A5051718290- Data Management and Algorithms
- Algorithms and Data Compression
- Explainable Artificial Intelligence (XAI)
- Distributed systems and fault tolerance
- Advanced Database Systems and Queries
- Data Analysis with R
- Data Stream Mining Techniques
- Machine Learning and Data Classification
- Data Quality and Management
- Data Mining Algorithms and Applications
Dankook University
2023-2025
In general, most of the main experiments in paper ''Can Learned Indexes be Built Efficiently? A Deep Dive into Sampling Trade-offs'' can successfully reproduced.
The authors provided detailed descriptions for setting up the re- quired system and performing experiments using individual scripts provided. While required considerable effort time, instructions facilitated evaluating results generating graphs that reproduced same patterns claimed by in paper Optimizing Distributed Protocols with Query Rewrites
The purpose of this paper is proposing a novel search mechanism, called SLR (Segmented Linear Regression) search, based on the concept learned index. It motivated by our observation that lot big data, collected and used previous studies, have linearity property, meaning keys their stored locations show strong linear correlation. This leads us to design where we apply segmentation into well-known machine learning algorithm, regression, for identifying location from given key. We devise two...
By embedding the distribution of keys in indexing structure, learned indexes can minimize index size and maximize lookup performance. Yet, one problems present is long index-building time. The conventional requires a complete traversal entire dataset, which makes it less practical than traditional index. This paper challenges efficiency build time to make practical. Our approach for time-efficient employ sampled learning. In this paper, we two error-bounded sampling schemes: Sample EB-PLA,...