Big Data Analytics for Earth Sciences: the EarthServer approach

Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains.

El profesorado de educación básica y su contribución a la enseñanza de las artes visuales en la escuela.

La construcción de experiencias formativas que repercuten en la asignatura de las artes visuales amplía en el alumnado su comprensión de la realidad, enriquece sus facultades creativas, imaginativas y simbólicas, lo que demanda un mejoramiento e integración de enfoques, métodos y prácticas innovadoras basados en la perspectiva pedagógica que abarcan las distintas disciplinas artísticas. El presente trabajo explora los conocimientos artísticos de un grupo de 10 docentes de Educación Básica en relación a la enseñanza de las bases curriculares de las artes visuales en los niveles de 1º a 6º año en distintas escuelas de la provincia de Bío Bío, al sur de Chile. A través de la metodología de estudio de caso, sobre la base de una entrevista semiestructurada y el análisis de contenido, permitió organizar la información recabada en dimensiones y categorías que evidencian como resultado, las complejidades, diferencias y contribuciones de las Artes Visuales en la formación integral de los niños y niñas del sistema escolar.

Evaluating Document Retrieval Methods for Resource Selection in Clustered P2P IR

Resource Selection (or Query Routing) is an important step in P2P IR. Though analogous to document retrieval in the sense of choosing a relevant subset of resources, resource selection methods have evolved independently from those for document retrieval. Among the reasons for such divergence is that document retrieval targets scenarios where underlying resources are semantically homogeneous, whereas peers would manage diverse content. We observe that semantic heterogeneity is mitigated in the clustered 2-tier P2P IR architecture resource selection layer by way of usage of clustering, and posit that this necessitates a re-look at the applicability of document retrieval methods for resource selection within such a framework. This paper empirically benchmarks document retrieval models against the state-of-the-art resource selection models for the problem of resource selection in the clustered P2P IR architecture, using classical IR evaluation metrics. Our benchmarking study illustrates that document retrieval models significantly outperform other methods for the task of resource selection in the clustered P2P IR architecture. This indicates that clustered P2P IR framework can exploit advancements in document retrieval methods to deliver corresponding improvements in resource selection, indicating potential convergence of these fields for the clustered P2P IR architecture.

Gestengestützte Schema- und Datenexploration

Der Zugang zu Datenbanken über die universelle Abfragesprache SQL stellt für Nicht-Spezialisten eine große Herausforderung dar. Als eine benutzerfreundliche Alternative wurden daher seit den 1970er-Jahren unterschiedliche visuelle Abfragesprachen (Visual Query Languages, kurz VQLs) für klassische PCs erforscht. Ziel der vorliegenden Arbeit ist es, eine generische VQL zu entwickeln und zu erproben, die eine gestenbasierte Exploration von Datenbanken auf Schema- und Instanzdatenebene für mobile Endgeräte, insbesondere Tablets, ermöglicht. Dafür werden verschiedene Darstellungsformen, Abfragestrategien und visuelle Hints für Fremdschlüsselbeziehungen untersucht, die den Benutzer bei der Navigation durch die Daten unterstützen. Im Rahmen einer Anforderungsanalyse erwies sich die Visualisierung der Daten und Beziehungen mittels einer platzsparenden geschachtelten NF2-Darstellung als besonders vorteilhaft. Zur Steuerung der Datenbankexploration wird eine geeignete Gestensprache, bestehend aus Stroke-, Multitouch- und Mid-Air-Gesten, vorgestellt. Das Gesamtkonzept aus Darstellung und Gestensteuerung wurde anhand des im Rahmen dieser Arbeit entwickelten GBXT-Prototyps auf seine reale Umsetzbarkeit hin, als plattformunabhängige Single-Page-Application für verschiedene mobile Endgeräte mittels JavaScript und HTML5/CSS3 untersucht.

Spatial Data Systems for Transportation Planning, 1997

The objective of this research was to develop a methodology for transforming and dynamically segmenting data. Dynamic segmentation enables transportation system attributes and associated data to be stored in separate tables and merged when a specific query requires a particular set of data to be considered. A major benefit of dynamic segmentation is that individual tables can be more easily updated when attributes, performance characteristics, or usage patterns change over time. Applications of a progressive geographic database referencing system in transportation planning are vast. Summaries of system condition and performance can be made, and analyses of specific portions of a road system are facilitated.

High-performance parallel systems for data-intensive computing

Thesis (Ph.D.)--University of Washington, 2016-08

A Provenance-Aware Policy Language (cProvl) and a Data Traceability Model (cProv) for the Cloud

Provenance plays a pivotal in tracing the origin of something and determining how and why something had occurred. With the emergence of the cloud and the benefits it encompasses, there has been a rapid proliferation of services being adopted by commercial and government sectors. However, trust and security concerns for such services are on an unprecedented scale. Currently, these services expose very little internal working to their customers; this can cause accountability and compliance issues especially in the event of a fault or error, customers and providers are left to point finger at each other. Provenance-based traceability provides a mean to address part of this problem by being able to capture and query events occurred in the past to understand how and why it took place. However, due to the complexity of the cloud infrastructure, the current provenance models lack the expressibility required to describe the inner-working of a cloud service. For a complete solution, a provenance-aware policy language is also required for operators and users to define policies for compliance purpose. The current policy standards do not cater for such requirement. To address these issues, in this paper we propose a provenance (traceability) model cProv, and a provenance-aware policy language (cProvl) to capture traceability data, and express policies for validating against the model. For implementation, we have extended the XACML3.0 architecture to support provenance, and provided a translator that converts cProvl policy and request into XACML type.

Fatores determinantes do comportamento infantil na consulta de Medicina Dentária da Unidade de Saúde da Ilha Terceira

O medo e a ansiedade dentária são importantes fatores condicionantes do tratamento dentário. Ao interferirem na condição psicológica do paciente, condicionam o seu comportamento na consulta e a atitude que apresentam em relação aos cuidados de saúde oral. Os pacientes ansiosos, medrosos ou fóbicos adiam a consulta de medicina dentária, evitam os tratamentos e só recorrem ao médico dentista quando surgem os sintomas dolorosos. Este adiar dos procedimentos dentários resultará num agravamento dos problemas de saúde oral, e em maiores necessidades de tratamento, tratamento esse que será mais intensivo, mais invasivo e potencialmente mais traumático, levando a um reforço do medo e da ansiedade dentária já existente. As crianças, pela menor maturidade psico-emocional têm menor capacidade de lidar com as suas emoções perante diversos acontecimentos, nomeadamente, em contexto médico-dentário. Tornam-se assim mais suscetíveis ao desenvolvimento de medo e ansiedade dentária, e exibindo, com alguma frequência, comportamentos negativos na consulta, que dificultam a adequada prestação de cuidados de saúde oral. Existem ainda outros fatores etiológicos predisponentes e desencadeantes de ansiedade dentária na criança e que condicionam o seu comportamento na consulta (idade, género, faixa etária, número de consultas anteriores, entre outros). Objetivo: Neste trabalho pretendeu-se avaliar os fatores determinantes do comportamento infantil na consulta de medicina dentária da Unidade de Saúde da Ilha Terceira, em crianças com idades entre os 4 e os 16 anos. Métodos: Foi realizado um estudo descritivo observacional transversal onde se pretendeu avaliar a ansiedade dentária da criança antes da consulta dentária através da Facial Image Scale (FIS); avaliar a ansiedade dentária dos acompanhantes através da. Corah Dental Anxiety Scale, Revised (DAS-R). e o comportamento das crianças durante o tratamento dentário usando a Escala de Frankl. O estudo decorreu de 30 de Abril a 8 de Maio na Unidade de Saúde da Ilha Terceira, Região Autónoma dos Açores, tendo sido observadas 53 crianças de idades compreendidas entre os 4 e os 16 anos. Resultados: Numa amostra de 53 crianças, verificou-se que 11,3% das crianças apresentavam ansiedade dentária antes da consulta dentária, que os pais eram mais ansiosos que as crianças, 49,1% apresentavam ansiedade dentária e que a percentagem de crianças com um comportamento negativo durante a consulta médico-dentária foi muito baixa, correspondendo a 1,9%. Verificou-se que a ansiedade dentária parental não interfere com a ansiedade dentária da criança, quando comparadas, ao contrário do que alguns estudos sugerem. Não houve relação entre a ansiedade dentária da criança e o género, idade, número de vezes que veio ao médico dentista ou estatuto social. Conclusão: Neste estudo pôde-se concluir que as crianças que frequentaram a consulta de medicina dentária da Unidade de Saúde da Ilha Terceira entre 30 de Abril a 8 de Maio apresentaram baixa prevalência de ansiedade dentária e elevada prevalência de comportamento positivo na consulta. Já os seus pais ou acompanhantes apresentaram uma prevalência de ansiedade dentária parental elevada. Conhecer os fatores que condicionam o comportamento infantil na consulta dentária como a ansiedade dentária da criança e a ansiedade dentária parental e medi-los antes da consulta poderá ajudar a equipa dentária na abordagem comportamental da criança durante os tratamentos dentários. Envolver a comunidade escolar e a população infantil em ações de promoção da saúde oral, promovendo rastreios dos problemas orais nas escolas, e consultas dentárias de acompanhamento logo desde muito jovens, poderá ter um efeito benéfico na diminuição da ansiedade dentária nas crianças e no desenvolvimento de comportamentos positivos nas consultas.

Conceção de um sistema informático de gestão do pessoal do ISCED – Cabinda

Com um aumento significativo dos dados nos setores empresariais, urge a necessidade de criar formas de facilitar o seu tratamento para um ganho do tempo na execução das atividades, garantindo uma maior eficiência dos sistemas e também proporcionando a maximização do lucro às empresas, quando for esse o caso. O sucesso de uma empresa depende bastante dos seus recursos humanos e estes, constituem o seu “instrumento” mais importante. É (ou seria) das pessoas que a empresa define ações estratégicas, constituindo ganhos de diversas naturezas (de conhecimento, de marca, económicas ou financeiras). O objetivo principal desta dissertação é permitir que a instituição universitária ISCED – Cabinda se adapte ao potencial digital pelo recurso às tecnologias de informação e comunicação, no contexto da sua gestão de Recursos Humanos. Em consequência, a instituição beneficiará com a maior facilidade e rapidez do acesso à informação e a facilidade na atualização dos dados do sistema. Uma vez automatizado, o sistema poderá facilitar o acesso aos dados a fim de responder a uma determinada necessidade que possa existir, independentemente de estar prevista ou não (tornando o sistema mais flexível e adaptável a novas realidades). Com uma Base de dados, far-se-á o cadastro dos dados dos funcionários da instituição fazendo assim evoluir a gestão de Recursos Humanos e proporcionando a busca de informação relativa a um determinado funcionário de uma forma fácil, rápida, precisa, fiável e coerente. Recorrendo ao desenvolvimento de uma aplicação móvel baseada na arquitetura cliente-servidor, as operações de consultas (adicionar, modificar, eliminar), ficam facilitadas e permitem ainda uma maior exploração de dados. A apresentação de uma aplicação móvel para a consulta no âmbito da gestão de Recursos Humanos é complementada com uma agenda de trabalhos a serem desenvolvidos para garantir a proposta de um sistema de gestão de Recursos Humanos para o ISCED.

BUILDING EFFICIENT AND COST-EFFECTIVE CLOUD-BASED BIG DATA MANAGEMENT SYSTEMS

In today’s big data world, data is being produced in massive volumes, at great velocity and from a variety of different sources such as mobile devices, sensors, a plethora of small devices hooked to the internet (Internet of Things), social networks, communication networks and many others. Interactive querying and large-scale analytics are being increasingly used to derive value out of this big data. A large portion of this data is being stored and processed in the Cloud due the several advantages provided by the Cloud such as scalability, elasticity, availability, low cost of ownership and the overall economies of scale. There is thus, a growing need for large-scale cloud-based data management systems that can support real-time ingest, storage and processing of large volumes of heterogeneous data. However, in the pay-as-you-go Cloud environment, the cost of analytics can grow linearly with the time and resources required. Reducing the cost of data analytics in the Cloud thus remains a primary challenge. In my dissertation research, I have focused on building efficient and cost-effective cloud-based data management systems for different application domains that are predominant in cloud computing environments. In the first part of my dissertation, I address the problem of reducing the cost of transactional workloads on relational databases to support database-as-a-service in the Cloud. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. I have designed, built and evaluated SWORD, an end-to-end scalable online transaction processing system, that utilizes workload-aware data placement and replication to minimize the number of distributed transactions that incorporates a suite of novel techniques to significantly reduce the overheads incurred both during the initial placement of data, and during query execution at runtime. In the second part of my dissertation, I focus on sampling-based progressive analytics as a means to reduce the cost of data analytics in the relational domain. Sampling has been traditionally used by data scientists to get progressive answers to complex analytical tasks over large volumes of data. Typically, this involves manually extracting samples of increasing data size (progressive samples) for exploratory querying. This provides the data scientists with user control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. I propose a new progressive data-parallel computation framework, NOW!, that provides support for progressive analytics over big data. In particular, NOW! enables progressive relational (SQL) query support in the Cloud using unique progress semantics that allow efficient and deterministic query processing over samples providing meaningful early results and provenance to data scientists. NOW! enables the provision of early results using significantly fewer resources thereby enabling a substantial reduction in the cost incurred during such analytics. Finally, I propose NSCALE, a system for efficient and cost-effective complex analytics on large-scale graph-structured data in the Cloud. The system is based on the key observation that a wide range of complex analysis tasks over graph data require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph; examples include ego network analysis, motif counting in biological networks, finding social circles in social networks, personalized recommendations, link prediction, etc. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high execution overheads. Further, the lack of support for extracting the relevant portions of the graph that are of interest to an analysis task and loading it onto distributed memory leads to poor scalability. NSCALE allows users to write programs at the level of neighborhoods or subgraphs rather than at the level of vertices, and to declaratively specify the subgraphs of interest. It enables the efficient distributed execution of these neighborhood-centric complex analysis tasks over largescale graphs, while minimizing resource consumption and communication cost, thereby substantially reducing the overall cost of graph data analytics in the Cloud. The results of our extensive experimental evaluation of these prototypes with several real-world data sets and applications validate the effectiveness of our techniques which provide orders-of-magnitude reductions in the overheads of distributed data querying and analysis in the Cloud.

Real-time analytics on large dynamic graphs

In today's fast-paced and interconnected digital world, the data generated by an increasing number of applications is being modeled as dynamic graphs. The graph structure encodes relationships among data items, while the structural changes to the graphs as well as the continuous stream of information produced by the entities in these graphs make them dynamic in nature. Examples include social networks where users post status updates, images, videos, etc.; phone call networks where nodes may send text messages or place phone calls; road traffic networks where the traffic behavior of the road segments changes constantly, and so on. There is a tremendous value in storing, managing, and analyzing such dynamic graphs and deriving meaningful insights in real-time. However, a majority of the work in graph analytics assumes a static setting, and there is a lack of systematic study of the various dynamic scenarios, the complexity they impose on the analysis tasks, and the challenges in building efficient systems that can support such tasks at a large scale. In this dissertation, I design a unified streaming graph data management framework, and develop prototype systems to support increasingly complex tasks on dynamic graphs. In the first part, I focus on the management and querying of distributed graph data. I develop a hybrid replication policy that monitors the read-write frequencies of the nodes to decide dynamically what data to replicate, and whether to do eager or lazy replication in order to minimize network communication and support low-latency querying. In the second part, I study parallel execution of continuous neighborhood-driven aggregates, where each node aggregates the information generated in its neighborhoods. I build my system around the notion of an aggregation overlay graph, a pre-compiled data structure that enables sharing of partial aggregates across different queries, and also allows partial pre-computation of the aggregates to minimize the query latencies and increase throughput. Finally, I extend the framework to support continuous detection and analysis of activity-based subgraphs, where subgraphs could be specified using both graph structure as well as activity conditions on the nodes. The query specification tasks in my system are expressed using a set of active structural primitives, which allows the query evaluator to use a set of novel optimization techniques, thereby achieving high throughput. Overall, in this dissertation, I define and investigate a set of novel tasks on dynamic graphs, design scalable optimization techniques, build prototype systems, and show the effectiveness of the proposed techniques through extensive evaluation using large-scale real and synthetic datasets.

Microblogging Temporal Summarization: Filtering Important Twitter Updates for Breaking News

While news stories are an important traditional medium to broadcast and consume news, microblogging has recently emerged as a place where people can dis- cuss, disseminate, collect or report information about news. However, the massive information in the microblogosphere makes it hard for readers to keep up with these real-time updates. This is especially a problem when it comes to breaking news, where people are more eager to know “what is happening”. Therefore, this dis- sertation is intended as an exploratory effort to investigate computational methods to augment human effort when monitoring the development of breaking news on a given topic from a microblog stream by extractively summarizing the updates in a timely manner. More specifically, given an interest in a topic, either entered as a query or presented as an initial news report, a microblog temporal summarization system is proposed to filter microblog posts from a stream with three primary concerns: topical relevance, novelty, and salience. Considering the relatively high arrival rate of microblog streams, a cascade framework consisting of three stages is proposed to progressively reduce quantity of posts. For each step in the cascade, this dissertation studies methods that improve over current baselines. In the relevance filtering stage, query and document expansion techniques are applied to mitigate sparsity and vocabulary mismatch issues. The use of word embedding as a basis for filtering is also explored, using unsupervised and supervised modeling to characterize lexical and semantic similarity. In the novelty filtering stage, several statistical ways of characterizing novelty are investigated and ensemble learning techniques are used to integrate results from these diverse techniques. These results are compared with a baseline clustering approach using both standard and delay-discounted measures. In the salience filtering stage, because of the real-time prediction requirement a method of learning verb phrase usage from past relevant news reports is used in conjunction with some standard measures for characterizing writing quality. Following a Cranfield-like evaluation paradigm, this dissertation includes a se- ries of experiments to evaluate the proposed methods for each step, and for the end- to-end system. New microblog novelty and salience judgments are created, building on existing relevance judgments from the TREC Microblog track. The results point to future research directions at the intersection of social media, computational jour- nalism, information retrieval, automatic summarization, and machine learning.

Abordagem Criminológica do Casamento de Conveniência

Projeto de Graduação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de licenciado em Criminologia

Case-based reasoning as a tool to improve the usability of numerical models

There may be advantages to be gained by combining Case-Based Reasoning (CBR) techniques with numerical models. In this paper we consider how CBR can be used as a flexible query engine to improve the usability of numerical models. Particularly they can help to solve inverse and mixed problems, and to solve constraint problems. We discuss this idea with reference to the illustrative example of a pneumatic conveyor. We describe a model of the problem of particle degradation in such a conveyor, and the problems faced by design engineers. The solution of these problems requires a system that allows iterative sharing of control between user, CBR system, and numerical model. This multi-initiative interaction is illustrated for the pneumatic conveyor by means of Unified Modeling Language (UML) collaboration and sequence diagrams. We show approaches to the solution of these problems via a CBR tool.

Leveraging Multiple Features for Image Retrieval and Matching

The goal of image retrieval and matching is to find and locate object instances in images from a large-scale image database. While visual features are abundant, how to combine them to improve performance by individual features remains a challenging task. In this work, we focus on leveraging multiple features for accurate and efficient image retrieval and matching. We first propose two graph-based approaches to rerank initially retrieved images for generic image retrieval. In the graph, vertices are images while edges are similarities between image pairs. Our first approach employs a mixture Markov model based on a random walk model on multiple graphs to fuse graphs. We introduce a probabilistic model to compute the importance of each feature for graph fusion under a naive Bayesian formulation, which requires statistics of similarities from a manually labeled dataset containing irrelevant images. To reduce human labeling, we further propose a fully unsupervised reranking algorithm based on a submodular objective function that can be efficiently optimized by greedy algorithm. By maximizing an information gain term over the graph, our submodular function favors a subset of database images that are similar to query images and resemble each other. The function also exploits the rank relationships of images from multiple ranked lists obtained by different features. We then study a more well-defined application, person re-identification, where the database contains labeled images of human bodies captured by multiple cameras. Re-identifications from multiple cameras are regarded as related tasks to exploit shared information. We apply a novel multi-task learning algorithm using both low level features and attributes. A low rank attribute embedding is joint learned within the multi-task learning formulation to embed original binary attributes to a continuous attribute space, where incorrect and incomplete attributes are rectified and recovered. To locate objects in images, we design an object detector based on object proposals and deep convolutional neural networks (CNN) in view of the emergence of deep networks. We improve a Fast RCNN framework and investigate two new strategies to detect objects accurately and efficiently: scale-dependent pooling (SDP) and cascaded rejection classifiers (CRC). The SDP improves detection accuracy by exploiting appropriate convolutional features depending on the scale of input object proposals. The CRC effectively utilizes convolutional features and greatly eliminates negative proposals in a cascaded manner, while maintaining a high recall for true objects. The two strategies together improve the detection accuracy and reduce the computational cost.