Hadoop for EEG Storage and Processing: A Feasibility Study

Lots of heterogeneous complex data are collected for diagnosis purposes. Such data should be shared between all caregivers and, often at least partly automatically processed, due to its complexity, for its full potential to be harnessed. This paper is a feasibility study that assesses the potential of Hadoop as a medical data storage and processing platform using EEGs as example of medical data

Data cube computational model with Hadoop MapReduce

XML has become a widely used and well structured data format for digital document handling and message transmission. To find useful knowledge in XML data, data warehouse and OLAP applications aimed at providing supports for decision making should be developed. Apache Hadoop is an open source cloud computing framework that provides a distributed file system for large scale data processing. In this paper, we discuss an XML data cube model which offers us the complete views to observe XML data, and present a basic algorithm to implement its building process on Hadoop. To improve the efficiency, an optimized algorithm more suitable for this kind of XML data is also proposed. The experimental results given in the paper prove the effectiveness of our optimization strategies

Wiki-health: from quantified self to self-understanding

Today, healthcare providers are experiencing explosive growth in data, and medical imaging represents a significant portion of that data. Meanwhile, the pervasive use of mobile phones and the rising adoption of sensing devices, enabling people to collect data independently at any time or place is leading to a torrent of sensor data. The scale and richness of the sensor data currently being collected and analysed is rapidly growing. The key challenges that we will be facing are how to effectively manage and make use of this abundance of easily-generated and diverse health data. This thesis investigates the challenges posed by the explosive growth of available healthcare data and proposes a number of potential solutions to the problem. As a result, a big data service platform, named Wiki-Health, is presented to provide a unified solution for collecting, storing, tagging, retrieving, searching and analysing personal health sensor data. Additionally, it allows users to reuse and remix data, along with analysis results and analysis models, to make health-related knowledge discovery more available to individual users on a massive scale. To tackle the challenge of efficiently managing the high volume and diversity of big data, Wiki-Health introduces a hybrid data storage approach capable of storing structured, semi-structured and unstructured sensor data and sensor metadata separately. A multi-tier cloud storage system—CACSS has been developed and serves as a component for the Wiki-Health platform, allowing it to manage the storage of unstructured data and semi-structured data, such as medical imaging files. CACSS has enabled comprehensive features such as global data de-duplication, performance-awareness and data caching services. The design of such a hybrid approach allows Wiki-Health to potentially handle heterogeneous formats of sensor data. To evaluate the proposed approach, we have developed an ECG-based health monitoring service and a virtual sensing service on top of the Wiki-Health platform. The two services demonstrate the feasibility and potential of using the Wiki-Health framework to enable better utilisation and comprehension of the vast amounts of sensor data available from different sources, and both show significant potential for real-world applications.Open Acces

Digital phenotyping through multimodal, unobtrusive sensing

The growing adoption of multimodal wearable and mobile devices, such as smartphones and wrist-worn watches has generated an increase in the collection of physiological and behavioural data at scale. This digital phenotyping data enables researchers to make inferences regarding users’ physical and mental health at scale, for the first time. However, translating this data into actionable insights requires computational approaches that turn unlabelled, multimodal time-series sensor data into validated measures that can be interpreted at scale. This thesis describes the derivation of novel computational methods that leverage digital phenotyping data from wearable devices in large-scale populations to infer physical behaviours. These methods combine insights from signal processing, data mining and machine learning alongside domain knowledge in physical activity and sleep epidemiology. First, the inference of sleeping windows in free-living conditions through a heart rate sensing approach is explored. This algorithm is particularly valuable in the absence of ground truth or sleep diaries given its simplicity, adaptability and capacity for personalization. I then explore multistage sleep classification through combined movement and cardiac wearable sensing and machine learning. Further, I demonstrate that postural changes detected through wrist accelerometers can inform habitual behaviours and are valuable complements to traditional, intensity-based physical activity metrics. I then leverage the concomitant responses of heart rate to physical activity that can be captured through multimodal wearable sensors through a self-supervised training task. The resulting embeddings from this task are shown to be useful for the downstream classification of demographic factors, BMI, energy expenditure and cardiorespiratory fitness. Finally, I describe a deep learning model for the adaptive inference of cardiorespiratory fitness (VO2max) using wearable data in free living conditions. I demonstrate the robustness of the model in a large UK population and show the models’ adaptability by evaluating its performance in a subset of the population with repeated measures ~6 years after the original recordings. Together, this work increases the potential of multimodal wearable and mobile sensors for physical activity and behavioural inferences in population studies. In particular, this thesis showcases the potential of using wearable devices to make valuable physical activity, sleep and fitness inferences in large cohort studies. Given the nature of the data collected and the fact that most of this data is currently generated by commercial providers and not research institutes, laying the foundations for responsible data governance and ethical use of these technologies will be critical to building trust and enabling the development of the field of digital phenotyping.I was funded by GlaxoSmithKline and the Engineering and Physical Sciences Research Council. I was also supported by the Alan Turing Institute through their Enrichment Scheme

Digital marketing, elements of the public sector competition value chain in Barranquilla, (Colombia)

La organización en la actualidad están obligadas a generar mayores beneficios a sus consumidores para lograr mayor posicionamiento en el mercado, eso depende del manejo de factores de competitividad internos y externos que predominan en las organizaciones medianas en el sector de la publicidad digital en Barranquilla. El objetivo de esta investigación fue describir el marketing digital del sector publicitario. La investigación es descriptiva con diseño no experimental y transversal. La muestra estuvo conformada por 15 empresas, cumpliendo los criterios: Empresa mediana, con departamento de Marketing digital, domiciliada en Barranquilla. Los resultados fueron descripción el marketing digital del sector publicitario, de acuerdo a los factores internos y externos en estas empresas presentan donde existe una consistencia moderada en la dinámica de respuesta de la empresa ante factores externos y viceversa. Se concluyó que las empresas de este sector requieren de estrategias que promuevan el desarrollo de los indicadores internos de competitividad que respondan a los factores cambiantes externo.The organization is currently forced to generate greater benefits to its consumers to achieve greater market positioning, that depends on the management of internal and external competitiveness factors that predominates in medium-sized organizations in the digital advertising sector in Barranquilla. The objective of this research was to describe the digital marketing of the advertising sector. The research is descriptive with non-experimental and transversal design. The sample was composed by 15 companies, fulfilling the criteria: Medium company, with department of Digital Marketing, placed in Barranquilla. The results were a description digital marketing of the advertising sector, according of the internal and external factors in these companies present where there is a moderate consistency in the dynamics of the company’s response to external factors and vice versa. It was concluded that companies in this sector have difficulties in strategies that promote the development of internal competitiveness indicators that respond to changing external factors