Abstract

Today's data management systems increasingly need to support both tensor-algebraic operations (for analysis) as well as relational-algebraic operations (for data manipulation and integration). Tensor decomposition techniques are commonly used for discovering underlying structures of multidimensional data sets. However, 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. We introduce an in-database analytic system for efficient implementations of in-database tensor decompositions on chunk-based array data stores, so called, TensorDB. TensorDB includes static in-database tensor decomposition and dynamic in-database tensor decomposition operators. TensorDB extends an array database and leverages array operations for data manipulation and integration. TensorDB supports complex data processing plans where multiple relational algebraic and tensor algebraic operations are composed with each other.

Original languageEnglish (US)
Title of host publicationCIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery, Inc
Pages2039-2041
Number of pages3
ISBN (Print)9781450325981
DOIs
StatePublished - Nov 3 2014
Event23rd ACM International Conference on Information and Knowledge Management, CIKM 2014 - Shanghai, China
Duration: Nov 3 2014Nov 7 2014

Other

Other23rd ACM International Conference on Information and Knowledge Management, CIKM 2014
CountryChina
CityShanghai
Period11/3/1411/7/14

Keywords

  • In-database tensor decomposition
  • Tensor decomposition

ASJC Scopus subject areas

  • Information Systems and Management
  • Computer Science Applications
  • Information Systems

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  • Cite this

    Kim, M., & Candan, K. (2014). TensorDB: In-database tensor manipulation with tensor-relational query plans. In CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management (pp. 2039-2041). Association for Computing Machinery, Inc. https://doi.org/10.1145/2661829.2661842