52 research outputs found

    Utilizing Advanced Network Context to Optimize Software-Defined Networks

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    Legacy network systems and protocols are mostly static and keep state information in silo-style storage, thus making state migration, transformation and re-use difficult. Software-Defined Network (SDN) approaches in unison with Network Function Virtualization (NFV) allow for more flexibility, yet they are currently restricted to a limited set of state migration options. Additionally, existing systems and protocols are mostly tailored to meet the requirements of specific application scenarios. As a result, the protocols cannot easily be adapted to novel application demands, organically growing networks, etc. Impeding the sharing of networking and system state, along with lacking support for dynamic transitions between systems and protocols, severely limits the ability to optimally manage resources and dynamically adapt to a desirable overall configuration. These limitations not only affect the network performance but also hinder the deployment of new and innovative protocols as a hard break is usually not feasible and thus full support for legacy systems is required. On the one hand, we propose a generalized way to collect, store, transform, and share context between systems and protocols in both the legacy Internet as well as NFV/SDN-driven networks. This allows us to share state information between multiple systems and protocols from NFs over BGP routers to protocols on all layers of the network stack. On the other hand, we introduce an architecture for designing modular protocols that are built with transition in mind. We argue that the modular design of systems and protocols can remove the key limitations of today’s monolithic protocols and allow for a more dynamic network management. First, we design and implement a Storage and Transformation Engine for Advanced Net- working context (STEAN) which constitutes a shared context storage, making network state information available to other systems and protocols. Its pivotal feature is the ability to allow for state transformation as well as for persisting state to enable future re-use. Second, we provide a Blueprint for Switching Between Mechanisms that serves as a framework and guideline for developers to standardize and ease the process of designing and implementing systems and protocols that support transitions as a first order principle. By means of experimentation, we show that our architecture covers a diverse set of challenging use cases in legacy systems—such as Wireless Multihop Networks (WMNs)—as well as in NFV/SDN-enabled systems. In particular, we demonstrate the feasibility of our approach by migrating state information between two instances of the PRADS NF in a virtualized Mininet environment, and show that our solution outperforms state of the art frameworks that are specifically built for NF migration. We further demonstrate that a dynamic switch between WMN routing protocols is possible at runtime and that the state information can be reutilized for bootstrapping novel protocol modules, thus minimizing the control overhead

    Uma proposta diferenciada de taxonomia para mecanismos de controle de concorrência de bancos de dados em ambientes sem fio

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    Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Ciência da Computação.O objetivo fundamental do controle de concorrência em banco de dados é assegurar que a execução concorrente de transações não resulte na perda da consistência do banco de dados, ou seja, é necessário assegurar o isolamento das transações. No que diz respeito aos bancos de dados móveis, os mecanismos de controle de concorrência aplicados em bancos de dados tradicionais, ou até mesmo distribuídos, não satisfazem as restrições impostas pelo ambiente de computação móvel, como mobilidade das unidades, as freqüentes desconexões de rede, a baixa largura de banda e a portabilidade. Baseando-se na referida fundamentação, neste trabalho é feito um estudo bibliográfico dos principais modelos de transações móveis, evidenciando suas arquiteturas, modos de processamento, tipos de transações utilizadas, traçando um comparativo de como é feito o suporte das propriedades ACID (Atomicidade, Consistência, Isolamento e Durabilidade) em cada modelo estudado. Com estas informações é feita uma análise dos mecanismos de controle de concorrência utilizados em cada modelo. De acordo com as necessidades dos modelos de transações investigados na literatura, a taxonomia proposta tem como diferencial a apresentação da abordagem híbrida, onde os modelos de transações móveis poderão obter um melhor desempenho utilizando o modo pessimista, quando a conectado ao banco de dados, e otimista quando desconectado

    A Pattern Language for Designing Application-Level Communication Protocols and the Improvement of Computer Science Education through Cloud Computing

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    Networking protocols have been developed throughout time following layered architectures such as the Open Systems Interconnection model and the Internet model. These protocols are grouped in the Internet protocol suite. Most developers do not deal with low-level protocols, instead they design application-level protocols on top of the low-level protocol. Although each application-level protocol is different, there is commonality among them and developers can apply lessons learned from one protocol to the design of new ones. Design patterns can help by gathering and sharing proven and reusable solution to common, reoccurring design problems. The Application-level Communication Protocols Design Patterns language captures this knowledge about application-level protocol design, so developers can create better, more fitting protocols base on these common and well proven solutions. Another aspect of contemporary development technics is the need of distribution of software artifacts. Most of the development companies have started using Cloud Computing services to overcome this need; either public or private clouds are widely used. Future developers need to manage this technology infrastructure, software, and platform as services. These two aspects, communication protocols design and cloud computing represent an opportunity to contribute to the software development community and to the software engineering education curriculum. The Application-level Communication Protocols Design Patterns language aims to help solve communication software design. The use of cloud computing in programming assignments targets on a positive influence on improving the Analysis to Reuse skills of students of computer science careers

    Efficient Online Processing for Advanced Analytics

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    With the advent of emerging technologies and the Internet of Things, the importance of online data analytics has become more pronounced. Businesses and companies are adopting approaches that provide responsive analytics to stay competitive in the global marketplace. Online analytics allow data analysts to promptly react to patterns or to gain preliminary insights from early results that aid in research, decision making, and effective strategy planning. The growth of data-velocity in a variety of domains including, high-frequency trading, social networks, infrastructure monitoring, and advertising require adopting online engines that can efficiently process continuous streams of data. This thesis presents foundations, techniques, and systems' design that extend the state-of-the-art in online query processing to efficiently support relational joins with arbitrary join-predicates (beyond traditional equi-joins); and to support other data models (beyond relational) that target machine learning and graph computations. The thesis is divided into two parts: We first present a brief overview of Squall, our open-source online query processing engine that supports SQL-like queries on top of streams. Then, we focus on extending Squall to support efficient theta-join processing. Scalable distributed join processing requires a partitioning policy that evenly distributes the processing load while minimizing the size of maintained state and duplicated messages. Efficient load-balance demands apriori-statistics which are not available in the online setting. We propose a novel operator that continuously adjusts itself to the data dynamics, through adaptive dataflow routing and state repartitioning. It is also resilient to data-skew, maintains high throughput rates, avoids blocking during state repartitioning, and behaves as a black-box dataflow operator with provable performance guarantees. Our evaluation demonstrates that the proposed operator outperforms the state-of-the-art static partitioning schemes in resource utilization, throughput, and execution time up to 7x. In the second part, we present a novel framework that supports the Incremental View Maintenance (IVM) of workloads expressed as linear algebra programs. Linear algebra represents a concrete substrate for advanced analytical tasks including, machine learning, scientific computation, and graph algorithms. Previous works on relational calculus IVM are not applicable to matrix algebra workloads. This is because a single entry change to an input-matrix results in changes all over the intermediate views, rendering IVM useless in comparison to re-evaluation. We present Lago, a unified modular compiler framework that supports the IVM of a broad class of linear algebra programs. Lago automatically derives and optimizes incremental trigger programs of analytical computations, while freeing the user from erroneous manual derivations, low-level implementation details, and performance tuning. We present a novel technique that captures Δ\Delta changes as low-rank matrices. Low-rank matrices are representable in a compressed factored form that enables cheaper computations. Lago automatically propagates the factored representation across program statements to derive an efficient trigger program. Moreover, Lago extends its support to other domains that use different semi-ring configurations, e.g., graph applications. Our evaluation results demonstrate orders of magnitude (10x-1

    Performance Optimizations and Operator Semantics for Streaming Data Flow Programs

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    Unternehmen sammeln mehr Daten als je zuvor und müssen auf diese Informationen zeitnah reagieren. Relationale Datenbanken eignen sich nicht für die latenzfreie Verarbeitung dieser oft unstrukturierten Daten. Um diesen Anforderungen zu begegnen, haben sich in der Datenbankforschung seit dem Anfang der 2000er Jahre zwei neue Forschungsrichtungen etabliert: skalierbare Verarbeitung unstrukturierter Daten und latenzfreie Datenstromverarbeitung. Skalierbare Verarbeitung unstrukturierter Daten, auch bekannt unter dem Begriff "Big Data"-Verarbeitung, hat in der Industrie schnell Einzug erhalten. Gleichzeitig wurden in der Forschung Systeme zur latenzfreien Datenstromverarbeitung entwickelt, die auf eine verteilte Architektur, Skalierbarkeit und datenparallele Verarbeitung setzen. Obwohl diese Systeme in der Industrie vermehrt zum Einsatz kommen, gibt es immer noch große Herausforderungen im praktischen Einsatz. Diese Dissertation verfolgt zwei Hauptziele: Zuerst wird das Laufzeitverhalten von hochskalierbaren datenparallelen Datenstromverarbeitungssystemen untersucht. Im zweiten Hauptteil wird das "Dual Streaming Model" eingeführt, das eine Semantik zur gleichzeitigen Verarbeitung von Datenströmen und Tabellen beschreibt. Das Ziel unserer Untersuchung ist ein besseres Verständnis über das Laufzeitverhalten dieser Systeme zu erhalten und dieses Wissen zu nutzen um Anfragen automatisch ausreichende Rechenkapazität zuzuweisen. Dazu werden ein Kostenmodell und darauf aufbauende Optimierungsalgorithmen für Datenstromanfragen eingeführt, die Datengruppierung und Datenparallelität einbeziehen. Das vorgestellte Datenstromverarbeitungsmodell beschreibt das Ergebnis eines Operators als kontinuierlichen Strom von Veränderugen auf einer Ergebnistabelle. Dabei behandelt unser Modell die Diskrepanz der physikalischen und logischen Ordnung von Datenelementen inhärent und erreicht damit eine deterministische Semantik und eine minimale Verarbeitungslatenz.Modern companies are able to collect more data and require insights from it faster than ever before. Relational databases do not meet the requirements for processing the often unstructured data sets with reasonable performance. The database research community started to address these trends in the early 2000s. Two new research directions have attracted major interest since: large-scale non-relational data processing as well as low-latency data stream processing. Large-scale non-relational data processing, commonly known as "Big Data" processing, was quickly adopted in the industry. In parallel, low latency data stream processing was mainly driven by the research community developing new systems that embrace a distributed architecture, scalability, and exploits data parallelism. While these systems have gained more and more attention in the industry, there are still major challenges to operate them at large scale. The goal of this dissertation is two-fold: First, to investigate runtime characteristics of large scale data-parallel distributed streaming systems. And second, to propose the "Dual Streaming Model" to express semantics of continuous queries over data streams and tables. Our goal is to improve the understanding of system and query runtime behavior with the aim to provision queries automatically. We introduce a cost model for streaming data flow programs taking into account the two techniques of record batching and data parallelization. Additionally, we introduce optimization algorithms that leverage our model for cost-based query provisioning. The proposed Dual Streaming Model expresses the result of a streaming operator as a stream of successive updates to a result table, inducing a duality between streams and tables. Our model handles the inconsistency of the logical and the physical order of records within a data stream natively, which allows for deterministic semantics as well as low latency query execution

    Sixth Goddard Conference on Mass Storage Systems and Technologies Held in Cooperation with the Fifteenth IEEE Symposium on Mass Storage Systems

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    This document contains copies of those technical papers received in time for publication prior to the Sixth Goddard Conference on Mass Storage Systems and Technologies which is being held in cooperation with the Fifteenth IEEE Symposium on Mass Storage Systems at the University of Maryland-University College Inn and Conference Center March 23-26, 1998. As one of an ongoing series, this Conference continues to provide a forum for discussion of issues relevant to the management of large volumes of data. The Conference encourages all interested organizations to discuss long term mass storage requirements and experiences in fielding solutions. Emphasis is on current and future practical solutions addressing issues in data management, storage systems and media, data acquisition, long term retention of data, and data distribution. This year's discussion topics include architecture, tape optimization, new technology, performance, standards, site reports, vendor solutions. Tutorials will be available on shared file systems, file system backups, data mining, and the dynamics of obsolescence

    Sistemas de bases de dados móveis

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    Nos últimos anos, tem-se vindo a presenciar a inúmeros avanços tecnológicos, tanto ao nível das comunicações sem fios como ao nível da computação portátil. Se por um lado as comunicações sem fios são cada vez mais rápidas e seguras, por outro as estações portáteis são, também, cada vez mais fáceis de transportar, uma vez que têm evidenciado uma diminuição do seu tamanho e peso, mantendo, todavia, alguma capacidade de processamento, armazenamento e autonomia das suas baterias. A par desses avanços tecnológicos têm surgido novos sistemas computacionais, que tentam tirar partido das facilidades fornecidas pela combinação de tais tecnologias. Os Sistemas de Base de Dados Móveis são um desses exemplos. Nestes sistemas, tal como é sabido, existe normalmente um conjunto de estações de trabalho distribuídas, em que algumas delas possuem uma localização fixa e conhecida e outras não. Estas últimas, por se poderem deslocar durante o período de execução das suas tarefas, são designadas de estações móveis. A comunicação entre as várias estações que integram um sistema distribuído pode ser realizada através de ligações com e sem fios. No caso de uma das estações do sistema não conseguir comunicar com uma outra, por motivos de falha do sistema de comunicações ou, simplesmente, por indisponibilidade da segunda estação, esta pode, contudo, continuar a executar as suas tarefas baseadas nos dados que mantém localmente, usufruindo assim da autonomia que o SBDM na qual está integrada lhe confere em termos de dados. Mais tarde, quando estiverem novamente repostas as condições para a comunicação entre as estações, os dados processados poderão ser então validados com outras estações do sistema. A autonomia é apenas uma das muitas vantagens e facilidades que os Sistemas de Base de Dados Móveis colocam à disposição dos seus utilizadores. Mas, como seria de esperar, nem tudo são vantagens. A implementação e Nos últimos anos, tem-se vindo a presenciar a inúmeros avanços tecnológicos, tanto ao nível das comunicações sem fios como ao nível da computação portátil. Se por um lado as comunicações sem fios são cada vez mais rápidas e seguras, por outro as estações portáteis são, também, cada vez mais fáceis de transportar, uma vez que têm evidenciado uma diminuição do seu tamanho e peso, mantendo, todavia, alguma capacidade de processamento, armazenamento e autonomia das suas baterias. A par desses avanços tecnológicos têm surgido novos sistemas computacionais, que tentam tirar partido das facilidades fornecidas pela combinação de tais tecnologias. Os Sistemas de Base de Dados Móveis são um desses exemplos. Nestes sistemas, tal como é sabido, existe normalmente um conjunto de estações de trabalho distribuídas, em que algumas delas possuem uma localização fixa e conhecida e outras não. Estas últimas, por se poderem deslocar durante o período de execução das suas tarefas, são designadas de estações móveis. A comunicação entre as várias estações que integram um sistema distribuído pode ser realizada através de ligações com e sem fios. No caso de uma das estações do sistema não conseguir comunicar com uma outra, por motivos de falha do sistema de comunicações ou, simplesmente, por indisponibilidade da segunda estação, esta pode, contudo, continuar a executar as suas tarefas baseadas nos dados que mantém localmente, usufruindo assim da autonomia que o SBDM na qual está integrada lhe confere em termos de dados. Mais tarde, quando estiverem novamente repostas as condições para a comunicação entre as estações, os dados processados poderão ser então validados com outras estações do sistema. A autonomia é apenas uma das muitas vantagens e facilidades que os Sistemas de Base de Dados Móveis colocam à disposição dos seus utilizadores. Mas, como seria de esperar, nem tudo são vantagens. A implementação eNos últimos anos, tem-se vindo a presenciar a inúmeros avanços tecnológicos, tanto ao nível das comunicações sem fios como ao nível da computação portátil. Se por um lado as comunicações sem fios são cada vez mais rápidas e seguras, por outro as estações portáteis são, também, cada vez mais fáceis de transportar, uma vez que têm evidenciado uma diminuição do seu tamanho e peso, mantendo, todavia, alguma capacidade de processamento, armazenamento e autonomia das suas baterias. A par desses avanços tecnológicos têm surgido novos sistemas computacionais, que tentam tirar partido das facilidades fornecidas pela combinação de tais tecnologias. Os Sistemas de Base de Dados Móveis são um desses exemplos. Nestes sistemas, tal como é sabido, existe normalmente um conjunto de estações de trabalho distribuídas, em que algumas delas possuem uma localização fixa e conhecida e outras não. Estas últimas, por se poderem deslocar durante o período de execução das suas tarefas, são designadas de estações móveis. A comunicação entre as várias estações que integram um sistema distribuído pode ser realizada através de ligações com e sem fios. No caso de uma das estações do sistema não conseguir comunicar com uma outra, por motivos de falha do sistema de comunicações ou, simplesmente, por indisponibilidade da segunda estação, esta pode, contudo, continuar a executar as suas tarefas baseadas nos dados que mantém localmente, usufruindo assim da autonomia que o SBDM na qual está integrada lhe confere em termos de dados. Mais tarde, quando estiverem novamente repostas as condições para a comunicação entre as estações, os dados processados poderão ser então validados com outras estações do sistema. A autonomia é apenas uma das muitas vantagens e facilidades que os Sistemas de Base de Dados Móveis colocam à disposição dos seus utilizadores. Mas, como seria de esperar, nem tudo são vantagens. A implementação e gestão destes sistemas não é nada simples, sendo, na generalidade, bastante mais complexa do que nos Sistemas de Bases de Dados Distribuídos. Questões como estas serviram de estímulo para o desenvolvimento de um trabalho de estudo pormenorizado e fundamentado sobre o domínio dos Sistemas de Bases de Dados Móveis, no qual se abordaria toda a problemática da sua implementação, manutenção e gestão, dando-se particular atenção a questões como os seus aspectos arquitecturais e funcionais, modelos de acesso e replicação de dados, sistemas de transacções, processamento de queries, manutenção de consistência, protecção dos dados e implementações reais. O resultado desse trabalho de estudo está apresentado nesta dissertação de mestrado.In the last few years, a lot of technological advances emerged in wireless communications and mobile computing fields. Wireless communications are becoming faster and safer and portable stations are evidencing a significant decrease on their size and weight, maintaining the same processing and storage capacities, and battery autonomy. As a direct consequence of these technological advances, new computational systems appeared integrating the best characteristics that the combination of these two technologies can provide. Mobile Database Systems are a clear example of such combination. They are characterized to have a set of distributed stations, where some of them have are fixed with a known localization and others are mobile, since they can move during the execution of their tasks. The communication between these stations can be supported by wireless communications. In a Mobile Database Systems, when one station can not communicate with another, due to a system failure or simple because the second one is unavailable, they can continue to execute their tasks based on their local data replicas. This is possible only because they have a high level of autonomy in terms of data provided by the Mobile Database System where they are integrated. Latter, when communications are re-established, the data that was processed locally have to be validated with the other system’s stations. Autonomy is only one of the several advantages that Mobile Database Systems can offer to their users. However, these systems have also some disadvantages. Their implementation and management tasks are not simple. In fact, in most of the cases, they are more complex than conventional Distributed Database Systems. Questions like these stimulated the developing of a detailed and supported study about the domain of Mobile Database Systems, approaching the characteristics, functionalities, and common problems that can occur in the design, implementation, and management of such systems. System architectural and functional features, access and replication data models, transactions systems, query processing, consistency maintenance models, data protection, and real current real-world implementations were some of the topics that were also explored. This thesis presents the result of that work

    Arquitectura, técnicas y modelos para posibilitar la Ciencia de Datos en el Archivo de la Misión Gaia

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    Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 26/05/2017.The massive amounts of data that the world produces every day pose new challenges to modern societies in terms of how to leverage their inherent value. Social networks, instant messaging, video, smart devices and scientific missions are just mere examples of the vast number of sources generating data every second. As the world becomes more and more digitalized, new needs arise for organizing, archiving, sharing, analyzing, visualizing and protecting the ever-increasing data sets, so that we can truly develop into a data-driven economy that reduces inefficiencies and increases sustainability, creating new business opportunities on the way. Traditional approaches for harnessing data are not suitable any more as they lack the means for scaling to the larger volumes in a timely and cost efficient manner. This has somehow changed with the advent of Internet companies like Google and Facebook, which have devised new ways of tackling this issue. However, the variety and complexity of the value chains in the private sector as well as the increasing demands and constraints in which the public one operates, needs an ongoing research that can yield newer strategies for dealing with data, facilitate the integration of providers and consumers of information, and guarantee a smooth and prompt transition when adopting these cutting-edge technological advances. This thesis aims at providing novel architectures and techniques that will help perform this transition towards Big Data in massive scientific archives. It highlights the common pitfalls that must be faced when embracing it and how to overcome them, especially when the data sets, their transformation pipelines and the tools used for the analysis are already present in the organizations. Furthermore, a new perspective for facilitating a smoother transition is laid out. It involves the usage of higher-level and use case specific frameworks and models, which will naturally bridge the gap between the technological and scientific domains. This alternative will effectively widen the possibilities of scientific archives and therefore will contribute to the reduction of the time to science. The research will be applied to the European Space Agency cornerstone mission Gaia, whose final data archive will represent a tremendous discovery potential. It will create the largest and most precise three dimensional chart of our galaxy (the Milky Way), providing unprecedented position, parallax and proper motion measurements for about one billion stars. The successful exploitation of this data archive will depend to a large degree on the ability to offer the proper architecture, i.e. infrastructure and middleware, upon which scientists will be able to do exploration and modeling with this huge data set. In consequence, the approach taken needs to enable data fusion with other scientific archives, as this will produce the synergies leading to an increment in scientific outcome, both in volume and in quality. The set of novel techniques and frameworks presented in this work addresses these issues by contextualizing them with the data products that will be generated in the Gaia mission. All these considerations have led to the foundations of the architecture that will be leveraged by the Science Enabling Applications Work Package. Last but not least, the effectiveness of the proposed solution will be demonstrated through the implementation of some ambitious statistical problems that will require significant computational capabilities, and which will use Gaia-like simulated data (the first Gaia data release has recently taken place on September 14th, 2016). These ambitious problems will be referred to as the Grand Challenge, a somewhat grandiloquent name that consists in inferring a set of parameters from a probabilistic point of view for the Initial Mass Function (IMF) and Star Formation Rate (SFR) of a given set of stars (with a huge sample size), from noisy estimates of their masses and ages respectively. This will be achieved by using Hierarchical Bayesian Modeling (HBM). In principle, the HBM can incorporate stellar evolution models to infer the IMF and SFR directly, but in this first step presented in this thesis, we will start with a somewhat less ambitious goal: inferring the PDMF and PDAD. Moreover, the performance and scalability analyses carried out will also prove the suitability of the models for the large amounts of data that will be available in the Gaia data archive.Las grandes cantidades de datos que se producen en el mundo diariamente plantean nuevos retos a la sociedad en términos de cómo extraer su valor inherente. Las redes sociales, mensajería instantánea, los dispositivos inteligentes y las misiones científicas son meros ejemplos del gran número de fuentes generando datos en cada momento. Al mismo tiempo que el mundo se digitaliza cada vez más, aparecen nuevas necesidades para organizar, archivar, compartir, analizar, visualizar y proteger la creciente cantidad de datos, para que podamos desarrollar economías basadas en datos e información que sean capaces de reducir las ineficiencias e incrementar la sostenibilidad, creando nuevas oportunidades de negocio por el camino. La forma en la que se han manejado los datos tradicionalmente no es la adecuada hoy en día, ya que carece de los medios para escalar a los volúmenes más grandes de datos de una forma oportuna y eficiente. Esto ha cambiado de alguna manera con la llegada de compañías que operan en Internet como Google o Facebook, ya que han concebido nuevas aproximaciones para abordar el problema. Sin embargo, la variedad y complejidad de las cadenas de valor en el sector privado y las crecientes demandas y limitaciones en las que el sector público opera, necesitan una investigación continua en la materia que pueda proporcionar nuevas estrategias para procesar las enormes cantidades de datos, facilitar la integración de productores y consumidores de información, y garantizar una transición rápida y fluida a la hora de adoptar estos avances tecnológicos innovadores. Esta tesis tiene como objetivo proporcionar nuevas arquitecturas y técnicas que ayudarán a realizar esta transición hacia Big Data en archivos científicos masivos. La investigación destaca los escollos principales a encarar cuando se adoptan estas nuevas tecnologías y cómo afrontarlos, principalmente cuando los datos y las herramientas de transformación utilizadas en el análisis existen en la organización. Además, se exponen nuevas medidas para facilitar una transición más fluida. Éstas incluyen la utilización de software de alto nivel y específico al caso de uso en cuestión, que haga de puente entre el dominio científico y tecnológico. Esta alternativa ampliará de una forma efectiva las posibilidades de los archivos científicos y por tanto contribuirá a la reducción del tiempo necesario para generar resultados científicos a partir de los datos recogidos en las misiones de astronomía espacial y planetaria. La investigación se aplicará a la misión de la Agencia Espacial Europea (ESA) Gaia, cuyo archivo final de datos presentará un gran potencial para el descubrimiento y hallazgo desde el punto de vista científico. La misión creará el catálogo en tres dimensiones más grande y preciso de nuestra galaxia (la Vía Láctea), proporcionando medidas sin precedente acerca del posicionamiento, paralaje y movimiento propio de alrededor de mil millones de estrellas. Las oportunidades para la explotación exitosa de este archivo de datos dependerán en gran medida de la capacidad de ofrecer la arquitectura adecuada, es decir infraestructura y servicios, sobre la cual los científicos puedan realizar la exploración y modelado con esta inmensa cantidad de datos. Por tanto, la estrategia a realizar debe ser capaz de combinar los datos con otros archivos científicos, ya que esto producirá sinergias que contribuirán a un incremento en la ciencia producida, tanto en volumen como en calidad de la misma. El conjunto de técnicas e infraestructuras innovadoras presentadas en este trabajo aborda estos problemas, contextualizándolos con los productos de datos que se generarán en la misión Gaia. Todas estas consideraciones han conducido a los fundamentos de la arquitectura que se utilizará en el paquete de trabajo de aplicaciones que posibilitarán la ciencia en el archivo de la misión Gaia (Science Enabling Applications). Por último, la eficacia de la solución propuesta se demostrará a través de la implementación de dos problemas estadísticos que requerirán cantidades significativas de cómputo, y que usarán datos simulados en el mismo formato en el que se producirán en el archivo de la misión Gaia (la primera versión de datos recogidos por la misión está disponible desde el día 14 de Septiembre de 2016). Estos ambiciosos problemas representan el Gran Reto (Grand Challenge), un nombre grandilocuente que consiste en inferir una serie de parámetros desde un punto de vista probabilístico para la función de masa inicial (Initial Mass Function) y la tasa de formación estelar (Star Formation Rate) dado un conjunto de estrellas (con una muestra grande), desde estimaciones con ruido de sus masas y edades respectivamente. Esto se abordará utilizando modelos jerárquicos bayesianos (Hierarchical Bayesian Modeling). Enprincipio,losmodelospropuestos pueden incorporar otros modelos de evolución estelar para inferir directamente la función de masa inicial y la tasa de formación estelar, pero en este primer paso presentado en esta tesis, empezaremos con un objetivo algo menos ambicioso: la inferencia de la función de masa y distribución de edades actual (Present-Day Mass Function y Present-Day Age Distribution respectivamente). Además, se llevará a cabo el análisis de rendimiento y escalabilidad para probar la idoneidad de la implementación de dichos modelos dadas las enormes cantidades de datos que estarán disponibles en el archivo de la misión Gaia...Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu

    Emerging Technologies

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    This monograph investigates a multitude of emerging technologies including 3D printing, 5G, blockchain, and many more to assess their potential for use to further humanity’s shared goal of sustainable development. Through case studies detailing how these technologies are already being used at companies worldwide, author Sinan Küfeoğlu explores how emerging technologies can be used to enhance progress toward each of the seventeen United Nations Sustainable Development Goals and to guarantee economic growth even in the face of challenges such as climate change. To assemble this book, the author explored the business models of 650 companies in order to demonstrate how innovations can be converted into value to support sustainable development. To ensure practical application, only technologies currently on the market and in use actual companies were investigated. This volume will be of great use to academics, policymakers, innovators at the forefront of green business, and anyone else who is interested in novel and innovative business models and how they could help to achieve the Sustainable Development Goals. This is an open access book
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