in Databases and Information Systems - ADBIS 2017 Short Papers and Workshops AMSD, BigNovelTI, DAS, SW4CH, DC, Proceedings. Communications in Computer and Information Science, vol. 767, Springer Nature, pp. 275-284, 21st European Conference on Advances in Databases and Information Systems, ADBIS 2017 and 1st workshop on Data Driven Approaches for Analyzing and Managing Scholarly Data, AMSD 2017, 1st workshop on Novel Techniques for Integrating Big Data, BigNovelTI 2017, 1st International workshop on Data Science: Methodologies and Use-Cases, DaS 2017, 2nd International workshop on Semantic Web for Cultural Heritage, SW4CH 2017, Nicosia, Cyprus,
24/09/17.
https://doi.org/10.1007/978-3-319-67162-8_27APA
Bobrov, N.
, Chernishev, G., & Novikov, B. (2017).
Workload-independent data-driven vertical partitioning. In J. Darmont, M. Kirikova, K. Norvag, R. Wrembel, G. A. Papadopoulos, J. Gamper, & S. Rizzi (Eds.),
New Trends in Databases and Information Systems - ADBIS 2017 Short Papers and Workshops AMSD, BigNovelTI, DAS, SW4CH, DC, Proceedings (pp. 275-284). (Communications in Computer and Information Science; Vol. 767). Springer Nature.
https://doi.org/10.1007/978-3-319-67162-8_27Vancouver
Bobrov N
, Chernishev G, Novikov B.
Workload-independent data-driven vertical partitioning. In Darmont J, Kirikova M, Norvag K, Wrembel R, Papadopoulos GA, Gamper J, Rizzi S, editors, New Trends in Databases and Information Systems - ADBIS 2017 Short Papers and Workshops AMSD, BigNovelTI, DAS, SW4CH, DC, Proceedings. Springer Nature. 2017. p. 275-284. (Communications in Computer and Information Science).
https://doi.org/10.1007/978-3-319-67162-8_27Author
Bobrov, Nikita
; Chernishev, George ; Novikov, Boris. /
Workload-independent data-driven vertical partitioning. New Trends in Databases and Information Systems - ADBIS 2017 Short Papers and Workshops AMSD, BigNovelTI, DAS, SW4CH, DC, Proceedings. editor / Jerome Darmont ; Marite Kirikova ; Kjetil Norvag ; Robert Wrembel ; George A. Papadopoulos ; Johann Gamper ; Stefano Rizzi. Springer Nature, 2017. pp. 275-284 (Communications in Computer and Information Science).
BibTeX
@inproceedings{10a0e2d72474476d8c815ca4a9f93d19,
title = "Workload-independent data-driven vertical partitioning",
abstract = "Vertical partitioning is a well-explored area of automatic physical database design. The classic approach is as follows: Derive an optimal vertical partitioning scheme for a given database and a workload. The workload describes queries, their frequencies, and involved attributes. In this paper we identify a novel class of vertical partitioning algorithms. The algorithms of this class do not rely on knowledge of the workload, but instead use data properties that are contained in the workload itself. We propose such algorithm that uses a logical scheme represented by functional dependencies, which are derived from stored data. In order to discover functional dependencies we use TANE — a popular functional dependency extraction algorithm. We evaluate our algorithm using an industrial DBMS (PostgreSQL) on number of workloads. We compare the performance of an unpartitioned configuration with partitions produced by our algorithm and several state-of-the-art workload-aware algorithms.",
keywords = "Functional dependency, Physical design, Vertical partitioning",
author = "Nikita Bobrov and George Chernishev and Boris Novikov",
year = "2017",
month = oct,
day = "1",
doi = "10.1007/978-3-319-67162-8_27",
language = "English",
isbn = "9783319671611",
series = "Communications in Computer and Information Science",
publisher = "Springer Nature",
pages = "275--284",
editor = "Jerome Darmont and Marite Kirikova and Kjetil Norvag and Robert Wrembel and Papadopoulos, {George A.} and Johann Gamper and Stefano Rizzi",
booktitle = "New Trends in Databases and Information Systems - ADBIS 2017 Short Papers and Workshops AMSD, BigNovelTI, DAS, SW4CH, DC, Proceedings",
address = "Germany",
note = "21st European Conference on Advances in Databases and Information Systems, ADBIS 2017 and 1st workshop on Data Driven Approaches for Analyzing and Managing Scholarly Data, AMSD 2017, 1st workshop on Novel Techniques for Integrating Big Data, BigNovelTI 2017, 1st International workshop on Data Science: Methodologies and Use-Cases, DaS 2017, 2nd International workshop on Semantic Web for Cultural Heritage, SW4CH 2017 ; Conference date: 24-09-2017 Through 27-09-2017",
}
RIS
TY - GEN
T1 - Workload-independent data-driven vertical partitioning
AU - Bobrov, Nikita
AU - Chernishev, George
AU - Novikov, Boris
PY - 2017/10/1
Y1 - 2017/10/1
N2 - Vertical partitioning is a well-explored area of automatic physical database design. The classic approach is as follows: Derive an optimal vertical partitioning scheme for a given database and a workload. The workload describes queries, their frequencies, and involved attributes. In this paper we identify a novel class of vertical partitioning algorithms. The algorithms of this class do not rely on knowledge of the workload, but instead use data properties that are contained in the workload itself. We propose such algorithm that uses a logical scheme represented by functional dependencies, which are derived from stored data. In order to discover functional dependencies we use TANE — a popular functional dependency extraction algorithm. We evaluate our algorithm using an industrial DBMS (PostgreSQL) on number of workloads. We compare the performance of an unpartitioned configuration with partitions produced by our algorithm and several state-of-the-art workload-aware algorithms.
AB - Vertical partitioning is a well-explored area of automatic physical database design. The classic approach is as follows: Derive an optimal vertical partitioning scheme for a given database and a workload. The workload describes queries, their frequencies, and involved attributes. In this paper we identify a novel class of vertical partitioning algorithms. The algorithms of this class do not rely on knowledge of the workload, but instead use data properties that are contained in the workload itself. We propose such algorithm that uses a logical scheme represented by functional dependencies, which are derived from stored data. In order to discover functional dependencies we use TANE — a popular functional dependency extraction algorithm. We evaluate our algorithm using an industrial DBMS (PostgreSQL) on number of workloads. We compare the performance of an unpartitioned configuration with partitions produced by our algorithm and several state-of-the-art workload-aware algorithms.
KW - Functional dependency
KW - Physical design
KW - Vertical partitioning
UR - http://www.scopus.com/inward/record.url?scp=85029792724&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-67162-8_27
DO - 10.1007/978-3-319-67162-8_27
M3 - Conference contribution
AN - SCOPUS:85029792724
SN - 9783319671611
T3 - Communications in Computer and Information Science
SP - 275
EP - 284
BT - New Trends in Databases and Information Systems - ADBIS 2017 Short Papers and Workshops AMSD, BigNovelTI, DAS, SW4CH, DC, Proceedings
A2 - Darmont, Jerome
A2 - Kirikova, Marite
A2 - Norvag, Kjetil
A2 - Wrembel, Robert
A2 - Papadopoulos, George A.
A2 - Gamper, Johann
A2 - Rizzi, Stefano
PB - Springer Nature
T2 - 21st European Conference on Advances in Databases and Information Systems, ADBIS 2017 and 1st workshop on Data Driven Approaches for Analyzing and Managing Scholarly Data, AMSD 2017, 1st workshop on Novel Techniques for Integrating Big Data, BigNovelTI 2017, 1st International workshop on Data Science: Methodologies and Use-Cases, DaS 2017, 2nd International workshop on Semantic Web for Cultural Heritage, SW4CH 2017
Y2 - 24 September 2017 through 27 September 2017
ER -
