TY - GEN
T1 - TensorDB
T2 - 23rd ACM International Conference on Information and Knowledge Management, CIKM 2014
AU - Kim, Mijung
AU - Candan, Kasim
PY - 2014/11/3
Y1 - 2014/11/3
N2 - 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.
AB - 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.
KW - In-database tensor decomposition
KW - Tensor decomposition
UR - http://www.scopus.com/inward/record.url?scp=84937565217&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84937565217&partnerID=8YFLogxK
U2 - 10.1145/2661829.2661842
DO - 10.1145/2661829.2661842
M3 - Conference contribution
AN - SCOPUS:84937565217
T3 - CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management
SP - 2039
EP - 2041
BT - CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
Y2 - 3 November 2014 through 7 November 2014
ER -