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Project: A Counterpart of SQL for Matrices and Tensors

Most commercial databases are relational and use SQL to query the data. Often, however, data is not relational. Indeed, data scientists often deal with matrices instead of relations. A counterpart of SQL for the matrices and tensors is therefore needed, and initial  progress has been reported [1].

Various projects are possible on this subject, ranging from theory-oriented projects aimed at studying the expressivity and normal forms to implementation-oriented projects aimed at implementation and translations from (fragments of) existing languages like numpy or R.

[1] R. Brijder, F. Geerts, J. Van den Bussche, and T. Weerwag, MATLANG: Matrix operations and their expressive power. ACM SIGMOD Record, v. 48, 60-67, 2019.

Robert Brijder
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