0000000001101790
AUTHOR
Mario Randazzo
showing 4 related works from this author
A big data approach for sequences indexing on the cloud via burrows wheeler transform
2020
Indexing sequence data is important in the context of Precision Medicine, where large amounts of "omics"data have to be daily collected and analyzed in order to categorize patients and identify the most effective therapies. Here we propose an algorithm for the computation of Burrows Wheeler transform relying on Big Data technologies, i.e., Apache Spark and Hadoop. Our approach is the first that distributes the index computation and not only the input dataset, allowing to fully benefit of the available cloud resources. Copyright © 2020 for this paper by its authors.
A perceptual sound space for auditory displays based on sung-vowel synthesis.
2022
AbstractWhen designing displays for the human senses, perceptual spaces are of great importance to give intuitive access to physical attributes. Similar to how perceptual spaces based on hue, saturation, and lightness were constructed for visual color, research has explored perceptual spaces for sounds of a given timbral family based on timbre, brightness, and pitch. To promote an embodied approach to the design of auditory displays, we introduce the Vowel–Type–Pitch (VTP) space, a cylindrical sound space based on human sung vowels, whose timbres can be synthesized by the composition of acoustic formants and can be categorically labeled. Vowels are arranged along the circular dimension, whi…
Burrows Wheeler Transform on a Large Scale: Algorithms Implemented in Apache Spark
2021
With the rapid growth of Next Generation Sequencing (NGS) technologies, large amounts of "omics" data are daily collected and need to be processed. Indexing and compressing large sequences datasets are some of the most important tasks in this context. Here we propose algorithms for the computation of Burrows Wheeler transform relying on Big Data technologies, i.e., Apache Spark and Hadoop. Our algorithms are the first ones that distribute the index computation and not only the input dataset, allowing to fully benefit of the available cloud resources.
A Big Data Approach for Sequences Indexing on the Cloud via Burrows Wheeler Transform
2020
Indexing sequence data is important in the context of Precision Medicine, where large amounts of ``omics'' data have to be daily collected and analyzed in order to categorize patients and identify the most effective therapies. Here we propose an algorithm for the computation of Burrows Wheeler transform relying on Big Data technologies, i.e., Apache Spark and Hadoop. Our approach is the first that distributes the index computation and not only the input dataset, allowing to fully benefit of the available cloud resources.