Abstract

From data collection to decision making, the life cycle of data often involves many steps of integration, manipulation, and analysis. To be able to provide end-to-end support for the full data life cycle, today's data management and decision making systems increasingly combine operations for data manipulation, integration as well as data analysis. Tensor-relational model (TRM) is a framework proposed to support both relational algebraic operations (for data manipulation and integration) and tensor algebraic operations (for data analysis). In this paper, we consider joint processing of relational algebraic and tensor analysis operations. In particular, we focus on data processing workflows that involve data integration from multiple sources (through unions) and tensor decomposition tasks. While, in traditional relational algebra, the costliest operation is known to be the join, in a framework that provides both relational and tensor operations, tensor decomposition tends to be the computationally costliest operation. Therefore, it is most critical to reduce the cost of the tensor decomposition task by manipulating the data processing workflow in a way that reduces the cost of the tensor decomposition step. Therefore, in this paper, we consider data processing workflows involving tensor decomposition and union operations and we propose a novel scheme for pushing down the tensor decompositions over the union operations to reduce the overall data processing times and to promote reuse of materialized tensor decomposition results. Experimental results confirm the efficiency and effectiveness of the proposed scheme.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages688-704
Number of pages17
Volume8724 LNAI
EditionPART 1
ISBN (Print)9783662448472
DOIs
StatePublished - 2014
EventEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2014 - Nancy, France
Duration: Sep 15 2014Sep 19 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8724 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

OtherEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2014
CountryFrance
CityNancy
Period9/15/149/19/14

Fingerprint

Tensor Decomposition
Tensors
Reuse
Union
Decomposition
Decompose
Tensor
Work Flow
Manipulation
Life Cycle
Data analysis
Decision Making
Life cycle
Relational Algebra
Decision making
Relational Model
Data Integration
Costs
Data Management
Data integration

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Kim, M., & Candan, K. (2014). Pushing-down tensor decompositions over unions to promote reuse of materialized decompositions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 1 ed., Vol. 8724 LNAI, pp. 688-704). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8724 LNAI, No. PART 1). Springer Verlag. https://doi.org/10.1007/978-3-662-44848-9_44

Pushing-down tensor decompositions over unions to promote reuse of materialized decompositions. / Kim, Mijung; Candan, Kasim.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8724 LNAI PART 1. ed. Springer Verlag, 2014. p. 688-704 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8724 LNAI, No. PART 1).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kim, M & Candan, K 2014, Pushing-down tensor decompositions over unions to promote reuse of materialized decompositions. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 edn, vol. 8724 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 8724 LNAI, Springer Verlag, pp. 688-704, European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2014, Nancy, France, 9/15/14. https://doi.org/10.1007/978-3-662-44848-9_44
Kim M, Candan K. Pushing-down tensor decompositions over unions to promote reuse of materialized decompositions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 ed. Vol. 8724 LNAI. Springer Verlag. 2014. p. 688-704. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). https://doi.org/10.1007/978-3-662-44848-9_44
Kim, Mijung ; Candan, Kasim. / Pushing-down tensor decompositions over unions to promote reuse of materialized decompositions. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8724 LNAI PART 1. ed. Springer Verlag, 2014. pp. 688-704 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).
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