On the complexity of inferring functional dependencies
Discrete Applied Mathematics - Special issue on combinatorial problems in databases
Journal of the ACM (JACM)
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Efficient Discovery of Functional Dependencies and Armstrong Relations
EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
DaWaK '01 Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery
CORDS: automatic discovery of correlations and soft functional dependencies
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Higher-Order Web Link Analysis Using Multilinear Algebra
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Scalable Tensor Decompositions for Multi-aspect Data Mining
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A Parallel Nonnegative Tensor Factorization Algorithm for Mining Global Climate Data
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
PARAFAC algorithms for large-scale problems
Neurocomputing
Nonnegative Tensor Factorization Accelerated Using GPGPU
IEEE Transactions on Parallel and Distributed Systems
Approximate tensor decomposition within a tensor-relational algebraic framework
Proceedings of the 20th ACM international conference on Information and knowledge management
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For many multi-dimensional data applications, tensor operations as well as relational operations need to be supported throughout the data lifecycle. Although tensor decomposition is shown to be effective for multi-dimensional data analysis, the cost of tensor decomposition is often very high. We propose a novel decomposition-by-normalization scheme that first normalizes the given relation into smaller tensors based on the functional dependencies of the relation and then performs the decomposition using these smaller tensors. The decomposition and recombination steps of the decomposition-by- normalization scheme fit naturally in settings with multiple cores. This leads to a highly efficient, effective, and parallelized decomposition-by-normalization algorithm for both dense and sparse tensors. Experiments confirm the efficiency and effectiveness of the proposed decomposition-by-normalization scheme compared to the conventional nonnegative CP decomposition approach.