The depleting fossil fuel and environmental concerns have created a revolutionary movement towards the installation and utilization of Renewable Energy Sources (RES) such as wind and solar energy. The RES entails challenges, both in regards to the physical integration into a grid system and regarding management of the expected demand. The flexibility in energy demand can facilitate the alignment of the supply and demand to achieve a dynamic Demand Response (DR). The flexibility is often not explicitly available or provided by a user and has to be analyzed and extracted automatically from historical consumption data. The predictive analytics of consumption data can reveal interesting patterns and periodicities that facilitate the effective extraction and representation of flexibility. The device-level analysis captures the atomic flexibilities in energy demand and provides the largest possible solution space to generate demand/supply schedules. The presence of stochasticity and noise in the device-level consumption data and the unavailability of contextual information makes the analytics task challenging. Hence, it is essential to design predictive analytical techniques that work at an atomic data granularity and perform various analyses on the effectiveness of the proposed techniques. The Ph.D. study is sponsored by the TotalFlex Project (http://www.totalflex.dk/) and is part of the IT4BI-DC program with Aalborg University and TU Dresden as Home and Host University, respectively. The main objective of the TotalFlex project is to develop a cost-effective, market-based system that utilizes total flexibility in energy demand, and provide financial and environmental benefits to all involved parties. The flexibilities from various devices are modeled using a unified format called a flex-offer, which facilitates, e.g., aggregation and trading in the energy market. In this regards, this Ph.D. study focuses on the predictive analytics of the historical device operation behavior of consumers for an efficient and effective extraction of flexibilities in their energy demands. First, the thesis performs a comprehensive survey of state-of-the-art work in the literature. It presents a critical review and analysis of various previously proposed approaches, algorithms, and methods in the field of user behavior analysis, forecasting, and flexibility analysis. Then, the thesis details the flexibility and flex-offer concepts and formally discusses the terminologies used throughout the thesis. Second, the thesis contributes to a comprehensive analysis of energy consumption behavior at the device-level. The key motive of the analysis is to extract device operation patterns of users, the correlation between devices operations, and influence of external factors in device-level demands. A novel cost/benefit trade-off analysis of device flexibility is performed to categorize devices into various segments according to their flexibility potential. Moreover, device-specific data preprocessing steps are proposed to clean device-level raw data into a format suitable for flexibility analysis. Third, the thesis presents various prediction models that are specifically tuned for device-level energy demand prediction. Further, it contributes to the feature engineering aspect of generating additional features from a demand consumption timeseries that effectively capture device operation preferences and patterns. The demand predictions utilize the carefully crafted features and other contextual information to improve the performance of the prediction models. Further, various demand prediction models are evaluated to determine the model, forecast horizon, and data granularity best suited for the device-level flexibility analysis. Furthermore, the effect of the forecast accuracy on flexibility-based DR is evaluated to identify an error level a market can absorb maintaining profitability. Fourth, the thesis proposes a generalized process for automated generation and evaluation of flex-offers from the three types of household devices, namely Wet-devices, Electric Vehicles (EV), and Heat Pumps. The proposed process automatically predicts and estimates times and values of device-specific events representing flexibility in its operations. The predicted events are combined to generate flex-offers for the device future operations. Moreover, the actual flexibility potential of household devices is quantified for various contextual conditions and degree days. Fifth, the thesis presents user-comfort oriented prescriptive techniques to prescribe flex-offers schedules. The proposed scheduler considers the trade-off between both social and financial aspects during scheduling of flex-offers, i.e., maximizing the financial benefits in a market and at the same time minimizing the loss of user comfort. Moreover, it also provides a distance-aware error measure that quantifies the actual performance of forecast models designed for flex-offers generation and scheduling. Sixth, the thesis contributes to the comprehensive analysis of the financial viability of device-level flexibility for dynamic balancing of demand and supply. The thesis quantifies the financial benefits of flexibility and investigates the device type specific market that maximizes the potential of flexibility, both regarding DR and financial incentives. Henceforth, a financial analysis of each proposed technique, namely forecast model, flex-offer generation model, and flex-offer scheduling is performed. The key motive is to evaluate the usability of the proposed models in the device-level flexibility based DR scheme and their potential in generating a positive financial incentive to markets and customers. Seven, the thesis presents a benchmark platform for device-level demand prediction. The platform provides the research community with a centralized repository of device-level datasets, forecast models, and functionalities that facilitate comparisons, evaluations, and validation of device-level forecast models. The results of the thesis can contribute to the energy market in materializing the vision of utilizing consumption and production flexibility to obtain dynamic energy balance. The developed demand forecast and flex-offer generation models also contribute to the energy data analytics and data mining fields. The quantification of flexibility further contributes by demonstrating the feasibility and financial benefits of flexibility-based DR. The developed experimental platform provide researchers and practitioners with the resources required for device-level demand analytics and prediction.

Depuis quelques années, nous assistons à une véritable explosion de la production de données dans de nombreux domaines, comme les réseaux sociaux ou le commerce en ligne. Ce phénomène récent est renforcé par la généralisation des périphériques connectés, dont l'utilisation est devenue aujourd'hui quasi-permanente. Le domaine aéronautique n'échappe pas à cette tendance. En effet, le besoin croissant de données, dicté par l'évolution des systèmes de gestion du trafic aérien et par les événements, donne lieu à une prise de conscience sur leur importance et sur une nouvelle manière de les appréhender, qu'il s'agisse de stockage, de mise à disposition et de valorisation. Les capacités d'hébergement ont été adaptées, et ne constituent pas une difficulté majeure. Celle-ci réside plutôt dans le traitement de l'information et dans l'extraction de connaissances. Dans le cadre du Visual Analytics, discipline émergente née des conséquences des attentats de 2001, cette extraction combine des approches algorithmiques et visuelles, afin de bénéficier simultanément de la flexibilité, de la créativité et de la connaissance humaine, et des capacités de calculs des systèmes informatiques. Ce travail de thèse a porté sur la réalisation de cette combinaison, en laissant à l'homme une position centrale et décisionnelle. D'une part, l'exploration visuelle des données, par l'utilisateur, pilote la génération des règles d'association, qui établissent des relations entre elles. D'autre part, ces règles sont exploitées en configurant automatiquement la visualisation des données concernées par celles-ci, afin de les mettre en valeur. Pour cela, ce processus bidirectionnel entre les données et les règles a été formalisé, puis illustré, à l'aide d'enregistrements de trafic aérien récent, sur la plate-forme Videam que nous avons développée. Celle-ci intègre, dans un environnement modulaire et évolutif, plusieurs briques IHM et algorithmiques, permettant l'exploration interactive des données et des règles d'association, tout en laissant à l'utilisateur la maîtrise globale du processus, notamment en paramétrant et en pilotant les algorithmes. / In the past few years, we have seen a large scale data production in many areas, such as social networks and e-business. This recent phenomenon is enhanced by the widespread use of devices, which are permanently connected. The aeronautical field is also involved in this trend. Indeed, its growing need for data, which is driven by air trafic management systems evolution and by events, leads to a widescale focus on its key role and on new ways to manage it. It deals with storage, availability and exploitation. Data hosting capacity, that has been adapted, is not a major challenge. The issue is now in data processing and knowledge extraction from it. Visual Analytics is an emerging field, stemming from the September 2001 events. It combines automatic and visual approaches, in order to benefit simultaneously from human flexibility, creativity and knowledge, and also from processing capacities of computers. This PhD thesis has focused on this combination, by giving to the operator a centered and decisionmaking role. On the one hand, the visual data exploration drives association rules extraction. They correspond to links between the data. On the other hand, these rules are exploited by automatically con_gurating the visualization of the concerned data, in order to highlight it. To achieve this, a bidirectional process has been formalized, between data and rules. It has been illustrated by air trafic recordings, thanks to the Videam platform, that we have developed. By integrating several HMI and algorithmic applications in a modular and upgradeable environment, it allows interactive exploration of both data and association rules. This is done by giving to human the mastering of the global process, especially by setting and driving algorithms.

Submitted by Fernando Souza (fernandoafsou@gmail.com) on 2017-08-15T11:20:29Z No. of bitstreams: 1 arquivototal.pdf: 3152655 bytes, checksum: 5c099c7c99cf7099f300445c236f2a03 (MD5) / Made available in DSpace on 2017-08-15T11:20:29Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 3152655 bytes, checksum: 5c099c7c99cf7099f300445c236f2a03 (MD5) Previous issue date: 2016-02-29 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / The use of Virtual Learning Environments (VLE) in education has grown considerably in recent years, much due to the expansion of courses in distance mode. Such learning support spaces require you to think of new educational methods, particularly as regards the assessment of learning. Because of the large number of students in this type of education working in AVA, a large volume of data is generated. So to get yourself a good learning evaluation model, which offers the teacher possibilities for measuring student performance, it is necessary an analysis of such data. Moodle provides the teacher reports, charts and graphs that let you see the data for the actions of the students. Such actions represent from access to resources and materials, to participate in activities such as discussion forums and results of participation in questionnaires, for example. However, these Moodle native views do not take into account the real needs of teachers, especially in regard to effective monitoring of learning. Given the above, it is evident the need to have tools that help in this process. For this, there have to be a model permits, agile and flexible, storage and use of educational data of students to the application of techniques of Learning Analytics - measurement, collection, analysis and reporting of data on students and their contexts, for understanding and learning in order to optimize the environments in which they occur - with focus on diagnosis of learning disability situations in the context of the Distance Education. To evaluate this proposal, the ConcetpVis tool was implemented from the model proposed in this paper. Then, there was a case study in the Elementary Mathematics discipline of the Bachelor's Degree in Computer Education Unit Distance UFPB and finally presented a questionnaire to a group of teachers answered from his impressions. / O uso de Ambientes Virtuais de Aprendizagem (AVA) na educação tem crescido bastante nos últimos anos, muito em virtude da expansão dos cursos na modalidade à distância. Tais espaços de suporte a aprendizagem exigem que se pense em novos métodos educativos, sobretudo no que se refere à avaliação da aprendizagem. Em virtude da grande quantidade de alunos nessa modalidade de educação atuando no AVA, um grande volume de dados é gerado. Assim, para obter-se um bom modelo de avaliação de aprendizagem, que ofereça ao professor possibilidades de medir o desempenho dos alunos, faz-se necessário uma análise desses dados. O Moodle oferece ao professor relatórios, tabelas e gráficos que permitem visualizar os dados referentes às ações dos alunos. Tais ações representam desde o acesso a recursos e materiais didáticos, até a participação em atividades, como fóruns de discussão e resultados de participação em questionários, por exemplo. Contudo, essas visualizações nativas do Moodle não levam em consideração as reais necessidades dos docentes, sobretudo em relação a um acompanhamento efetivo da aprendizagem. Diante do exposto, fica evidente a necessidade de se ter ferramentas que auxiliem neste processo. Para isso, há de ter-se um Modelo que permita, de modo ágil e flexível, o armazenamento e a utilização dos dados educacionais dos alunos para a aplicação de técnicas de Learning Analytics – medição, coleta, análise e comunicação de dados sobre os alunos e seus contextos, para fins de compreensão e aprendizagem com fim de otimizar os ambientes em que ocorrem – com foco no diagnóstico de situações de dificuldade de aprendizagem no contexto da Educação a Distância. Para avaliar esta proposta, a ferramenta ConcetpVis foi implementada a partir do modelo proposto no presente trabalho. Em seguida, realizou-se um estudo de caso na disciplina Matemática Elementar do curso de Licenciatura em Computação da Unidade de Educação a Distância da UFPB e, por fim, apresentou-se um questionário para que um grupo de professores respondesse a partir de suas impressões.

This report documents the work and results of a master’s thesis in Media Tech- nology that has been carried out at the Department of Science and Technology at Linköping University with the support of Sports Editing Sweden AB (SES). Its aim is to create a solution which aids the users of SES’ web CMS products in gaining insight into web site visit and usage statistics. The resulting solu- tion is the concept and initial version of a web based service. This service has been developed through an agile process with user centered design in mind and provides a graphical user interface which makes high use of visualizations to achieve the project goal.

The first part of this thesis, Monitoring website progress after the implementation of a language mutation, focuses on the theoretical foundations of web analytics (history of web analytics, data collection methods, basic metrics) and Internet marketing (importance of Internet marketing and its distribution, Pay Per Click advertising). An analytical tool Google Analytics is also presented in this part. The analytical and application part contains a presentation of Segway-Point company and its website. On this website the progress (sessions, bounce rate, users flow, conversions, etc.) before and after the implementation of a language mutation was monitored and the ads settings using Google AdWords was set up accordingly. The result is the evaluation of hypotheses and a list of advices and recommendations for Segway-Point company.

<div>This study aimed to identify if data and analytics are, or can be, meaningfully and extensively used for improving efficiencies in project management. The research problem was addressed using a survey, involving capture, collection, analysis and interpretation of qualitative (and some quantitative) data obtained from industry practitioners of project management.</div><div><br></div><div>The study was completed in two important parts. First was the laying of groundwork which involved questionnaire planning and design to ensure coverage, completeness, relevance, usefulness, and logical (and where pertinent, statistical) validity of the answers for performing analysis and drawing inferences. The second part was the actual analysis of the survey results, and compilation of the research details into this written report with a conclusion (my M.S. thesis). </div><div><br></div><div>The survey was mainly in the form of multiple-choice questions, along with two free form text boxes to glean additional insights from comments and notes that structured questions with fixed choices for answers could not have easily elicited.</div><div><br></div>

No description available.

Social Academic Analytics (SAA) is proposed as a new scientific approach toward developing suitable instruments to promote virtual collaboration among participants in the higher education field. SAA refers to the process of extracting relational data for the purpose of exploring organizational structures within virtual learning organizations and knowledge networks. Implementation of SAA provides opportunities for organizers and instructors to optimize socio-technological infrastructures within (virtual) knowledge networks so as to encourage collaborative work, while offering significant potential for quality assurance. SAA combines theories and models from both informatics and the social sciences at the macro level in order to formulate data analysis for the field of (web-based) educational research. In this paper we introduce SAA and its constituent activities. Finally we select case studies and applications to compare analytical concepts from diverse disciplines and conclude with further suggestions as to how SAA concepts can be applied in educational data management.

No description available.

Nowadays companies are in a dynamic environment leading to competition in finding new revenue streams to strengthen their positions in their markets by using new technologies to provide capabilitiesto organize resources whilst taking into account changes that can occur in their environment. Therefore, decision making is inevitable to combat uncertainties where taking the optimal action by leveraging concepts and technologies that support decision making such as Business Intelligence (BI)tools and systems could determine a company’s future. Companies can optimize their decision making with BI features like Data-Driven Alerts that sends messages when fluctuations occur within a supervised threshold that reflects the state of business operations. The purpose of this research was to conduct an empirical study on how Swedish companies and enterprises located in different industries apply BI tools and with Data-driven Alerts features for decision making whereby we further studied the characteristics of Data-driven Alerts in terms of usability from the perspectives of different industry professionals through the thematic lens of the Technology acceptance model (TAM) in a qualitative approach. We conducted interviews with professionals from diverse organizations where we applied the Thematic Coding technique on empirical results for further analysis. We found out that by allowing possibilities for users to analyze data in their own preferences for decisions, it will provide managers and leaders with sufficient information needed to empower strategic and tactical decision-making. Despite the emergence of state-of-the-art predictive analytics technologies such as Machine Learning and AI, the literature clearly states that these processes are technical and complex to be comprehended by the decision maker. At the end of the day, prescriptive analytics will end up providing descriptive options being presented to the end user as we move towards automated decision making. This we see as an opportunity for reporting tools and data-driven alerts to be in contemporary symbiotic relationship with advanced analytics in decision making contexts to improve its outcome, quality and user friendliness.