Towards Automated Management and Analysis of Heterogeneous Data Within Cannabinoids Domain

Cannabinoid research requires the cooperation of experts from various field biochemistry and chemistry to psychological and social sciences. The data that have to be managed and analysed are highly heterogeneous, especially because they are provided by a very diverse range of sources. A number of approaches focused on data collection and the corresponding analysis, restricting the scope to a sub-domain. Our goal is to elaborate a solution that would allow for automated management and analysis of heterogeneous data within the complete cannabinoids domain. The corresponding integration of diverse data sources would increase the quality and preciseness of the analysis. In this paper, we introduce the core ideas of the proposed framework as well as present the implemented prototype of a cannabinoids data platform.Comment: Preprint. Accepted to the 14th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2019). Final version published by SCITEPRES

Big Data Proteogenomics and High Performance Computing: Challenges and Opportunities

Proteogenomics is an emerging field of systems biology research at the intersection of proteomics and genomics. Two high-throughput technologies, Mass Spectrometry (MS) for proteomics and Next Generation Sequencing (NGS) machines for genomics are required to conduct proteogenomics studies. Independently both MS and NGS technologies are inflicted with data deluge which creates problems of storage, transfer, analysis and visualization. Integrating these big data sets (NGS+MS) for proteogenomics studies compounds all of the associated computational problems. Existing sequential algorithms for these proteogenomics datasets analysis are inadequate for big data and high performance computing (HPC) solutions are almost non-existent. The purpose of this paper is to introduce the big data problem of proteogenomics and the associated challenges in analyzing, storing and transferring these data sets. Further, opportunities for high performance computing research community are identified and possible future directions are discussed

Big Data Proteogenomics and High Performance Computing: Challenges and Opportunities

Proteogenomics is an emerging field of systems biology research at the intersection of proteomics and genomics. Two high-throughput technologies, Mass Spectrometry (MS) for proteomics and Next Generation Sequencing (NGS) machines for genomics are required to conduct proteogenomics studies. Independently both MS and NGS technologies are inflicted with data deluge which creates problems of storage, transfer, analysis and visualization. Integrating these big data sets (NGS+MS) for proteogenomics studies compounds all of the associated computational problems. Existing sequential algorithms for these proteogenomics datasets analysis are inadequate for big data and high performance computing (HPC) solutions are almost non-existent. The purpose of this paper is to introduce the big data problem of proteogenomics and the associated challenges in analyzing, storing and transferring these data sets. Further, opportunities for high performance computing research community are identified and possible future directions are discussed

Integration of Smart Wearable Devices and Cloud Computing in the Kenyan Public Health Care System

The utilization of smart wearable devices and cloud computing in the Kenyan public health care system will facilitate real-time patient monitoring and management. The shortage of certified healthcare professionals and the limited access to quality specialized care for individuals in remote settings has prompted the adoption of wearable devices and cloud computing strategies in Kenya. However, there lacks a clear framework design of integrating the technologies in the public health sector. This article evaluates the current status of healthcare systems in Kenya. It also investigates the existing mobile health and cloud computing services in the country while evaluating the main legal concerns inherent to the utilization of the technologies. The document further outlines a framework design for a mobile application named GB Health. The application incorporates cloud computing and smart wearable devices in the Kenyan public health care system. The design will enhance workflow and patient outcomes in the sector. Keywords: Smart wearable devices, cloud computing, GB Health DOI: 10.7176/IKM/11-4-04 Publication date:June 30th 2021

BIOKIMIA DI ERA BIG DATA GENOMIK: TANTANGAN, APLIKASI DAN PELUANG INOVASI

The completion of human genome project at beginning of 21st century with the advancement of computer technology has transformed Biochemistry into a genomic era. Further, it is accelerated by parallel and massive genome sequencing technology known as next generation sequencing (NGS) that enhances the identification of genetic variants associated with complex diseases such as cancer, diabetes and Alzheimer. Currently, this knowledge has been driving the development of precision and personalized medicine. Wisely applied, it is believed that the explosion of genomic big data can be of great use in advancing the diagnosis, therapy and drug discovery to combat complex diseases

Plataforma colaborativa, distribuida, escalable y de bajo costo basada en microservicios, contenedores, dispositivos móviles y servicios en la Nube para tareas de cómputo intensivo

A la hora de resolver tareas de cómputo intensivo de manera distribuida y paralela, habitualmente se utilizan recursos de hardware x86 (CPU/GPU) e infraestructura especializada (Grid, Cluster, Nube) para lograr un alto rendimiento. En sus inicios los procesadores, coprocesadores y chips x86 fueron desarrollados para resolver problemas complejos sin tener en cuenta su consumo energético. Dado su impacto directo en los costos y el medio ambiente, optimizar el uso, refrigeración y gasto energético, así como analizar arquitecturas alternativas, se convirtió en una preocupación principal de las organizaciones. Como resultado, las empresas e instituciones han propuesto diferentes arquitecturas para implementar las características de escalabilidad, flexibilidad y concurrencia. Con el objetivo de plantear una arquitectura alternativa a los esquemas tradicionales, en esta tesis se propone ejecutar las tareas de procesamiento reutilizando las capacidades ociosas de los dispositivos móviles. Estos equipos integran procesadores ARM los cuales, en contraposición a las arquitecturas tradicionales x86, fueron desarrollados con la eficiencia energética como pilar fundacional, ya que son mayormente alimentados por baterías. Estos dispositivos, en los últimos años, han incrementado su capacidad, eficiencia, estabilidad, potencia, así como también masividad y mercado; mientras conservan un precio, tamaño y consumo energético reducido. A su vez, cuentan con lapsos de ociosidad durante los períodos de carga, lo que representa un gran potencial que puede ser reutilizado. Para gestionar y explotar adecuadamente estos recursos, y convertirlos en un centro de datos de procesamiento intensivo; se diseñó, desarrolló y evaluó una plataforma distribuida, colaborativa, elástica y de bajo costo basada en una arquitectura compuesta por microservicios y contenedores orquestados con Kubernetes en ambientes de Nube y local, integrada con herramientas, metodologías y prácticas DevOps. El paradigma de microservicios permitió que las funciones desarrolladas sean fragmentadas en pequeños servicios, con responsabilidades acotadas. Las prácticas DevOps permitieron construir procesos automatizados para la ejecución de pruebas, trazabilidad, monitoreo e integración de modificaciones y desarrollo de nuevas versiones de los servicios. Finalmente, empaquetar las funciones con todas sus dependencias y librerías en contenedores ayudó a mantener servicios pequeños, inmutables, portables, seguros y estandarizados que permiten su ejecución independiente de la arquitectura subyacente. Incluir Kubernetes como Orquestador de contenedores, permitió que los servicios se puedan administrar, desplegar y escalar de manera integral y transparente, tanto a nivel local como en la Nube, garantizando un uso eficiente de la infraestructura, gastos y energía. Para validar el rendimiento, escalabilidad, consumo energético y flexibilidad del sistema, se ejecutaron diversos escenarios concurrentes de transcoding de video. De esta manera se pudo probar, por un lado, el comportamiento y rendimiento de diversos dispositivos móviles y x86 bajo diferentes condiciones de estrés. Por otro lado, se pudo mostrar cómo a través de una carga variable de tareas, la arquitectura se ajusta, flexibiliza y escala para dar respuesta a las necesidades de procesamiento. Los resultados experimentales, sobre la base de los diversos escenarios de rendimiento, carga y saturación planteados, muestran que se obtienen mejoras útiles sobre la línea de base de este estudio y que la arquitectura desarrollada es lo suficientemente robusta para considerarse una alternativa escalable, económica y elástica, respecto a los modelos tradicionales.Facultad de Informátic

Holistic Approach Framework for Cloud Computing Strategic Decision-Making in the Healthcare Sector (HAF-CCS)

Cloud Computing is an evolving information technology paradigm that impacts many sectors in many countries. Cloud Computing offers IT services anytime, anywhere via any device and is applicable to healthcare organisations, offering a potential cost saving of 15% to 37%. This research investigates Cloud Computing as a facilitating technology to solve some of the challenges experienced by healthcare organisations such as the high cost of implementing IT solutions. The purpose of this research is to develop and apply an Holistic Approach Framework for Cloud Computing Strategic Decision-Making in the Healthcare Sector (HAF-CCS) to provide a systematic approach to the adoption of Cloud Computing that considers different perspectives. Although, Cloud Computing is becoming widely used, there is limited evidence in the literature concerning its application in the Saudi healthcare sector. In the thesis, current cloud adoption decision-making frameworks are analysed and the need to develop a strategic framework for Cloud Computing decision-making processes which emphasises a multidisciplinary holistic approach is identified. Understanding the different strategic aspects of Cloud Computing is important and could encourage organisations to adopt this model of computing since the decision regarding whether to adopt Cloud Computing is potentially a complex process; there are many perspectives to be considered, and studying this process requires a multiple perspective framework. The framework developed in this thesis aims to support decision-makers in healthcare organisations by covering five perspectives of Cloud Computing adoption: Organisation, Technology, Environment, Human and Business. The framework integrates the TOE (Technology-Organisation-Environment) framework with the Information Systems Strategy Triangle (IS Triangle) and the HOT-fit (Human- Organisation-Technology) model to support an holistic evaluation of the determinants of Cloud Computing adoption in healthcare organisations. The factors that will affect Cloud Computing adoption in healthcare organisations in Saudi Arabia have been identified using quantitative and qualitative methods, and a case study approach was implemented to validate the framework. The results of the validation showed that the framework can support decision-makers in understanding an organisation’s position regarding Cloud Computing and identifying any gaps that may hinder Cloud Computing adoption. The framework can also provide healthcare organisations with a strategic assessment tool to help in gaining the advantages of Cloud Computing