Matching Items (12)
Description
Traditionally, databases have been categorized as either row-oriented or column-oriented databases. Row-oriented databases store each row of the table’s data contiguously onto the disk whereas column-oriented databases store each column’s data contiguously onto the disk. In recent years, columnar database management systems are becoming increasingly popular because deep and narrow queries are faster on them. Hence, column-oriented databases are highly optimized to be used with analytical (OLAP) workloads (Mike Freedman 2019). That is why they are very frequently used in business intelligence (BI), data warehouses, etc., which involve working with large data sets, intensive queries and aggregated computing. As the size of data keeps growing, efficient compression of data becomes an important consideration for these databases to optimize storage as well as improve query performance. Since column-oriented databases store data of the same data type contiguously, most modern compression techniques provide better compression ratios as compared to row-oriented databases. This thesis introduces a new compression technique called SA128 for column-oriented databases that performs a column-wise compression of database tables. SA128 is a multi-stage compression technique which performs a column-wise compression followed by a table-wide compression of database tables. In the first stage, SA128 performs an analysis based on the characteristics of data (such as data type and distribution) and determines which combination of lossless compression algorithms would result in the best compression ratio. In the second phase, SA128 uses an entropy encoding technique such as rANS (Duda, J., 2013) to further improve the compression ratio.
ContributorsAnand, Sukhpreet Singh (Author) / Bansal, Ajay (Thesis advisor) / Heinrichs, Robert R (Committee member) / Gonzalez-Sanchez, Javier (Committee member) / Arizona State University (Publisher)
Created2021
Description
In this paper, I explore practical applications of neural networks for automated skin lesion identification. The visual characteristics are of primary importance in the recognition of skin diseases, hence, the development of deep neural network models proven capable of classifying skin lesions can potentially change the face of modern medicine by extending the availability and lowering the cost of diagnostic care. Previous work has demonstrated the effectiveness of convolutional neural networks in image classification in general, with even higher accuracy achievable by data augmentation techniques, such as cropping, rotating, and flipping input images, along with more advanced computationally intensive approaches. In this research, I provide an overview of Convolutional Neural Networks (CNN) and CNN implementation with TensorFlow and Keras API in context of image recognition and classification. I also experiment with custom convolutional neural network model architecture trained using HAM10000 dataset. The dataset used for the case study is obtained from Harvard Dataverse and is maintained by Medical University of Vienna. The HAM10000 dataset is a large collection of multi-source dermatoscopic images of common pigmented skin lesions and is available for academic research under Creative Commons Attribution-Noncommercial 4.0 International Public License. With over ten thousand dermatoscopic images of seven classes of benign and malignant skin lesions, the dataset is substantial for academic machine learning purposes for multiclass image classification. I discuss the successes and shortcomings of the model in respect to its application to the dataset.
ContributorsKaraliova, Natallia (Author) / Bansal, Ajay (Thesis director) / Gonzalez-Sanchez, Javier (Committee member) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05