Singularities Chief Data Scientist awarded by the IEEE


Singularities Chief Data Scientist Luis L. Pérez, recently published two papers at the at the 2017 IEEE International Conference on Data Engineering (ICDE17), symposium that addresses research issues in designing, building, managing, and evaluating advanced data-intensive systems and applications, and one of the leading forums for researchers, practitioners, developers, and users.

One of his papers, titled Scalable Linear Algebra on a Relational Database System, written along co-authors Shangyu Luo, Zekai "Jacob" Gao, and Michael Gubanov, won the Best Paper award at the conference.

The purpose of the paper is to demonstrate the feasibility of building scalable linear algebra on top of a parallel/distributed relational database system, thus, facilitating large-scale data analytics.

In this paper, the authors tackle the problem from a different angle, and end up proving a ground-breaking method to use large-scale linear algebra computations over parallel/distributed relational database systems. One of the main concepts to notice is “that brand new systems designed from the ground up to support scalable linear algebra are not absolutely necessary, and that such systems could instead be built on top of existing relational technology. Our results also suggest that if scalable linear algebra is to be added to a modern dataflow platform such as Spark, they should be added on top of the system’s more structured (relational) data abstractions, rather than being constructed directly on top of the system’s raw dataflow operators.”

Click here to read the full paper.

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