2 research outputs found
BitE : Accelerating Learned Query Optimization in a Mixed-Workload Environment
Although the many efforts to apply deep reinforcement learning to query
optimization in recent years, there remains room for improvement as query
optimizers are complex entities that require hand-designed tuning of workloads
and datasets. Recent research present learned query optimizations results
mostly in bulks of single workloads which focus on picking up the unique traits
of the specific workload. This proves to be problematic in scenarios where the
different characteristics of multiple workloads and datasets are to be mixed
and learned together. Henceforth, in this paper, we propose BitE, a novel
ensemble learning model using database statistics and metadata to tune a
learned query optimizer for enhancing performance. On the way, we introduce
multiple revisions to solve several challenges: we extend the search space for
the optimal Abstract SQL Plan(represented as a JSON object called ASP) by
expanding hintsets, we steer the model away from the default plans that may be
biased by configuring the experience with all unique plans of queries, and we
deviate from the traditional loss functions and choose an alternative method to
cope with underestimation and overestimation of reward. Our model achieves
19.6% more improved queries and 15.8% less regressed queries compared to the
existing traditional methods whilst using a comparable level of resources.Comment: This work was done when the first three author were interns in SAP
Labs Korea and they have equal contributio
DGU-HAO: A Dataset With Daily Life Objects for Comprehensive 3D Human Action Analysis
The importance of a high-quality dataset availability in 3D human action analysis research cannot be overstated. This paper introduces DGU-HAO (Human Action analysis dataset with daily life Objects). This novel 3D human action multi-modality dataset encompasses four distinct data modalities accompanied by annotation data, including motion capture, RGB video, image, and 3D object modeling data. It features 63 action classes involving interactions with 60 common furniture and electronic devices. Each action class comprises approximately 1,000 motion capture data representing 3D skeleton data and corresponding RGB video and 3D object modeling data, resulting in 67,505 motion capture data samples. It offers comprehensive 3D structural information of the human, RGB images and videos, and point cloud data for 60 objects, collected through the participation of 126 subjects to ensure inclusivity and account for diverse human body types. To validate our dataset, we leveraged MMNet, a 3D human action recognition model, achieving Top-1 accuracy of 91.51% and 92.29% using the skeleton joint and bone methods, respectively. Beyond human action recognition, our versatile dataset is valuable for various 3D human action analysis research endeavors