The surge in demand for upscale apartments in South Korea in the 2000s necessitates the enhanced quality of apartment complexes. Achieving this improvement involves creating diverse spaces within complexes and categorizing them based on spatial arrangement. However, obtaining actual as-built drawings poses challenges, and manual analysis lacks objectivity. The study utilized map API for data collection and Roboflow API for labeling, employing a YOLOv8n-cls model for categorization. Performance evaluation included accuracy, precision, recall, and F1-score values using a confusion matrix. Eigen-CAM was utilized for an analysis that revealed the specific features influencing predictions. The classification model demonstrated relatively high accuracy. Furthermore, the prediction performance was high for lattice and square apartment complexes but low for distributed apartment complexes. These results indicate that a classification model is insufficient for assessing complex characteristics such as the scattered arrangement of building layouts and outdoor spaces, as seen in distributed apartment complexes. We determined that an in-depth analysis of the architectural plans for distributed apartment complexes is necessary to clearly identify their types, and the types must be categorized into several classes, including the distributed type.

Log-Structured Merge-tree (LSM-tree) organizes write-friendly and hierarchical structure, which leads to inevitable disk I/O from data compaction occuring between layers. Previous research tried to reduce write bottlenecks of LSM-tree by using non-volatile memory (NVM) to LSM-tree, but write amplification on compaction does not resolved. In this paper, we present tiered LSM (TLSM), a persistent LSM-based key-value database tiering NVM, which is regarded as fast storage device. Our design aims to exploit multi-layered LSM system utilizing NVM and reduce redundant cost on compaction. We address 4 key points: (1) analysis of disk I/O overhead on current LSM-tree, (2) novel design of TLSM preserving the concept of LSM-tree and utilizing NVM, (3) persistent and byte-addressable data compaction minimizing disk write, and (4) tiering policy for increasing usability to TLSM. To implement TLSM-tree, we extended LevelDB, which is a simple LSM-based key-value store. On micro-benchmark DB Bench, the write and read latencies of TLSM are improved up to 34% and 57%, respectively, compared to those of LevelDB-NVM. In evaluation of YCSB, TLSM also reduces the write and read latencies of existing LevelDB up to 25%, 33%, respectively. Furthermore, frequency of write stall and total amount of data write are reduced significantly in TLSM.