Dans le domaine de “smart cities” ou “villes connectées”, les technologies de l’information et de la communication sont intégrées aux services traditionnels de la ville (eau, électricité, gaz, transports collectifs, équipements publics, bâtiments, etc.) pour améliorer la qualité des services urbains ou encore pour réduire les coûts. Les données dans la ville connectée sont généralement produites par une grande variété d’acteurs. Ces données devraient être partagées entre diverses applications ou services. Or, il y a un problème, comment les acteurs peuvent-ils exercer un contrôle sur la façon dont leurs données vont être utilisées? C’est important car pour encourager le partage des données, nous devons établir des relations de confiance entre acteurs. Les acteurs ont confiance s’ils ont la capacité à contrôler l’utilisation de leurs données. Nous prendrons en compte les obligations définies par les acteurs pour leurs données : (i) Abstraction de certaines informations, (ii) Granularité spatio-temporelle, (iii) Classification des acteurs et des objectifs, et (iv) Monétisation des données. Mes contributions sont: (i) Un modèle de contrôle d’utilisation des données. Ce modèle répond aux obligations définies par les acteurs pour leur données. (ii) Une plateforme en tant que service. La plateforme a rajouté des composants nécessaire pour permettre la transparence et la traçabilité d’utilisation des données basée sur le modèle. (iii) Un outil de visualisation. C’est l’implémentation d’un prototype pour que les acteurs puissent exercer un contrôle sur la façon dont leurs données vont être utilisées. (iv) Une évaluation de la performance et l’impact de notre solution. Ces solutions permettent l’établissement des relations de confiance pour le partage des données de Smart Cities basées sur le modèle de contrôle d’utilisation des données. Les résultats de ma thèse peuvent être appliqués à la plateforme IoT Datavenue d’Orange / In smart cities, Information and Communication Technologies, in particular Internet of Things (IoT) Technologies, are integrated into traditional services of our city, for example waste management, air pollution monitoring, and parking to improve quality while reducing costs of these services. IoT data in this context are generated by different actors, such as service providers, developers, and municipal authorities. These data should be shared among applications or services. However, in traditional scenario, there is no sharing of IoT data between them. Each actor consumes data from sensors deployed on behalf of that actor, and network infrastructure maybe shared. In order to encourage IoT data sharing, we need to establish the confidence between the actors. Exercising control over the usage of data by other actors is critical in building trust. Thus, the actors should have an ability to exercise control on how their data are going to be used. This major issue have not been treated in IoT namely Usage Control. In this thesis, we take into account obligations defined by the actors for their data (i) Abstraction of certain information, (ii) Spatial and temporal granularity, (iii) Classification of actors and purposes, and (iv) Monetization of data. For example, requirements of data usage in Intelligent parking applications are (i) Data owners have full access to all the details, (ii) Municipal authorities can access the average occupancy of parking place per street on an hourly basis, (iii) Commercial service providers can access only statistical data over a zone and a weekly basis, and (iv) Monetization of data can be based on subscription types or users roles. Thesis contributions include: (i) Policy-based Data Usage Control Model (DUPO) responds to the obligations defined by actors to their data. (ii) Trustworthy Data Sharing Platform as a Service allows transparency and traceability of data usage with open APIs based on the DUPO and Semantic technologies. (iii) Visualization Tool Prototype enables actors to exercise control on how their data will be used. (iv) Evaluation of the performance and the impact of our solution. The results show that the performance of the added trust is not affecting of the system. Mistrust might hamper public acceptance of IoT data sharing in smart cities. Our solution is key which will establish the trust between data owners and consumers by taking into account the obligations of the data owners. It is useful for data operators who would like to provide an open data platform with efficient enablers to partners, data-based services to clients, and ability to attract partners to share data on their platforms

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Diploma thesis deals with complex view on the current online advertising form in the context of continuous technological development of contemporary society. Internet advertising and its range is viewed from a marketing perspective and in the context of media studies. Together with online advertising history, diploma thesis represents the development of the internet market and in this context it is pointed at reach of online advertising industry. Readers are acquainted with basic types of online advertisements, payment models and online advertising buying ways. Thesis in detail deals with modern trends such as programmatic buying of online advertising, behavioral targeting, retargeting, and many others that are examined from the perspective of advertisers, online content publishers and users whose privacy is from some perspective disrupted by these trends. To show the complexity of the issue, the thesis also contains deeper information about technology. At the end of the theoretical part, the thesis deals with intensively discussed topic of ad-blockers. To create a wider conclusion about the topic, there is also a part of the work that contains interpretation of survey focused on standard users and presentation on findings from structured questionnaire that has been designated to leading Czech...

Användningen av fossila bränslen och den ökade digitaliseringen har båda en stor påverkan på klimatet. Klimatförändringar är ett allvarligt hot och många länder arbetar för att minska växthusgasutsläppen. Trots ökad användning av förnybar energi är energisektorn fortfarande en stor källa till utsläpp. Samhällets utveckling har ökat användningen av digital kommunikation, vilket leder till ökad energiförbrukning. Informationsteknik (ICT) har revolutionerat vårt sätt att leva, arbeta och studera, men dess energibehov strider mot målet om koldioxidneutralitet. ICT-sektorn behöver anpassa sig till en hållbar utveckling. Datacenter är energikrävande, vilket är ett snabbt växande problem då dessa står för en fjärdedel av ICT-sektorns koldioxidutsläpp. Sektorn som helhet bidrar med 2,3% av de globala utsläppen och i och med den fortsatta utvecklingen av ICT inom samhället, så ökar även konsumtionen från användare. För att arbeta mot en mer hållbar värld behöver individer ha tillgång till vägledning för att göra långsiktiga förändringar och informerade val. Teori och forskning har analyserats och banat väg för konstruktionen av en skattningsmodell för att främja medvetenhet om ICT-sektorns klimatpåverkan och främja hållbara digitala vanor. Modellen tar hänsyn till flera parametrar och aktiviteter som anses vara relevanta för framtagandet av en applikation för användarna. Data från olika studier används som underlag för att beräkna individens digitala koldioxidutsläpp, energiförbrukning och kostnader. Applikationen testas i en fallstudie för att undersöka den faktiska klimatpåverkan av digital användning. Resultaten visar att vissa aktiviteter, som videokonferenser och samtal, är mer energikrävande än e-postkommunikation. Valet av enhet och tidsåtgång har störst påverkan på resultatet. Rekommendationer ges baserat på användarbeteenden för att minska energiförbrukningen. Målet är att skapa medvetenhet och stödja hållbar användning av digitala tjänster.

Submitted by SANTIAGO BURGADA (sburgada@maxam.net) on 2015-01-25T12:10:08Z No. of bitstreams: 1 DISSERTATION SANTIAGO BURGADA CORPORATE INTERNATIONAL MASTERS SUBMISSION VERSION.pdf: 3588309 bytes, checksum: b70385fd690a43ddea32379f34b4afe9 (MD5) / Approved for entry into archive by Janete de Oliveira Feitosa (janete.feitosa@fgv.br) on 2015-02-04T19:27:15Z (GMT) No. of bitstreams: 1 DISSERTATION SANTIAGO BURGADA CORPORATE INTERNATIONAL MASTERS SUBMISSION VERSION.pdf: 3588309 bytes, checksum: b70385fd690a43ddea32379f34b4afe9 (MD5) / Approved for entry into archive by Marcia Bacha (marcia.bacha@fgv.br) on 2015-02-11T13:27:32Z (GMT) No. of bitstreams: 1 DISSERTATION SANTIAGO BURGADA CORPORATE INTERNATIONAL MASTERS SUBMISSION VERSION.pdf: 3588309 bytes, checksum: b70385fd690a43ddea32379f34b4afe9 (MD5) / Made available in DSpace on 2015-02-11T13:34:18Z (GMT). No. of bitstreams: 1 DISSERTATION SANTIAGO BURGADA CORPORATE INTERNATIONAL MASTERS SUBMISSION VERSION.pdf: 3588309 bytes, checksum: b70385fd690a43ddea32379f34b4afe9 (MD5) Previous issue date: 2014-10-29 / Industrial companies in developing countries are facing rapid growths, and this requires having in place the best organizational processes to cope with the market demand. Sales forecasting, as a tool aligned with the general strategy of the company, needs to be as much accurate as possible, in order to achieve the sales targets by making available the right information for purchasing, planning and control of production areas, and finally attending in time and form the demand generated. The present dissertation uses a single case study from the subsidiary of an international explosives company based in Brazil, Maxam, experiencing high growth in sales, and therefore facing the challenge to adequate its structure and processes properly for the rapid growth expected. Diverse sales forecast techniques have been analyzed to compare the actual monthly sales forecast, based on the sales force representatives’ market knowledge, with forecasts based on the analysis of historical sales data. The dissertation findings show how the combination of both qualitative and quantitative forecasts, by the creation of a combined forecast that considers both client´s demand knowledge from the sales workforce with time series analysis, leads to the improvement on the accuracy of the company´s sales forecast.

The exponential rise in the generation of data has introduced many new areas of research including data science, data engineering, machine learning, artificial in- telligence to name a few. It has become important for any industry or organization to precisely understand and analyze the data in order to extract value out of the data. The value of the data can only be realized when it is put into practice in the real world and the most common approach to do this in the technology industry is through software engineering. This brings into picture the area of privacy oriented software engineering and thus there is a rise of data protection regulation acts such as GDPR (General Data Protection Regulation), PDPA (Personal Data Protection Act), etc. Many organizations, governments and companies who have accumulated huge amounts of data over time may conveniently use the data for increasing business value but at the same time the privacy aspects associated with the sensitivity of data especially in terms of personal information of the people can easily be circumvented while designing a software engineering model for these types of applications. Even before the software engineering phase for any data processing application, often times there can be one or many data sharing agreements or privacy policies in place. Every organization may have their own way of maintaining data privacy practices for data driven development. There is a need to generalize or categorize their approaches into tactics which could be referred by other practitioners who are trying to integrate data privacy practices into their development. This qualitative study provides an understanding of various approaches and tactics that are being practised within the industry for privacy preserving data science in software engineering, and discusses a tool for data usage monitoring to identify unethical data access. Finally, we studied strategies for secure data publishing and conducted experiments using sample data to demonstrate how these techniques can be helpful for securing private data before publishing. / Graduate