The diploma thesis deals with automated measurement of humidity and CO2 concentration inside buildings. Results of this measurement form the input parameters for the effective management of technologies reducing the energy performance of buildings. In the introduction, the issue of indoor air quality of buildings and indicators characterizing this quality are approached. The technical part of the thesis consists of making a measuring device which contains two prototype sensors provided by Teco Inc. with online access to measured data. The measurement of relative humidity, CO2 concentration and temperature in the interior of the building with the made device is compared with commercially available devices for measuring selected parameters. For unambiguous interpretation of online data, the virtual machine with an online database is configured for the created measuring device. The possibilities of using the prepared measuring device to achieve a reduction in the energy performance of buildings are discussed in the final part.

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The emergence of the exascale era necessitates development of new techniques to efficiently perform high-performance scientific simulations, online data analysis and on-the-fly visualization. Critical applications like cyclone tracking and earthquake modeling require high-fidelity and high- performance simulations involving large-scale computations and generate huge amounts of data. Faster simulations and simultaneous online data analysis and visualization enable scientists provide real-time guidance to policy makers. In this thesis, we present a set of techniques for efficient high-fidelity simulations, online data analysis and visualization in environments with varying resource configurations. First, we present a strategy for improving throughput of weather simulations with multiple regions of interest. We propose parallel execution of these nested simulations based on partitioning the 2D process grid into disjoint rectangular regions associated with each subdomain. The process grid partitioning is obtained from a Huffman tree which is constructed from the relative execution times of the subdomains. We propose a novel combination of performance prediction, processor allocation methods and topology-aware mapping of the regions on torus interconnects. We observe up to 33% gain over the default strategy in weather models. Second, we propose a processor reallocation heuristic that minimizes data redistribution cost while reallocating processors in the case of dynamic regions of interest. This algorithm is based on hierarchical diffusion approach that uses a novel tree reorganization strategy. We have also developed a parallel data analysis algorithm to detect regions of interest within a domain. This helps improve performance of detailed simulations of multiple weather phenomena like depressions and clouds, thereby in- creasing the lead time to severe weather phenomena like tornadoes and storm surges. Our method is able to reduce the redistribution time by 25% over a simple partition from scratch method. We also show that it is important to consider resource constraints like I/O bandwidth, disk space and network bandwidth for continuous simulation and smooth visualization. High simulation rates on modern-day processors combined with high I/O bandwidth can lead to rapid accumulation of data at the simulation site and eventual stalling of simulations. We show that formulating the problem as an optimization problem can deter- mine optimal execution parameters for enabling smooth simulation and visualization. This approach proves beneficial for resource-constrained environments, whereas a naive greedy strategy leads to stalling and disk overflow. Our optimization method provides about 30% higher simulation rate and consumes about 25-50% lesser storage space than a naive greedy approach. We have then developed an integrated adaptive steering framework, InSt, that analyzes the combined e ect of user-driven steering with automatic tuning of application parameters based on resource constraints and the criticality needs of the application to determine the final parameters for the simulations. It is important to allow the climate scientists to steer the ongoing simulation, specially in the case of critical applications. InSt takes into account both the steering inputs of the scientists and the criticality needs of the application. Finally, we have developed algorithms to minimize the lag between the time when the simulation produces an output frame and the time when the frame is visualized. It is important to reduce the lag so that the scientists can get on-the- y view of the simulation, and concurrently visualize important events in the simulation. We present most-recent, auto-clustering and adaptive algorithms for reducing lag. The lag-reduction algorithms adapt to the available resource parameters and the number of pending frames to be sent to the visualization site by transferring a representative subset of frames. Our adaptive algorithm reduces lag by 72% and provides 37% larger representativeness than the most-recent for slow networks.

The big data framework applied to smart meters offers an exception platform for data-driven forecasting and decision making to achieve sustainable energy efficiency. Buying-in consumer confidence through respecting occupants' energy consumption behavior and preferences towards improved participation in various energy programs is imperative but difficult to obtain. The key elements for understanding and predicting household energy consumption are activities occupants perform, appliances and the times that appliances are used, and inter-appliance dependencies. This information can be extracted from the context rich big data from smart meters, although this is challenging because: (1) it is not trivial to mine complex interdependencies between appliances from multiple concurrent data streams; (2) it is difficult to derive accurate relationships between interval based events, where multiple appliance usage persist; (3) continuous generation of the energy consumption data can trigger changes in appliance associations with time and appliances. To overcome these challenges, we propose an unsupervised progressive incremental data mining technique using frequent pattern mining (appliance-appliance associations) and cluster analysis (appliance-time associations) coupled with a Bayesian network based prediction model. The proposed technique addresses the need to analyze temporal energy consumption patterns at the appliance level, which directly reflect consumers' behaviors and provide a basis for generalizing household energy models. Extensive experiments were performed on the model with real-world datasets and strong associations were discovered. The accuracy of the proposed model for predicting multiple appliances usage outperformed support vector machine during every stage while attaining accuracy of 81.65\%, 85.90\%, 89.58\% for 25\%, 50\% and 75\% of the training dataset size respectively. Moreover, accuracy results of 81.89\%, 75.88\%, 79.23\%, 74.74\%, and 72.81\% were obtained for short-term (hours), and long-term (day, week, month, and season) energy consumption forecasts, respectively.

The thesis evaluates the current possibilities and problems of drone control and suggests possible solutions. The aim is to control drones more efficiently and easily. The final system is based on third person view and Augmented Virtuality technology where real data from the drone (video-stream, localization information) has been integrated into the virtual 3D model of the surroundings. The model of the surroundings has been created using free data. The application provides the pilot with the means to navigate in the surroundings and to navigate to destinations. It also offers the possibility to define areas with various potential security risks during mission planning, which will be used to navigate in the mission zones, and to visualize the overall situation in the virtual scene extended with online real data.

[ES] Los avances tecnológicos de los últimos años han permitido la aparición de nuevas fuentes de datos y con ello, el almacenamiento de grandes cantidades de datos o `Big Data' se ha cobrado cada vez mayor importancia. Cada vez más y más estudios científicos utilizan estas fuentes de `Big Data' para tratar de mejorar el entendimiento en diversos campos científicos. En la economía del turismo ya se han utilizado muchas de estas fuentes para predecir el comportamiento de variables reales. En turismo, la utilidad de estas nuevas fuentes de datos reside en que pueden ayudar a entender el comportamiento de los turistas, desde sus patrones espaciotemporales hasta qué atracciones y actividades son las más populares en el destino, y por tanto, pueden ayudar en la toma de decisiones de los agentes económicos. Por tanto, esta tesis intenta entender mejor cuáles son las fuentes de Big Data que resultan más útiles a la hora de lidiar con variables turísticas y además proponer mejoras metodológicas para que dichas fuentes se puedan aplicar al campo del turismo rural, y más concretamente, a la predicción de turistas. En esta tesis se presentan varios avances en este aspecto: Primero, una clasificación de fuentes de datos que genera todo turista durante su proceso turístico y que componen su huella digital. Después, respecto a esta clasificación, se escoge Google Trends como la fuente más adecuada para ayudar a predecir la demanda turística, pero se encuentran problemas de precisión, que son demostrados y ejemplificados. Más adelante, se demuestra cómo se genera este error de precisión a través del proceso de muestreo de GT y se proponen soluciones para aliviar este error, a saber, obteniendo más extracciones y utilizando su media. Finalmente, este método se pone a prueba para la predicción de pernoctaciones mensuales en alojamientos de turismo rural en España. En resumen, la contribución que esta tesis pretende hacer es aportar una mayor comprensión de las fuentes de Big Data y ayudar a generar buenas prácticas en el uso de las mismas para que se puedan aplicar a la predicción de variables reales en el turismo rural, de forma que agilice y mejore la toma de decisiones de los agentes económicos. / [CA] Els avanços tecnològics dels últims anys han permés l'aparició de noves fonts de dades i amb això, l'emmagatzematge de grans quantitats de dades o `Big Data' s'ha cobrat cada vegada major importància. Cada vegada més i més estudis científics utilitzen aquestes fonts de `Big Data' per a tractar de millorar l'enteniment en diversos camps científics. En l'economia del turisme ja s'han utilitzat moltes d'aquestes fonts per a predir el comportament de variables reals. En turisme, la utilitat d'aquestes noves fonts de dades resideix en què poden ajudar a entendre el comportament dels turistes, des dels seus patrons espaciotemporals fins a quines atraccions i activitats són les més populars en el destí, i per tant, poden ajudar en la presa de decisions dels agents econòmics. Per tant, aquesta tesi intenta entendre millor quines són les fonts de Big Data que resulten més útils a l'hora de bregar amb variables turístiques i a més proposar millores metodològiques perquè aquestes fonts es puguen aplicar al camp del turisme rural, i més concretament, a la predicció de turistes. En aquesta tesi es presenten diversos avanços en aquest aspecte: Primer, una classificació de fonts de dades que genera tot turista durant el seu procés turístic i que componen la seua empremta digital. Després, respecte a aquesta classificació, es tria Google Trends com la font més adequada per a ajudar a predir la demanda turística, però es troben problemes de precisió, que són demostrats i exemplificats. Més endavant, es demostra com es genera aquest error de precisió a través del procés de mostreig de GT i es proposen solucions per a alleujar aquest error, a saber, obtenint més extraccions i utilitzant la seua mitjana. Finalment, aquest mètode es posa a prova per a la predicció de pernoctacions mensuals en allotjaments de turisme rural a Espanya. En resum, la contribució que aquesta tesi pretén fer, és aportar una major comprensió de les fonts de Big Data i ajudar a generar bones pràctiques en l'ús de les mateixes perquè es puguen aplicar a la predicció de variables reals en el turisme rural, de manera que agilitze i millore la presa de decisions dels agents econòmics. / [EN] Technological advances in recent years have enabled the emergence of new data sources and with it, the storage of large amounts of data or 'Big Data' has become increasingly important. More and more scientific studies are using these Big Data sources to try to improve understanding in various scientific fields. In tourism economics, many of these sources have already been used to predict the behavior of real variables. In tourism, the usefulness of these new data sources lies in the fact that they can help to understand the behavior of tourists, from their spatial-temporal patterns to which attractions and activities are the most popular in the destination, and therefore, they can help in the decision making of economic agents. Therefore, this thesis tries to better understand which Big Data sources are the most useful when dealing with tourism variables and also to propose methodological improvements so that these sources can be applied to the field of rural tourism, and more specifically, to the prediction of tourists. In this thesis several advances in this aspect are presented: First, a classification of data sources that every tourist generates during his tourist process and that compose his Digital Footprint. Then, with respect to this classification, Google Trends is chosen as the most appropriate source to help predict tourist demand, but accuracy problems are found, which are demonstrated and exemplified. Further on, it is demonstrated how this accuracy error is generated through the GT sampling process and solutions are proposed to alleviate this error, namely by obtaining more extractions and using their mean. Finally, this method is tested for the prediction of monthly overnight stays in rural tourism accommodations in Spain. In summary, the contribution that this thesis aims to make is to provide a better understanding of Big Data sources and help to generate good practices in the use of them so that they can be applied to the prediction of real variables in rural tourism, in a way that streamlines and improves the decision making of economic agents. / Cebrián Cerdá, E. (2024). Big Data Sources Applied to Rural Tourism [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/206090