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TU Berlin

Inhalt des Dokuments


Bearbeiter: Thomas Otfried Bodner

Betreuer: Max Heimel

Angestrebter Abschluss: Master


Within the past decade, industrial and academic organizations have built many
software stacks for data-intensive analysis. More recently, there have been efforts

to create new data programming languages on top of these stacks to better suit

various target audiences. The current trend is to ease this process by employing

compiler frameworks that provide language building blocks and a common base

for optimizations known from different fields, such as relational database systems,

information extraction, machine learning and programming language compilers.

In this thesis, we present extensions to RelL, which is one such compiler framework.

On the one hand, we extend the imperative RelL programming interface (API)

for the specification of a more complete subset of the extended relational algebra.

One the other hand, we add an extensible rule-based optimization phase with

rules to reorder relational as well as user-defined operations to the RelL compiler.

As shown in case studies, the extended version of the RelL API is capable to express

analysis programs common in relational OLAP (Online Analytical Processing).

As shown through experimental evaluations, our approach to optimization is able

to reorder relational operations in this imperative setting, thereby improving

performance by orders of magnitude for an increasingly important program class.

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