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Today's low-power devices, such as smartphones and wearables, form a very heterogeneous ecosystem. Applications in such a system typically follow a reactive pattern based on stream analytics, i.e., sensing, processing, and actuating. Despite the simplicity of this pattern, deciding where to place the processing tasks of an application to achieve energy efficiency is non-trivial in a heterogeneous system since application components are distributed across multiple devices. In this paper, we present Aves - a decision-making engine based on a holistic energy-prediction model, with which the processing tasks of applications can be placed automatically in an energy-efficient manner without programmer/user intervention. We validate the effectiveness of the model and reveal several counter-intuitive placement decisions. Our decision engine's improvements are typically 10-30%, with up to a factor 14 in the most extreme cases. We also show that Aves gives an accurate decision in comparison with real energy measurements for two sensor-based applications.

Sensor-based applications using Big Data are of increasing importance in various fields. A typical example of such use cases is building health-care applications [1], [2]. A typical scenario is where a patient's heart rate is monitored by a smartwatch. A smartphone can then analyze the gathered data and identify patterns in the patient's heart rate. However, if the data analysis is too complex to be performed on a smartphone, the computation could be offloaded to a nearby cloudlet or a remote cloud. A decision usually follows the analysis, and actuation is performed accordingly (e.g., a message is sent to either the patient or the doctor). Developing such an application is intrinsically complex, as the programmer needs to reconcile different APIs specific to different platforms.

GDPR (General Data Protection Regulation) is a new regulation of the European Union that superimposes strict privacy constraints on storing, accessing and processing user data, as a way to ensure that personal user data are not violated neither disclosed without an explicit consent. As a consequence, business processes that interact with large amounts of such data may easily cause GDPR violations, due to the typical complexity of such processes. Inspired by these considerations, this paper highlights the challenges and critical aspects associated with the GDPR compliance journey when opting for naïve straight-forward solutions. We propose a business-aware GDPR compliance journey using online process mining. Using several large log files generated based on a real scenario, we show that the proposed tool is both effective and efficient. As such, it proves to be a powerful concept for usage in incremental GDPR compliance environments.

This Ph.D. work addresses the problem of prediction within energy systems design and operation problems, and particularly the adequacy assessment of renewable power generation systems. The general aim is to develop an empirical modeling framework for providing predictions with the associated uncertainties. Along this research direction, a non-parametric, empirical approach to estimate neural network (NN)-based prediction intervals (PIs) has been developed, accounting for the uncertainty in the predictions due to the variability in the input data and the system behavior (e.g. due to the stochastic behavior of the renewable sources and of the energy demand by the loads), and to model approximation errors. A novel multi-objective framework for estimating NN-based PIs, optimal in terms of both accuracy (coverage probability) and informativeness (interval width) is proposed. Ensembling of individual NNs via two novel approaches is proposed as a way to increase the performance of the models. Applications on real case studies demonstrate the power of the proposed framework., Cette thèse s’intéresse à la problématique de la prédiction dans le cadre du design de systèmes énergétiques et des problèmes d’opération, et en particulier, à l’évaluation de l’adéquation de systèmes de production d’énergie renouvelables. L’objectif général est de développer une approche empirique pour générer des prédictions avec les incertitudes associées. En ce qui concerne cette direction de la recherche, une approche non paramétrique et empirique pour estimer les intervalles de prédiction (PIs) basés sur les réseaux de neurones (NNs) a été développée, quantifiant l’incertitude dans les prédictions due à la variabilité des données d’entrée et du comportement du système (i.e. due au comportement stochastique des sources renouvelables et de la demande d'énergie électrique), et des erreurs liées aux approximations faites pour établir le modèle de prédiction. Une nouvelle méthode basée sur l'optimisation multi-objectif pour estimer les PIs basée sur les réseaux de neurones et optimale à la fois en termes de précision (probabilité de couverture) et d’information (largeur d’intervalle) est proposée. L’ensemble de NN individuels par deux nouvelles approches est enfin présenté comme un moyen d’augmenter la performance des modèles. Des applications sur des études de cas réels démontrent la puissance de la méthode développée.

In this work, the non-dominated sorting genetic algorithm-II (NSGA-II) is applied to determine the weights of a neural network trained for short-term forecasting of wind speed. More precisely, the neural network is trained to produce the lower and upper bounds of the prediction intervals of wind speed. The objectives driving the search for the optimal values of the neural network weights are the coverage of the prediction intervals (to be maximized) and the width (to be minimized). A real application is shown with reference to hourly wind speed, temperature, relative humidity and pressure data in the region of Regina, Saskatchewan, Canada. Correlation analysis shows that the wind speed has weak dependence on the above mentioned meteorological parameters; hence, only hourly historical wind speed is used as input to a neural network model trained to provide in output the one-hour-ahead prediction of wind speed. The originality of the work lies in proposing a multi-objective framework for estimating wind speed prediction intervals (PIs), optimal both in terms of accuracy (coverage probability) and efficacy (width). In the case study analyzed, a comparison with two single-objective methods has been done and the results show that the PIs produced by NSGA-II compare well with those and are satisfactory in both objectives of high coverage and small width.

International audience; Wind speed forecasting is a fundamental task in wind power production management. In the present work, a multi-objective genetic algorithm (MOGA) is employed to train an artificial neural network (ANN) for short-term forecasting of wind speed prediction intervals.

The city of Istanbul, Turkey is a large metropolis with 13 million residents. Every day, trucks carrying hazardous goods (hazmat) in Istanbul expose risk to the residents as well as economic assets of the city. In this paper, we analyze Istanbul's hazmat transportation risk profile using risk analysis techniques and provide the authorities an interactive GIS-based decision support system. Past studies in this area mainly address long-haul hazmat transportation. Hence we introduce a new risk model that considers population exposure along a route and the duration of such exposure, the latter being variable due to the congested nature of road transportation in urban areas. Using this DSS, we produce a risk map of Istanbul and run several routing scenarios between selected hazmat shipment origins and destinations. Results suggest that the hazmat routes under the new model may not always match with how hazmat is usually transported in an urban area.

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2009, Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2009, Bu çalışma kapsamında, günümüze kadar yapılmış optimal ve sezgisel işyeri düzenleme algoritmaları incelenmiş ve olabildiğince açıklanmaya çalışılmıştır. Yapılan kaynak araştırmasına ek olarak, metal sektöründe faaliyet gösteren orta ölçekli bir fabrikada, bir işyeri düzenleme paket programı olan VIP-PLANOPT 2006 yazılım programı kullanılarak bir gerçek yaşam problemi ele alınmıştır. Yapılan uygulama, fabrika için hem yazılım programından elde edilen seçenek yerleşimleri, hem de uzman görüşüne dayanarak oluşturulan sezgisel yerleşim düzenini içermektedir. Yapılan literatür incelemesi ve uygulama sonucunda görülmüştür ki, her ne kadar kullanılan yazılım programı ile fabrika için en iyi yerleşim düzeni bulunmuş olsa da, önemli olan bu çözümün fabrikanın işleyişine ve gerçek yaşama uygun olmasıdır diğer bir ifade ile gerçekleştirilebilir olmasıdır., In this study, an attempt is made to present state-of-the-art review of optimal and heuristic studies on facility layout problems and to explain them as much as possible. In addition to review, an application in a medium sized factory operating on metal industry sector is done by using VIP-PLANOPT 2006 software package. The application consists of both alternative layouts that have been obtained from the program and heuristic layout design obtained from expert opinion. After literature review and application, it can be claimed that, although optimal facility layout was obtained from the program, implementing this optimal layout to the factory is more important., Yüksek Lisans, M.Sc.

Today, scientific experiments and simulations produce massive amounts of heterogeneous data that need to be stored and analyzed. Given that these large datasets are stored in many files, formats and locations, how can scientists find relevant data, duplicates or similarities? In this context, we concentrate on developing algorithms to compare similarity of time series for the purpose of search, classification and clustering. For example, generating accurate patterns from climate related time series is important not only for building models for weather forecasting and climate prediction, but also for modeling and predicting the cycle of carbon, water, and energy. We developed the methodology and ran an exploratory analysis of climatic and ecosystem variables from the FLUXNET2015 dataset. The proposed combination of similarity metrics, nonlinear dimension reduction, clustering methods and validity measures for time series data has never been applied to unlabeled datasets before, and provides a process that can be easily extended to other scientific time series data. The dimensionality reduction step provides a good way to identify the optimum number of clusters, detect outliers and assign initial labels to the time series data. We evaluated multiple similarity metrics, in terms of the internal cluster validity for driver as well as response variables. While the best metric often depends on a number of factor, the Euclidean distance seems to perform well for most variables and also in terms of computational expense.