No abstract

Filtering data based on predicates is one of the most fundamental operations for any modern data warehouse. Techniques to accelerate the execution of filter expressions include clustered indexes, specialized sort orders (e.g., Z-order), multi-dimensional indexes, and, for high selectivity queries, secondary indexes. However, these schemes are hard to tune and their performance is inconsistent. Recent work on learned multi-dimensional indexes has introduced the idea of automatically optimizing an index for a particular dataset and workload. However, the performance of that work suffers in the presence of correlated data and skewed query workloads, both of which are common in real applications. In this paper, we introduce Tsunami, which addresses these limitations to achieve up to 6X faster query performance and up to 8X smaller index size than existing learned multi-dimensional indexes, in addition to up to 11X faster query performance and 170X smaller index size than optimally-tuned traditional indexes.

Filtering data based on predicates is one of the most fundamental operations for any modern data warehouse. Techniques to accelerate the execution of filter expressions include clustered indexes, specialized sort orders (e.g., Z-order), multi-dimensional indexes, and, for high selectivity queries, secondary indexes. However, these schemes are hard to tune and their performance is inconsistent. Recent work on learned multi-dimensional indexes has introduced the idea of automatically optimizing an index for a particular dataset and workload. However, the performance of that work suffers in the presence of correlated data and skewed query workloads, both of which are common in real applications. In this paper, we introduce Tsunami, which addresses these limitations to achieve up to 6× faster query performance and up to 8× smaller index size than existing learned multi-dimensional indexes, in addition to up to 11× faster query performance and 170× smaller index size than optimally-tuned traditional indexes.

Biobanks now contain genetic data from millions of individuals. Dimensionality reduction, visualization and stratification are standard when exploring data at these scales; while efficient and tractable methods exist for the first two, stratification remains challenging because of uncertainty about sources of population structure. In practice, stratification is commonly performed by drawing shapes around dimensionally reduced data or assuming populations have a "type" genome. We propose a method of stratifying data with topological analysis that is fast, easy to implement, and integrates with existing pipelines. The approach is robust to the presence of sub-populations of varying sizes and wide ranges of population structure patterns. We demonstrate its effectiveness on genotypes from three biobanks and illustrate how topological genetic strata can help us understand structure within biobanks, evaluate distributions of genotypic and phenotypic data, examine polygenic score transferability, identify potential influential alleles, and perform quality control.

Our human civilization has encountered many dangerous diseases and death due to lung cancer is increasing worldwide. The finding of lung cancer cells in the earliest stage is the most challenging process in the medical field. To address the issue, digital image processing techniques, along with neuro-fuzzy logic, has experimented in this current study. The system is aimed to help in the initial stage detection of lung nodules by classifying the existence of abnormalities in the computed tomography (CT) scan image. The critical stages involved in the detection process are preprocessing, image enhancement, image segmentation, feature extraction and neuro-fuzzy algorithm. The proposed system is an entirely functional automatic method which completely avoids physical calculation and this designed system produces a better result with a high accuracy rate.