Abstract
As the relevant data sets get large, existing in-memory schemes for tensor decomposition become increasingly ineffective and, instead, memory-independent solutions, such as in-database analytics, are necessitated. In this paper, we present techniques for efficient implementations of in-database tensor decompositions on chunk-based array data stores. The proposed static and incremental in-database tensor decomposition operators and their optimizations address the constraints imposed by the main memory limitations when handling large and high-order tensor data. Firstly, we discuss how to implement alternating least squares operations efficiently on a chunk-based data storage system. Secondly, we consider scenarios with frequent data updates and show that compressed matrix multiplication techniques can be effective in reducing the incremental tensor decomposition maintenance costs. To the best of our knowledge, this paper presents the first attempt to develop efficient and optimized in-database tensor decomposition operations. We evaluate the proposed algorithms on tensor data sets that do not fit into the available memory and results show that the proposed techniques significantly improve the scalability of this core data analysis.
| Original language | English (US) |
|---|---|
| Title of host publication | CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 969-978 |
| Number of pages | 10 |
| ISBN (Print) | 9781450325981 |
| DOIs | |
| State | Published - Nov 3 2014 |
| Event | 23rd ACM International Conference on Information and Knowledge Management, CIKM 2014 - Shanghai, China Duration: Nov 3 2014 → Nov 7 2014 |
Other
| Other | 23rd ACM International Conference on Information and Knowledge Management, CIKM 2014 |
|---|---|
| Country | China |
| City | Shanghai |
| Period | 11/3/14 → 11/7/14 |
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Keywords
- In-database tensor decomposition
- Tensor decomposition
ASJC Scopus subject areas
- Information Systems and Management
- Computer Science Applications
- Information Systems
Cite this
Efficient static and dynamic in-database tensor decompositions on chunk-based array stores. / Kim, Mijung; Candan, Kasim.
CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, Inc, 2014. p. 969-978.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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TY - GEN
T1 - Efficient static and dynamic in-database tensor decompositions on chunk-based array stores
AU - Kim, Mijung
AU - Candan, Kasim
PY - 2014/11/3
Y1 - 2014/11/3
N2 - As the relevant data sets get large, existing in-memory schemes for tensor decomposition become increasingly ineffective and, instead, memory-independent solutions, such as in-database analytics, are necessitated. In this paper, we present techniques for efficient implementations of in-database tensor decompositions on chunk-based array data stores. The proposed static and incremental in-database tensor decomposition operators and their optimizations address the constraints imposed by the main memory limitations when handling large and high-order tensor data. Firstly, we discuss how to implement alternating least squares operations efficiently on a chunk-based data storage system. Secondly, we consider scenarios with frequent data updates and show that compressed matrix multiplication techniques can be effective in reducing the incremental tensor decomposition maintenance costs. To the best of our knowledge, this paper presents the first attempt to develop efficient and optimized in-database tensor decomposition operations. We evaluate the proposed algorithms on tensor data sets that do not fit into the available memory and results show that the proposed techniques significantly improve the scalability of this core data analysis.
AB - As the relevant data sets get large, existing in-memory schemes for tensor decomposition become increasingly ineffective and, instead, memory-independent solutions, such as in-database analytics, are necessitated. In this paper, we present techniques for efficient implementations of in-database tensor decompositions on chunk-based array data stores. The proposed static and incremental in-database tensor decomposition operators and their optimizations address the constraints imposed by the main memory limitations when handling large and high-order tensor data. Firstly, we discuss how to implement alternating least squares operations efficiently on a chunk-based data storage system. Secondly, we consider scenarios with frequent data updates and show that compressed matrix multiplication techniques can be effective in reducing the incremental tensor decomposition maintenance costs. To the best of our knowledge, this paper presents the first attempt to develop efficient and optimized in-database tensor decomposition operations. We evaluate the proposed algorithms on tensor data sets that do not fit into the available memory and results show that the proposed techniques significantly improve the scalability of this core data analysis.
KW - In-database tensor decomposition
KW - Tensor decomposition
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U2 - 10.1145/2661829.2661864
DO - 10.1145/2661829.2661864
M3 - Conference contribution
SN - 9781450325981
SP - 969
EP - 978
BT - CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery, Inc
ER -