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Article Citation - WoS: 11Citation - Scopus: 20Reinforcement Learning Using Fully Connected, Attention, and Transformer Models in Knapsack Problem Solving(Wiley, 2022) Yildiz, Beytullah; Yıldız, Beytullah; Yıldız, BeytullahKnapsack is a combinatorial optimization problem that involves a variety of resource allocation challenges. It is defined as non-deterministic polynomial time (NP) hard and has a wide range of applications. Knapsack problem (KP) has been studied in applied mathematics and computer science for decades. Many algorithms that can be classified as exact or approximate solutions have been proposed. Under the category of exact solutions, algorithms such as branch-and-bound and dynamic programming and the approaches obtained by combining these algorithms can be classified. Due to the fact that exact solutions require a long processing time, many approximate methods have been introduced for knapsack solution. In this research, deep Q-learning using models containing fully connected layers, attention, and transformer as function estimators were used to provide the solution for KP. We observed that deep Q-networks, which continued their training by observing the reward signals provided by the knapsack environment we developed, optimized the total reward gained over time. The results showed that our approaches give near-optimum solutions and work about 40 times faster than an exact algorithm using dynamic programming.Article Citation - WoS: 5Citation - Scopus: 9Optimizing Bitmap Index Encoding for High Performance Queries(Wiley, 2021) Yildiz, Beytullah; Yıldız, Beytullah; Yıldız, BeytullahMany sources such as historical archives, sensor readings, health systems, and machine records produce ever-increasing but often unchanging data. These accumulating data create a need for faster processing. Bitmap index, which can take advantage of multi-core and multiprocessor systems, is designed to process data that increase over time but do not change frequently. It has a well-known advantage, especially in queries on data with low cardinality. However, bitmap index can handle high cardinality data efficiently because it can use its own compression algorithm. Bitmap index has many encoding schemes that affect query processing time. In this study, we developed an algorithm that improves query performance by using optimal encoding among bitmap encodings. With this optimization algorithm, we witnessed up to 40% performance increase in queries made with bitmap indexes created with different encodings. Furthermore, in comparison with a commonly used relational database, we found significant improvements in the number of query operations per second performed on optimized encoded bitmap indexes generated by the introduced algorithm.
