Physical Sciences and Mathematics Commons^™

Indexer++: Workload-Aware Online Index Tuning With Transformers And Reinforcement Learning, Vishal Sharma, Curtis Dyreson

Computer Science Student Research

With the increasing workload complexity in modern databases, the manual process of index selection is a challenging task. There is a growing need for a database with an ability to learn and adapt to evolving workloads. This paper proposes Indexer++, an autonomous, workload-aware, online index tuner. Unlike existing approaches, Indexer++ imposes low overhead on the DBMS, is responsive to changes in query workloads and swiftly selects indexes. Our approach uses a combination of text analytic techniques and reinforcement learning. Indexer++ consist of two phases: Phase (i) learns workload trends using a novel trend detection technique based on a pre-trained …

Artificial Intelligence And Deep Reinforcement Learning Stock Market Predictions, Andrew W. Brim

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Billions of dollars are traded automatically in the stock market every day, including algorithms that use artificial intelligence (AI) techniques, but there are still questions regarding how AI trades successfully. The black box nature of these AI techniques, namely neural networks, gives pause to entrusting it with valuable trading funds. This dissertation applies AI techniques to stock market trading strategies, but it also provides exploratory research into how these techniques predict the stock market successfully.

This dissertation presents the work of three research papers. The first paper presented in this dissertation applies a artificial intelligence technique, reinforcement learning, to candlestick …