942 resultados para crystallization ontology


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Sensor networks are increasingly becoming one of the main sources of Big Data on the Web. However, the observations that they produce are made available with heterogeneous schemas, vocabularies and data formats, making it difficult to share and reuse these data for other purposes than those for which they were originally set up. In this thesis we address these challenges, considering how we can transform streaming raw data to rich ontology-based information that is accessible through continuous queries for streaming data. Our main contribution is an ontology-based approach for providing data access and query capabilities to streaming data sources, allowing users to express their needs at a conceptual level, independent of implementation and language-specific details. We introduce novel query rewriting and data translation techniques that rely on mapping definitions relating streaming data models to ontological concepts. Specific contributions include: • The syntax and semantics of the SPARQLStream query language for ontologybased data access, and a query rewriting approach for transforming SPARQLStream queries into streaming algebra expressions. • The design of an ontology-based streaming data access engine that can internally reuse an existing data stream engine, complex event processor or sensor middleware, using R2RML mappings for defining relationships between streaming data models and ontology concepts. Concerning the sensor metadata of such streaming data sources, we have investigated how we can use raw measurements to characterize streaming data, producing enriched data descriptions in terms of ontological models. Our specific contributions are: • A representation of sensor data time series that captures gradient information that is useful to characterize types of sensor data. • A method for classifying sensor data time series and determining the type of data, using data mining techniques, and a method for extracting semantic sensor metadata features from the time series.

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The development of crystalline lamellae in ultra-thin layers of poly(ethylene terephthalate) PET confined between polycarbonate (PC) layers in an alternating assembly is investigated as a function of layer thickness by means of X-ray diffraction methods. Isothermal crystallization from the glassy state is in-situ followed by means of small-angle X-ray diffraction. It is found that the reduced size of the PET layers influences the lamellar nanostructure and induces a preferential lamellar orientation. Two lamellar populations, flat-on and edge-on, are found to coexist in a wide range of crystallization temperatures (Tc = 117–150 °C) and within layer thicknesses down to 35 nm. Flat-on lamellae appear at a reduced crystallization rate with respect to bulk PET giving rise to crystals of similar dimensions separated by larger amorphous regions. In addition, a narrower distribution of lamellar orientations develops when the layer thickness is reduced or the crystallization temperature is raised. In case of edge-on lamellae, crystallization conditions also influence the development of lamellar orientation; however, the latter is little affected by the reduced size of the layers. Results suggest that flat-on lamellae arise as a consequence of spatial confinement and edge-on lamellae could be generated due to the interactions with the PC interface. En este trabajo se investiga mediante difracción de rayos X a ángulos bajos (SAXS) y a ángulos altos (WAXS), la cristalización de láminas delgadas de Polietilén tereftalato (PET) confinadas entre láminas de Policarbonato (PC), tomando como referencia PET sin confinar. El espesor de las capas de PET varía entre 35nm y 115 nm. Se realizaron medidas de difracción a tres temperaturas de cristalización (117ºC, 132ºC y 150ºC) encontrándose que el reducido espesor de las capas de PET influye en la estructura lamelar que se desarrolla, induciendo una orientación preferente de las láminas. Se integró la intensidad difractada alrededor del máximo en SAXS para obtener una representación de la intensidad en función del ángulo acimutal. Mediante análisis de mínimos cuadrados se separó la curva experimental obtenida en tres contribuciones diferentes: una función Gausiana que describe la distribución de las orientaciones de las lamelas, una función lorenziana asociada a los máximos meridionales (asociados a las interfases PET-PC) y un background constante. Por otra parte la cantidad de material cristalizado se estimó asumiendo que la intensidad del background en el barrido acimutal, una vez restado el background del primer difractograma (sin máximos en SAXS) se asocia con la contribución del material isotrópico que resta en la muestra cristalizada. Se observa la coexistencia de dos poblaciones de lamelas: flat-on y edge-on. A medida que el espesor de las láminas de PET disminuye la población de las lamelas flat-on experimenta los siguientes cambios: 1) la distribución de orientación se estrecha, 2) la fracción de material cristalizado orientado aumenta, 3) la cinética de cristalización se ralentiza y 4) el largo espaciado aumenta es decir las regiones amorfas entre lamelas aumentan su tamaño. Parece demostrarse que es en las primeras etapas del crecimiento lamelar cuando la restricción espacial fuerza a las lamelas a esta orientación tipo flat-on frente a la orientación edge-on.

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The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL 2 ontology to describe sensors and observations ? the SSN ontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects.

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The LifeWear-Mobilized Lifestyle with Wearables (Lifewear) project attempts to create Ambient Intelligence (AmI) ecosystems by composing personalized services based on the user information, environmental conditions and reasoning outputs. Two of the most important benefits over traditional environments are 1) take advantage of wearable devices to get user information in a nonintrusive way and 2) integrate this information with other intelligent services and environmental sensors. This paper proposes a new ontology composed by the integration of users and services information, for semantically representing this information. Using an Enterprise Service Bus, this ontology is integrated in a semantic middleware to provide context-aware personalized and semantically annotated services, with discovery, composition and orchestration tasks. We show how these services support a real scenario proposed in the Lifewear project.

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In this position paper, we claim that the need for time consuming data preparation and result interpretation tasks in knowledge discovery, as well as for costly expert consultation and consensus building activities required for ontology building can be reduced through exploiting the interplay of data mining and ontology engineering. The aim is to obtain in a semi-automatic way new knowledge from distributed data sources that can be used for inference and reasoning, as well as to guide the extraction of further knowledge from these data sources. The proposed approach is based on the creation of a novel knowledge discovery method relying on the combination, through an iterative ?feedbackloop?, of (a) data mining techniques to make emerge implicit models from data and (b) pattern-based ontology engineering to capture these models in reusable, conceptual and inferable artefacts.

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Verifying whether an ontology meets the set of established requirements is a crucial activity in ontology engineering. In this sense, methods and tools are needed (a) to transform (semi-)automatically functional ontology requirements into SPARQL queries, which can serve as unit tests to verify the ontology, and (b) to check whether the ontology fulfils the requirements. Thus, our purpose in this poster paper is to apply the SWIP approach to verify whether an ontology satisfies the set of established requirements.

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The application of methodologies for building ontologies can improve ontology quality. However, such quality is not guaranteed because of the difficulties involved in ontology modelling. These difficulties are related to the inclusion of anomalies or bad practices within the ontology development. In this context, our aim is to describe OOPS!(OntOlogy Pitfall Scanner!), a tool for detecting pitfalls in ontologies.

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Autonomous systems refer to systems capable of operating in a real world environment without any form of external control for extended periods of time. Autonomy is a desired goal for every system as it improves its performance, safety and profit. Ontologies are a way to conceptualize the knowledge of a specific domain. In this paper an ontology for the description of autonomous systems as well as for its development (engineering) is presented and applied to a process. This ontology is intended to be applied and used to generate final applications following a model driven methodology.

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This paper presents an Ontology-Based multi-technology platform as part of an open energy management system which also comprises a wireless transducer network for control and monitoring. The platform allows the integration of several building automation protocols, eases the development and implementation of different kinds of services and allows sharing of the data of a building. The system has been implemented and tested in the Energy Efficiency Research Facility at CeDInt-UPM.

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In the context of the Semantic Web, resources on the net can be enriched by well-defined, machine-understandable metadata describing their associated conceptual meaning. These metadata consisting of natural language descriptions of concepts are the focus of the activity we describe in this chapter, namely, ontology localization. In the framework of the NeOn Methodology, ontology localization is defined as the activity of adapting an ontology to a particular language and culture. This adaptation mainly involves the translation of the natural language descriptions of the ontology from a source natural language to a target natural language, with the final objective of obtaining a multilingual ontology, that is, an ontology documented in several natural languages. The purpose of this chapter is to provide detailed and prescriptive methodological guidelines to support the performance of this activity.

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Interoperability on multiple levels, concerning both the ontologies themselves and their engineering activities, is a key requirement for ontology networks to be efficient, with minimal redundancy and high reuse. This requirement has a strict binding for software tools that can support some interoperability levels, yet they can be hindered by a lack of shared models and vocabularies describing the resources to be handled, as well as the ways of handling them. Here, three examples of metalevel vocabularies are proposed, each covering at least one peculiar interoperability aspect: OMV for modeling the artifacts themselves, LIR for managing a multilingual layer on top of them, and C-ODO Light for modeling collaboration-supportive life cycle management tasks and processes. All of these models lend themselves to handling by dedicated software tools and are all being employed within NeOn products.

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While ontology engineering is rapidly entering the mainstream, expert ontology engineers are a scarce resource. Hence, there is a need for practical methodologies and technologies, which can assist a variety of user types with ontology development tasks. To address this need, this book presents a scenario-based methodology, the NeOn Methodology, which provides guidance for all main activities in ontology engineering. The context in which we consider these activities is that of a networked world, where reuse of existing resources is commonplace, ontologies are developed collaboratively, and managing relationships between ontologies becomes an essential aspect of the ontological engineering process. The description of both the methodology and the ontology engineering activities is grounded in a comprehensive software environment, the NeOn Toolkit and its plugins, which provides integrated support for all the activities described in the book. Here we provide an introduction for the whole book, while the rest of the content is organized into 4 parts: (1) the NeOn Methodology Framework, (2) the set of ontology engineering activities, (3) the NeOn Toolkit and plugins, and (4) three use cases. Primary goals of this book are (a) to disseminate the results from the NeOn project in a structured and comprehensive form, (b) to make it easier for students and practitioners to adopt ontology engineering methods and tools, and (c) to provide a textbook for undergraduate and postgraduate courses on ontology engineering.

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One of the major problems related to cancer treatment is its recurrence. Without knowing in advance how likely the cancer will relapse, clinical practice usually recommends adjuvant treatments that have strong side effects. A way to optimize treatments is to predict the recurrence probability by analyzing a set of bio-markers. The NeoMark European project has identified a set of preliminary bio-markers for the case of oral cancer by collecting a large series of data from genomic, imaging, and clinical evidence. This heterogeneous set of data needs a proper representation in order to be stored, computed, and communicated efficiently. Ontologies are often considered the proper mean to integrate biomedical data, for their high level of formality and for the need of interoperable, universally accepted models. This paper presents the NeoMark system and how an ontology has been designed to integrate all its heterogeneous data. The system has been validated in a pilot in which data will populate the ontology and will be made public for further research.

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This chapter presents methodological guidelines that allow engineers to reuse generic ontologies. This kind of ontologies represents notions generic across many fields, (is part of, temporal interval, etc.). The guidelines helps the developer (a) to identify the type of generic ontology to be reused, (b) to find out the axioms and definitions that should be reused and (c) to adapt and integrate the generic ontology selected in the domain ontology to be developed. For each task of the methodology, a set of heuristics with examples are presented. We hope that after reading this chapter, you would have acquired some basic ideas on how to take advantage of the great deal of well-founded explicit knowledge that formalizes generic notions such as time concepts and the part of relation.

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The goal of the ontology requirements specification activity is to state why the ontology is being built, what its intended uses are, who the end users are, and which requirements the ontology should fulfill. This chapter presents detailed methodological guidelines for specifying ontology requirements efficiently. These guidelines will help ontology engineers to capture ontology requirements and produce the ontology requirements specification document (ORSD). The ORSD will play a key role during the ontology development process because it facilitates, among other activities, (1) the search and reuse of existing knowledge resources with the aim of reengineering them into ontologies, (2) the search and reuse of ontological resources (ontologies, ontology modules, ontology statements as well as ontology design patterns), and (3) the verification of the ontology along the ontology development.