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Title: TensorDB and Tensor-Relational Model (TRM) for Efficient Tensor-Relational Operations

Citation Type: Dissertation/Thesis

Publication Year: 2014

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Abstract: Multidimensional data have various representations. Thanks to their simplicity in modeling multidimensional data and the availability of various mathematical tools (such as tensor decomposition) that support multi-aspect analysis of such data, tensors are increasingly being used in many application domains including scientific data management, sensor data management, and social network data analysis. Relational model, on the other hand, enables semantic manipulation of data using relational operators, such as projection, selection, Catesian-product and set operators. For many multidimensional data applications, tensor operations as well as relational operations need to be supported throughout the data life cycle. In this thesis, we introduce a tensor-based relational data model (TRM), which enables both tensor-based data analysis and relational manipulations of multidimensional data, and define tensor-relational operations on this model. Then we introduce a tensor-relational data management system, so called, Tensor DB. TensorDB is based on TRM, which brings together relational algebraic operations (for data manipulation and integration) and tensor algebraic operations (for data analysis). We develop optimization strategies for tensor-relational operations in both in-memory and in-database TensorDB. The goal of the TRM and TensorDB is to serve as a single environment that supports the entire life cycle of data; that is, data can be manipulated, integrated, processed, and analyzed.

Url: http://repository.asu.edu/attachments/137278/content/Kim_asu_0010E_14162.pdf

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Authors: Kim, Mijung

Institution: Arizona State University

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Publisher Location: Tempe, Arizona

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Data Collections: IPUMS USA

Topics: Other

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