A Dynamic Scaling Methodology for Improving Performance of Big Data Systems

The continuous growth of data volume in various fields such as, healthcare, sciences, economics, and business has caused an overwhelming flow of data in the last decade. The overwhelming flow of data has raised challenges in processing, analyzing, and storing data, which lead many systems to face an issue in performance. Poor performance of systems creates negative impact such as delays, unprocessed data, and increasing response time. Processing huge amounts of data demands a powerful computational infrastructure to ensure that data processing and analysis success [7]. However, the architectures of these systems are not suitable to process that quantity of data. This calls for necessity to develop a methodology to improve the performance of systems handle massive amount of data. This thesis presents a novel dynamic scaling methodology to improve the performance of big data systems. The dynamic scaling methodology is developed to scale up the system based on the several aspects from the big data perspective. Moreover, these aspects are used by the helper project algorithm which is designed to divide a task into small chunks to be processed by the system. These small chunks run on several virtual machines to work in parallel to enhance the system’s runtime performance. In addition, the dynamic scaling methodology does not require many modifications on the applied, which makes it easy to use. The dynamic scaling methodology improves the performance of the big data system significantly. As a result, it provides a solution for performance failures in systems that process huge amount of data. This is study would be beneficial to IT researches that focus on performance of big data systems

Service Integration Design Patterns in Microservices

“Microservices” is a new term in software architecture that was defined in 2014 [1]. It is a method to build a software application with a set of small services. Each service has its process to serve a single purpose and communicates with other services through lightweight mechanisms. Because of a great deal of independently distributed services, it is a challenge to integrate the loose services fully. Too many trivial relationships can be messed up easily during deployment. Also, it is hard to modify the relationships if the services are updated as the source codes need to be re-edited and tested. The microservices architecture is attracting much attention recently. More and more software-developers are producing continuous applications and microservices deliveries [2]. There is a need to develop a mechanism to better integrate the scattered services during the application delivery process. The thesis proposes three general design patterns to integrate services in microservices architecture. These patterns are classified by the inter-service communication mechanisms and described with specific problems, contexts, solutions, example implementations and consequences. Besides, the informative guidelines are provided to make these patterns apply in different applications quickly. The service integration design patterns help compose services and facilitate the process of building applications in microservices. All the patterns are helpful tools to address the service integration issues in microservices. Each approach is simple and flexible to apply generally. The structures can be easily modified through these approaches

Data mining techniques on satellite images for discovery of risk areas

The high rates of cholera epidemic mortality in less developed countries is a challenge for health fa- cilities to which it is necessary to equip itself with the epidemiological surveillance. To strengthen the capacity of epidemiological surveillance, this paper focuses on remote sensing satellite data processing using data mining methods to discover risk areas of the epidemic disease by connecting the environ- ment, climate and health. These satellite data are combined with field data collected during the same set of periods in order to explain and deduct the causes of the epidemic evolution from one period to another in relation to the environment. The existing technical (algorithms) for processing satellite im- ages are mature and efficient, so the challenge today is to provide the most suitable means allowing the best interpretation of obtained results. For that, we focus on supervised classification algorithm to process a set of satellite images from the same area but on different periods. A novel research method- ology (describing pre-treatment, data mining, and post-treatment) is proposed to ensure suitable means for transforming data, generating information and extracting knowledge. This methodology consists of six phases: (1.A) Acquisition of information from the field about epidemic, (1.B) Satellite data acquisition, (2) Selection and transformation of data (Data derived from images), (3) Remote sensing measurements, (4) Discretization of data, (5) Data treatment, and (6) Interpretation of results. The main contributions of the paper are: to establish the nature of links between the environment and the epidemic, and to highlight those risky environments when the public awareness of the problem and the prevention policies are absolutely necessary for mitigation of the propagation and emergence of the epidemic. This will allow national governments, local authorities and the public health officials to effective management according to risk areas. The case study concerns the knowledge discovery in databases related to risk areas of the cholera epidemic in Mopti region, Mali (West Africa). The results generate from data mining association rules indicate that the level of the Niger River in the wintering periods and some societal factors have an impact on the variation of cholera epidemic rate in Mopti town. More the river level is high, at 66% the rate of contamination is high

Emerg Infect Dis

PMC4550154611

Modélisation des informations et extraction des connaissances pour la gestion des crises

L’essor des technologies émergentes de collecte de données offre des opportunités nouvelles pour diverses disciplines scientifiques. L’informatique est appelé à jouer sa partition par le développement de techniques d’analyse intelligente des données pour apporter un certain éclairage dans la résolution de problèmes complexes. Le contenu de ce mémoire de recherche doctorale s’inscrit dans la problématique générale de l’extraction des connaissances à partir de données par les techniques informatiques. Ce travail de thèse s’intéresse dans un premier temps à la problématique de la modélisation des informations pour la gestion de crise nécessitant des prises en charge médicale, à l’aide d’une collaboration des applications informatiques de la télémédecine. Nous avons proposé une méthodologie de gestion d’une crise à distance en trois étapes. Elle est principalement axée sur la collaboration des actes de télémédecine (Téléconsultation, Téléexpertise, Télésurveillance, Téléassistance, et la Régulation médicale), de la phase de transport des victimes à la phase de traitements médicaux dans et/ou entre les structures de santé. Cette méthodologie permet non seulement de mettre à la disposition des gestionnaires de crise un système d'aide à la décision informatisé, mais aussi de minimiser les coûts financiers et réduire le temps de réponse des secours à travers une gestion organisée de la crise. Dans un deuxième temps, nous avons étudié en détail l’extraction de la connaissance à l’aide des techniques de data mining sur les images satellitaires afin de découvrir des zones à risques d’épidémie, dont l’étude de cas a porté sur l’épidémie de choléra dans la région de Mopti, au Mali. Ainsi, une méthodologie de six phases a été présentée en mettant en relation les données collectées sur le terrain et les données satellitaires pour prévenir et surveiller plus efficacement les crises d’épidémie. Les résultats nous indiquent qu’à 66% le taux de contamination est lié au fleuve Niger, en plus de certains facteurs sociétaux comme le jet des ordures en période hivernale. Par conséquent, nous avons pu établir le lien entre l’épidémie et son environnement d’évolution, ce qui permettra aux décideurs de mieux gérer une éventuelle crise d’épidémie. Et enfin, en dernier lieu, pendant une situation de crise d’épidémie, nous nous sommes focalisés sur l’analyse médicale, plus précisément par l’usage des microscopes portables afin de confirmer ou non la présence des agents pathogènes dans les prélèvements des cas suspects. Pour ce faire, nous avons présenté une méthodologie de six phases, basée sur les techniques du deep learning notamment l’une des techniques des réseaux de neurones convolutifs, l’apprentissage par transfert qui tirent parti des systèmes complexes avec des invariants permettant la modélisation et l'analyse efficace de grandes quantités de données. Le principe consiste à entraîner les réseaux de neurones convolutifs à la classification automatique d’images des agents pathogènes. Par exemple dans notre cas d’étude, cette approche a été utilisée pour distinguer une image microscopique contenant le virus de l’épidémie de choléra appelé Vibrio cholerae d’une image microscopique contenant le virus de l’épidémie du paludisme appelé Plasmodium. Ceci nous a permis d’obtenir un taux de réussite de classification de 99%. Par la suite, l’idée est de déployer cette solution de reconnaissance d’images d’agents pathogènes dans les microscopes portables intelligents pour les analyses de routine et applications de diagnostic médical dans la gestion de situations de crise. Ce qui permettra de combler le manque de spécialistes en manipulation microscopique et un gain de temps considérable dans l’analyse des prélèvements avec des mesures précises favorisant l’accomplissement du travail dans de meilleures conditions

Emerging infectious diseases

Emerging Infectious Diseases is providing access to these abstracts on behalf of the ICEID 2012 program committee (www.iceid.org), which performed peer review. Emerging Infectious Diseases has not edited or proofread these materials and is not responsible for inaccuracies or omissions. All information is subject to change. Comments and corrections should be brought to the attention of the authors.Influenza preparedness: lessons learned -- Policy implications and infectious diseases -- Improving preparedness for infectious diseases -- New or rapid diagnostics -- Foodborne and waterborne infections -- Effective and sustainable surveillance platforms -- Healthcare-associated infections -- Molecular epidemiology -- Antimicrobial resistance -- Tropical infections and parasitic diseases -- H1N1 influenza -- Risk Assessment -- Laboratory Support -- Zoonotic and Animal Diseases -- Viral Hepatitis -- E1. Zoonotic and animal diseases -- E2. Vaccine issues -- E3. H1N1 influenza -- E4. Novel surveillance systems -- E5. Antimicrobial resistance -- E6. Late-breakers I -- Antimicrobial resistance -- Influenza preparedness: lessons learned -- Zoonotic and animal diseases -- Improving preparedness for infectious diseases -- Laboratory support -- Early warning systems -- H1N1 influenza -- Policy implications and infectious diseases -- Modeling -- Molecular epidemiology -- Novel surveillance systems -- Tropical infections and parasitic diseases -- Strengthening public health systems -- Immigrant and refugee health -- Foodborne and waterborne infections -- Healthcare-associated infections -- Foodborne and waterborne infections -- New or rapid diagnostics -- Improving global health equity for infectious diseases -- Vulnerable populations -- Novel agents of public health importance -- Influenza preparedness: lessons learned -- Molecular epidemiology -- Zoonotic and animal diseases -- Vaccine-preventable diseases -- Outbreak investigation: lab and epi response -- H1N1 influenza -- laboratory support -- effective and sustainable surveillance platforms -- new vaccines -- vector-borne diseases and climate change -- travelers' health -- J1. Vectorborne diseases and climate change -- J2. Policy implications and infectious diseases -- J3. Influenza preparedness: lessons learned -- J4. Effective and sustainable surveillance platforms -- J5. Outbreak investigation: lab and epi response I -- J6. Late-breakers II -- Strengthening public health systems -- Bacterial/viral coinfections -- H1N1 influenza -- Novel agents of public health importance -- Foodborne and waterborne infections -- New challenges for old vaccines -- Vectorborne diseases and climate change -- Novel surveillance systems -- Geographic information systems (GIS) -- Improving global health equity for infectious diseases -- Vaccine preventable diseases -- Vulnerable populations -- Laboratory support -- Prevention challenges for respiratory diseases -- Zoonotic and animal diseases -- Outbreak investigation: lab and epi response -- Vectorborne diseases and climate change -- Outbreak investigation: lab and epi response -- Laboratory proficiency testing/quality assurance -- Effective and sustainable surveillance platforms -- Sexually transmitted diseases -- H1N1 influenza -- Surveillance of vaccine-preventable diseases -- Foodborne and waterborne infections -- Role of health communication -- Emerging opportunistic infections -- Host and microbial genetics -- Respiratory infections in special populations -- Zoonotic and animal diseases -- Laboratory support -- Antimicrobial resistance -- Vulnerable populations -- Global vaccine initiatives -- Tuberculosis -- Prevention challenges for respiratory diseases -- Infectious causes of chronic diseases -- O1. Outbreak investigation: lab and epi response II -- O2. Prevention challenges for respiratory diseases -- O3. Populations at high risk for infectious diseases -- O4. Foodborne and waterborne infections -- O5. Laboratory support: surveillance and monitoring infections -- O6. Late-breakers IIIAbstracts published in advance of the conference