Abschlussarbeiten

The idea is to expose building blocks of database systems, for example hash tables, for data mining and machine learning algorithms. The thesis should build upon an existing open-source database system, for example Hyrise, where you should first write selected algorithms in SQL. Afterwards, you should improve the performance of the algorithms by adding certain suboperators to the database system. For example, you can perform gradient descent iteratively using recursive CTEs. Then you create an operator for iterations, for example trampolin, with less memory consumption. The building blocks should be accessed through an extension of SQL by user-defined functions (UDFs), calles SQL++.

LeanStore is an open-source system for OLTP and OLAP transaction but lacks an SQL interface. The goal of this thesis is to write a query compiler in C++.

Modern database system generate code instead of interpreting function call for an operator trees. In this thesis, you have to generate code to run on GPUs and investigate, how SIMT (single instruction mutliple threads) will accelerate query processing.

You have to implement a protocol to access camera data for crowd detection.

For this topic, you have to ellaborate use cases for higher-order lambda functions in SQL. Lambda functions exist only as functions to customise database operators but the plan is to extend database systems by higher-order functions. A use case would be a ModelDB: a relation that stores different models that should be executed at runtime. In your thesis, you have to invent use cases for higher-order SQL lambda functions based on existing models for data pipelines.

Write a NumPy to SQL compiler based on a relational representation of matrices to compute matrix algebra in database systems.

Write or generate SQL queries in order to solve mathematical equation in SQL.

Generate the SQL code for automatically deriving model functions to train neural networks in SQL.