Дисертації з теми "Data sharing approach"

Thesis (M.S. in Systems Technology (Joint Command, Control, Communications, Computers, and Intelligence Systems) Naval Postgraduate School, June 1995.
"June 1995." Thesis advisor(s): Orin E. Marvel. Includes bibliographical references. Also available online.

Bien que la technologie de composition de services Web soit considérée comme l’une des technologies les plus prometteuses pour l’intégration des sources de données hétérogènes et multiples ainsi que pour la réalisation d’opérations complexes, la question de la protection des données personnelles demeure l’une des préoccupation majeure liés à cette technologie. Ainsi, lors d’un processus de composition, l’échange de données entre toutes les entités – tels que, les services Web recueillant et fournissant des données, les individus dont les données peuvent être fournies et gérées par les services Web, les systèmes qui composent les services Web et les clients finaux des services – est une étape nécessaire et indispensable pour répondre à des requêtes complexes. En conséquence, des données personnelles sont échangées et manipulées entre toutes les entités du système. Notre objectif dans cette thèse est la conception et le développement d’un cadre permettant d’améliorer la composition des services Web avec des mécanismes de protection des données personnelles. Pour atteindre cet objectif, nous avons proposé une approche générale composée de trois éléments. Premièrement, nous avons proposé un modèle formel de confidentialité pour permettre aux services Web de décrire leurs contraintes de confidentialité liées aux données personnelles. Notre modèle permet une spécification des contraintes de confidentialité relative non seulement au niveau des données manipulées, mais aussi au niveau des opérations invoquées par les services. Deuxièmement, nous développons un algorithme de compatibilité qui permet de vérifier formellement la compatibilité entre les exigences et les politiques de confidentialité de tous les services lors d’un processus de composition. Troisièmement, dans le cas où certains services dans la composition sont incompatibles par rapport à leur spécification de confidentialité, nous avons introduit une nouvelle approche basée sur un modèle de négociation dans la perspective de trouver une composition compatible (c’est-à-dire, d’obtenir la compatibilité de toutes les spécifications de confidentialité des services impliqués dans la composition). Enfin, nous avons mis en œuvre les techniques présentées dans cette thèse au sein du prototype PAIRSE et mené une étude de performance sur les algorithmes proposés
While Web service composition technologies have been beneficial to the integration of a wealth of information sources and the realization of complex and personalized operations, the issue of privacy is considered by many as a major concern in services computing. Central to the development of the composition process is the exchange of sensitive and private data between all parties: Web services collecting and providing data, individuals whose data may be provided and managed by Web services, systems composing Web service to answer complex queries, and requesters. As a consequence, managing privacy between all parties of the system is far from being an easy task. Our goal in this thesis is to build the foundations of an integrated framework to enhance Web service composition with privacy protection capabilities. To this aim, we firstly propose a formal privacy model to allow Web services to describe their privacy specifications. Our privacy model goes beyond traditional data-oriented models by dealing with privacy not only at the data level but also service level. Secondly, we develop a compatibility-matching algorithm to check privacy compatibility between privacy requirements and policies within a composition. Thirdly, in the case where some services in the composition are incompatible regarding their privacy specifications, we introduce a novel approach based on a negotiation model to reach compatibility of concerned services (i.e. services that participate in a composition which are incompatible). Finally, we conduct an extensive performance study of the proposed algorithms. The techniques presented in this dissertation are implemented in PAIRSE prototype

Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2016.
People who are referred to as data owners in this study, use the Internet for various purposes and one of those is using online services like Gmail, Facebook, Twitter and so on. These online services are offered by organizations which are referred to as data controllers. When data owners use these service provided by data controllers they usually have to agree to the terms and conditions which gives data controllers indemnity against any privacy issues that may be raised by the data owner. Data controllers are then free to share that data with any other organizations, referred to as third parties. Though data controllers are protected from lawsuits it does not necessarily mean they are free of any act that may be considered a privacy violation by the data owner. This thesis aims to arrive at a design proposition using the design science research paradigm for a middleware extension, specifically focused on the Tomcat server which is a servlet engine running on the JVM. The design proposition proposes a client side annotation based API to be used by developers to specify classes which will carry data outside the scope of the data controller's system to a third party system, the specified classes will then have code weaved in that will communicate with a Privacy Engine component that will determine based on data owner's preferences if their data should be shared or not. The output of this study is a privacy enhancing platform that comprises of three components the client side annotation based API used by developers, an extension to Tomcat and finally a Privacy Engine.

We asked whether the design of a Science Data Repository (SDR) can influence data sharing behaviour in small scientific collaborations. We hypothesised that an SDR can influence data-sharing behaviour when its design considers the context of data-sharing. We proposed an alternative approach to those documented in the literature, employing a combination of socio-technical empirical and analytical methods for context capturing, and choice architecture for context incorporation. To evaluate the approach we applied it to design features in a Scientific Data Repository for a population of small scientific collaborations within the Life Sciences. The application of this thesis' approach consisted of an exploratory case study, a review of factors associated with data sharing, the definition of design claims, and implementation of a set of design features. We collected data using interviews with members of the collaborations and designers of the SDR; as well as obtaining the data-logs from the collaborations' SDR. We evaluated the resulting design features using an asynchronous web experiment. We found that using the empirical approach to context capturing we are able to effectively identify factors associated with data sharing in the small scientific collaborations. Moreover, we identified a number of limitations on the application of the analytical approach to context capturing. Furthermore, we found that the Choice Architecture based procedure for context incorporation can define effective design features in Science Data Repositories. In this work, we show that we can facilitate data-sharing by incorporating context into the design of a Science Data Repository, and identified a set of restrictions to use our approach. The approach proposed in this thesis can be used by practitioners wishing to improve data sharing in an SDR. Contributions, such as the survey of factors associated with data sharing behaviour, can be used by researchers to understand the problems associated with data sharing in small scientific collaborations.

Les médias sociaux occupent un rôle grandissant dans de nombreux domaines de notre vie quotidienne. Parmi d'autres, les réseaux sociaux tels que Facebook, Twitter, LinkedIn et Google+ dont la popularité a explosé ces dernières années, attirent des millions d'utilisateurs qui se communiquent, publient et partagent des informations et contenus à un rythme sans précédent. Outre les avantages reconnus, les réseaux sociaux ont également soulevé des problèmes divers. Nous sommes particulièrement intéressés par deux problèmes spécifiques : surcharge d'information et cloisonnement de données. Ces deux problèmes empêchent les utilisateurs d'exploiter pleinement et efficacement la richesse des informations poussées sur les réseaux sociaux. Les utilisateurs ont des difficultés pour filtrer tous les contenus reus, pour découvrir de nouveaux contenus au-delà de leurs réseaux personnels, et surtout pour partager les contenus intéressants avec leurs différents groupes d'intérêt. Pour aider les utilisateurs à surmonter ces difficultés, nous proposons une Approche centrée sur utilisateur et basée groupe pour filtrer et partager des données sociales. Cette nouvelle approche a un double objectif : (1) permettre aux utilisateurs d'agréger leurs données sociales en provenance de différents réseaux sociaux, d'en extraire des contenus de leur intérêt et (2) organiser et partager les contenus au sein de différents groupes. Les membres d'un groupe sont en outre en mesure de choisir quelle partie de leurs données à partager avec le groupe et définir collectivement les sujets d’intérêt de ce dernier. Pour implémenter l'approche proposée, nous spécifions une architecture de système comprenant plusieurs modules extensibles, et nous développons un prototype fonctionnel basé Web, appelé SoCoSys. Les résultats expérimentaux, obtenus des deux tests différents, valident les valeurs ajoutées de notre approche
The social media have played an increasingly important role in many areas of our every day life. Among others, social network sites such as Facebook, LinkedIn, Twitter and Google+ have recently exploded in popularity by attracting millions of users, who communicate with each other, share and publish information and contents at an unprecedented rate. Besides the recognized advantages, social network sites have also raised various issues and challenges. We are particularly interested in two of them, information overload and "walled gardens". These two problems prevent the users from fully and efficiently exploiting thewealth of information available on social network sites. The users have difficulties to filter all incoming contents, to discover additional contents from outside of their friend circles, and importantly to share interesting contents with their different groups of interest. For helping the users to overcome such difficulties, we propose a User-centered and group- based approach for social data filtering and sharing. This novel approach has a twofold purpose : (1) allow the users to aggregate their social data from different social network sites, and to extract from those data the contents of their interest, and (2) organize and share the contents within different groups. The members of a group are moreover able to choose which part of their social data to share with the group, and collectively define its topics of interest. To achieve the proposed approach, we define a modular system architecture including a number of extensible modules, and accordingly build a working Web-based prototype, called SoCoSys. The experimental results, obtained from the two different tests, confirm the added values of our approach

Phishing has been a big concern due to its active roles in recent data breaches and state- sponsored attacks. While existing works have extensively analyzed phishing websites and detection methods, there is still a limited understanding of the data flow of the phishing process. In this thesis, we perform an empirical measurement to draw a clear picture of the data flow of phishing from both attacker and defender perspectives. First, from attackers' perspective, we want to know how attackers collect the sensitive information stolen from victims throughout the end-to-end phishing attack process. So we collected more than 179,000 real-world phishing URLs. Then we build a measurement tool to feed fake credentials to live phishing sites and monitor how the credential information is shared with the phishing server and potentially third-party collectors on the client side. Besides, we also obtain phishing kits to analyze how credentials are sent to attackers and third-parties on the server side. Then, from defenders' perspective, online scan engines such as VirusTotal are heavily used by phishing defenders to label phishing URLs, however, the data flow behind phishing detection by those scan engines is still unclear. So we build our own phishing websites, submit them to VirusTotal for scanning, to understand how VirusTotal works and the quality of its labels. Our study reveals the key mechanisms for information sharing during phishing attacks and the need for developing more rigorous methodologies to assess and make use of the labels obtained from VirusTotal.
Master of Science
Phishing attack is the fraudulent attempt to lure the target users to give away sensitive information such as usernames, passwords and credit card details. Cybercriminals usually build phishing websites (mimicking a trustworthy entity), and trick users to reveal important credentials. However, the data flow of phishing process is still unclear. From attackers' per- spective, we want to know how attackers collect the sensitive information stolen by phishing websites. On the other hand, from defenders' perspective, we are trying to figure out how online scan engines (e.g., VirusTotal) detect phishing URLs and how reliable their detection results are. In this thesis, we perform an empirical measurement to help answer the two questions above. By monitoring and analyzing a large number of real-world phishing websites, we draw a clear picture of the credential sharing process during phishing attacks. Also, by building our own phishing websites and submitting to VirusTotal for scanning, we find that more rigorous methodologies to use VirusTotal labels are desperately needed.

With the rapid development of fitness industry, Internet of Things (IoT) technology is becoming one of the most popular trends for the health and fitness areas. IoT technologies have revolutionised the fitness and the sport industry by giving users the ability to monitor their health status and keep track of their training sessions. More and more sophisticated wearable devices, fitness trackers, smart watches and health mobile applications will appear in the near future. These systems do collect data non-stop from sensors and upload them to the Cloud. However, from a data-centric perspective the landscape of IoT fitness devices and wellness appliances is characterised by a plethora of representation and serialisation formats. The high heterogeneity of IoT data representations and the lack of common accepted standards, keep data isolated within each single system, preventing users and health professionals from having an integrated view of the various information collected. Moreover, in order to fully exploit the potential of the large amounts of data, it is also necessary to enable advanced analytics over it, thus achieving actionable knowledge. Therefore, due the above situation, the aim of this thesis project is to design and implement an ontology based system to (1) allow data interoperability among heterogeneous IoT fitness and wellness devices, (2) facilitate the integration and the sharing of information and (3) enable advanced analytics over the collected data (Cognitive Computing). The novelty of the proposed solution lies in exploiting Semantic Web technologies to formally describe the meaning of the data collected by the IoT devices and define a common communication strategy for information representation and exchange.

Abstract The current practice for information sharing with building information modelling (BIM) is distributed data sharing based on conversions. Conversions are problematic due to data loss, redundancy, and conflicting information. The hypotheses of this research were that i) a conversion-free data exchange is a feasible approach for BIM, ii) benefits can be achieved with a conversion-free information sharing, and iii) no impediment in principle exists for wider industrial use. The use of a single data schema by all applications is a requisite for a conversion-free data collaboration. For enabling the free evolution of the data content, a version free data schema is necessary. A model arrangement implementing partial models is needed for the growing size of models. A single shared model approach eliminates data conflicts and duplicates. For the best availability, the location for the shared model should be on a cloud service. Accessing the cloud model only through a web service, which encapsulates all model handling functionality, will ensure data integration and validity. The validity of IT solutions can only be confirmed with real software. For testing the conversion-free BIM method, a software development kit (SDK) with required functionality was programmed. Three applications and a cloud service for handling the shared model were developed with the help of SDK. In the experiments, Leonardo application was used for modelling walls, 3DTrussme for trusses, and Viewer for viewing the model. All applications were using the same shared model on the cloud. In the experimental test, the information exchange occurred without conversions, and all the data were saved only once on the cloud database. Without conversions, less conflicts and redundancies occurred, which lead to better data integrity and integration. Using SDK, there was no technical barrier for applications to join the single shared model ecosystem, but a drawback was that existing BIM programs are not compatible without remarkable changes. The performance was acceptable in the test run, but in real use, the size of the model and the number of applications and users will be much larger. However, a conversion-free single shared model approach can be a possible trend to the next generation BIM as well as a potential alternative for current data sharing methods using distributed files, conversions, and linked data
Tiivistelmä Rakentamisen tietomallinnuksen (BIM) nykyisenä tiedonjakamisen käytäntönä on hajautettu tietojärjestelmä, joka perustuu konversioihin. Konversiot ovat ongelmallisia tiedon häviämisen, ristiriitojen ja päällekkäisyyksien vuoksi. Tämän tutkimuksen hypoteesit olivat: i) konversiovapaa tiedonjakaminen on mahdollista, ii) etuja on saavutettavissa ilman konversioita tapahtuvassa tiedonsiirrossa ja iii) laajemmalle teolliselle käytölle ei ole periaatteellisia esteitä. Konversiovapaa tiedon jakaminen edellyttää yhden dataformaatin käyttöä. Alati kasvavien tietomallien koko vaatii tiedon järjestämismenetelmän, joko mahdollistaa osamallit. Datan ristiriidat ja päällekkäisyydet voidaan estää yhden jaetun mallin menetelmällä. Informaatio on parhaiten kaikkien osapuolien saatavilla, kun jaettu malli sijoitetaan pilvipalveluun. Kun tietomallia käsitellään vain web-palvelun rajapintafunktioilla, tiedon eheys ja kelpoisuus säilyvät. Tietoteknisten ratkaisujen kelpoisuus voidaan viime kädessä osoittaa vain toimivilla ohjelmistoilla. Konversiovapaan menetelmän testausta varten kehitettiin vaadittavat ominaisuudet sisältävä ohjelmistokehityspaketti (SDK), joka on edellytys sovellusten yhteensopivuudelle. Kolme sovellusta eli 3DTrussme, Leonardo ja Viewer ohjelmoitiin SDK:n avulla. Ohjelmointirajapinta sisälsi tarvittavat toiminnallisuudet tiedonjakamiseen, ja se toteutettiin pilvipalveluna. Testiajossa Leonardo-sovelluksella mallinnettiin seinät, 3DTrussmellä suunniteltiin naulalevyristikot ja Viewer-sovelluksella tarkasteltiin mallia. Kaikki kolme sovellusta käyttivät samaa jaettua mallia pilvipalvelussa. Suoritetussa testissä kaikki informaatio jaettiin ilman konversioita ja tallennettiin vain kerran. Ilman konversioita ja päällekkäisyyksiä saavutettiin parempi datan eheys ja integraatio. SDK:n avulla uudet sovellukset pystyivät liittymään yhden jaetun mallin ekosysteemiin ilman teknisiä esteitä. Toisaalta, ilman merkittäviä muutoksia nykyiset BIM sovellukset eivät ole yhteensopivia. Testiajossa suorituskyky oli hyväksyttävä, mutta todellisuudessa mallien koko sekä sovellusten ja käyttäjien lukumäärä ovat paljon suurempia. Tutkimus osoitti, että konversiovapaa yhden jaetun mallin menetelmä voi olla seuraava BIM-kehityssuunta ja vaihtoehto nykyisille tiedonsiirtoratkaisuille, jotka käyttävät erillisiä tiedostoja, konversioita ja linkkejä

Aujourd'hui, la gestion des documents est l'un des problèmes les plus importants en informatique. L'objectif de cette thèse est de proposer un système de gestion documentaire basé sur une approche appelée recherche structurée et collaborative. Les caractéristiques essentielles sont :

• Dès lors que les utilisateurs ont plusieurs centres d'intérêts, ils sont décrits par des profils, un profil correspondant à un centre d'intérêt particulier. C'est la partie structurée du système.

• Pour construire une description des profils, les utilisateurs jugent des documents en fonction de leur intérêt

• Le système regroupe les profils similaires pour former un certain nombre de communautés virtuelles

• Une fois les communautés virtuelles définies, des documents jugés comme intéressants par certains utilisateurs d'une communauté peuvent être partagés dans toute la communauté. C'est la partie collaborative du système.

Le système a été validé sur plusieurs corpora de documents en utilisant une méthodologie précise et offre des résultats prometteurs.

Doctorat en sciences appliquées
info:eu-repo/semantics/nonPublished

Актуальність теми. На сьогоднішній день MongoDB є СУБД, що часто використовується при розробці систем, орієнтованих на обробку слабко структурованих даних. Однією з причин цього є можливість використання горизонтального масштабування (шардингу) та налаштування розподілу даних, що значною мірою впливає на продуктивність роботи системи в цілому. Тому були розроблені різні стратегії розподілу даних в кластері, такі як: стратегія палаючого будинку, стратегія гарячих точок тощо. Дані стратегії мають свої сфери застосування, свої переваги та недоліки, але загальним недоліком даних стратегій є нерівномірний розподіл даних при наявності так званих «джамбо-чанків», що в свою чергу впливає на продуктивність роботи системи. Об’єктом дослідження є розподіл даних між серверами в кластері MongoDB. Предметом дослідження є програмні засоби для підвищення продуктивності роботи СУБД MongoDB, які призначені для налаштування розподілу даних відповідно до допустимих відсоткових навантажень вузлів кластера, враховуючи вплив «джамбо-чанків». Мета роботи: підвищення продуктивності роботи СУБД MongoDB. Наукова новизна: 1. Запропоновано підхід розподілу даних, який включає в себе ідеї існуючих підходів та, на відміну від існуючих підходів, при розподілі даних враховує можливість наявності «джамбо-чанків». 2. Виконано порівняльний аналіз запропонованого підходу з існуючими, визначено в яких саме ситуаціях потрібно використовувати даний підхід, його переваги та недоліки порівняно з існуючими підходами розподілу даних. Практична цінність отриманих в роботі результатів полягає в тому, що запропонований підхід надає можливість налаштування розподілу даних відповідно до допустимих відсоткових навантажень вузлів кластера, враховуючи вплив «джамбо-чанків». Крім того розроблені в роботі програмні засоби можуть бути використані для реалізації автоматизованого розподілу даних з заданими налаштуваннями. Апробація роботи. Запропонований підхід був представлений та обговорений на науковій конференції магістрантів та аспірантів «Прикладна математика та комп‘ютинг» ПМК-2018 (Київ, 14 - 16 листопада 2018 р.) та на V Міжнародній науково-технічній Internet-конференції «Сучасні методи, інформаційне, програмне та технічне забезпечення систем керування організаційно-технічними та технологічними комплексами», яка проводилась 22 листопада 2018 р. у Національному університеті харчових технологій. Структура та обсяг роботи. Магістерська дисертація складається з вступу, чотирьох розділів та висновків. У вступі подано загальну характеристику роботи, зроблено оцінку сучасного стану проблеми, обґрунтовано актуальність напрямку досліджень, сформульовано мету і задачі досліджень, показано наукову новизну отриманих результатів. У першому розділі розглянуто існуючі підходи розподілу даних, їхні особливості, недоліки та переваги, розглянуто різні реалізації. У другому розділі запропоновано підхід розподілу даних який буде вирішувати виявлену проблему. У третьому розділі наведені алгоритмічні особливості реалізації розроблених програмних засобів для реалізації запропонованого підходу. У четвертому розділі представлено результати тестування запропонованого підходу. У висновках представлені результати проведеної роботи. Магістерська дисертація представлена на 80 аркушах, містить посилання на список використаних літературних джерел.
Actuality of theme. To date, MongoDB is a database, often used in the development of systems focused on the processing of poorly structured data. One reason for this is the ability to use horizontal scaling (shading) and data distribution settings, which greatly affects the performance of the system as a whole. Therefore, various strategies for data splitting in the cluster were developed, such as: a burning house strategy, hot spots strategy, and so on. These strategies have their own applications, their advantages and disadvantages, but the general disadvantage of these strategies is the uneven distribution of data in the presence of so-called "jumbo chunk", which in turn affects the performance of the system. The object of the study is the distribution of data between servers in the MongoDB cluster. The subject of the study is software tools to improve the performance of the MongoDB database, which are designed to configure the distribution of data in accordance with the permissible percentage loads of nodes of the cluster, taking into account the influence of "jumbo chunks". The purpose of the work: to increase the productivity of the DBMS MongoDB. Scientific novelty: 1. A data-sharing approach is proposed that incorporates the ideas of existing approaches and, unlike existing approaches, takes into account the possibility of having "jumbo chunks" when distributing data. 2. A comparative analysis of the proposed approach with existing ones is made, it is determined in what situations it is necessary to use this approach of approach, its advantages and disadvantages in comparison with existing approaches of data distribution. The practical value of the results obtained in the work is that the proposed approach provides the possibility of adjusting the distribution of data in accordance with the permissible percentages of nodes of the cluster, taking into account the influence of "jumbo chunks". In addition, the software developed in the work can be used to implement automated data distribution with the specified settings. Test work. The proposed approach was presented and discussed at the scientific conference of undergraduates and postgraduates "Applied Mathematics and Computing", PMK-2018 (Kyiv, November 14-16, 2018) and at the V International Scientific and Technical Internet Conference "Modern Methods, Information, software and technical support of control systems for organizational, technical and technological complexes", held on November 22, 2018 at the National University of Food Technologies. Structure and scope of work. The master's thesis consists of an introduction, four chapters and conclusions. The introduction gives a general description of the work, assesses the current state of the problem, substantiates the relevance of the research direction, formulates the purpose and objectives of the research, shows the scientific novelty of the results obtained. The first chapter examines the existing approaches to data distribution, their features, disadvantages and advantages, and discusses different implementations. The second chapter proposes a data-sharing approach that addresses the problem identified. The third chapter, algorithmic features of implementation of the developed software tools for implementation of the proposed approach are presented. The fourth chapter presents the results of testing the proposed approach. The conclusions are the results of the work. The master's dissertation is presented on 80 sheets, contains a link to the list of used literary sources.
Актуальность темы. На сегодняшний день MongoDB является СУБД, которая часто используется при разработке систем, ориентированных на обработку слабо структурированных данных. Одной из причин этого является возможность использования горизонтального масштабирования (шардингу) и настройки распределения данных, что в значительной мере влияет на производительность работы системы в целом. Поэтому были разработаны различные стратегии распределения данных в кластере, такие как: стратегия горящего дома, стратегия горячих точек и тому подобное. Данные стратегии имеют свои области применения, свои преимущества и недостатки, но общим недостатком данных стратегий является неравномерное распределение данных при наличии так называемых «джамбо-чанков», что в свою очередь влияет на производительность работы системы. Объектом исследования является распределение данных между серверами в кластере MongoDB. Предметом исследования являются программные средства для повышения производительности работы СУБД MongoDB, которые предназначены для настройки распределения данных в соответствии с допустимых процентных нагрузок узлов кластера, учитывая влияние «джамбо-чанков». Цель работы: повышение производительности работы СУБД MongoDB. Научная новизна: 1. Предложен подход распределения данных, который включает в себя идеи существующих подходов и, в отличие от существующих подходов, при распределении данных учитывает возможность наличия «джамбо-чанков». 2. Выполнен сравнительный анализ предложенного подхода с существующими, определено в каких именно ситуациях нужно использовать данный подход, его преимущества и недостатки по сравнению с существующими подходами распределения данных. Практическая ценность полученных в работе результатов заключается в том, что предложенный подход предоставляет возможность настройки распределения данных в соответствии с допустимыми процентных нагрузками узлов кластера, учитывая влияние «джамбо-чанков». Кроме того разработанные в работе программные средства могут быть использованы для реализации автоматизированного распределения данных с заданными настройками. Апробация работы. Предложенный подход был представлен и обсужден на научной конференции магистрантов и аспирантов «Прикладная математика и компьютинг» ПМК-2018 (Киев, 14 - 16 ноября 2018) и на V Международной научно-технической Internet-конференции «Современные методы, информационное, программное и техническое обеспечение систем управления организационно-техническими и технологическими комплексами», которая проводилась 22 ноября 2018 в Национальном университете пищевых технологий. Структура и объем работы. Магистерская диссертация состоит из введения, четырех глав и выводов. Во введении представлена общая характеристика работы, произведена оценка современного состояния проблемы, обоснована актуальность направления исследований, сформулированы цели и задачи исследований, показано научную новизну полученных результатов. В первой главе рассмотрены существующие подходы распределения данных, их особенности, недостатки и преимущества, рассмотрены различные реализации. Во втором разделе предложено подход распределения данных который будет решать обнаруженную проблему. В третьем разделе приведены алгоритмические особенности реализации разработанных программных средств для реализации предложенного подхода. В четвертом разделе представлены результаты тестирования предложенного подхода. В выводах представлены результаты проведенной работы. Магистерская диссертация представлена на 80 листах, содержит ссылки на список использованных литературных источников.

Dans cette thèse, nous nous sommes intéressés aux protocoles pair-à-pair (P2P), qui représentent une solution prometteuse pour la diffusion et le partage de données à faible coût sur Internet. Nous avons mené, dans un premier temps, une étude comportementale de différents protocoles P2P pour le partage de fichier (distribution de contenus sans contrainte de temps) puis le live. Dans la première étude centréesur le partage de fichier, nous avons montré l’impact d’Hadopi sur le comportement des utilisateurs et discuté l’efficacité des protocoles en fonction du contenu et l’efficacité protocolaire, en se basant sur les choix des utilisateurs. BitTorrent s’est nettement démarqué au cours de cette étude, notamment pour les grands contenus. En ce qui concerne le live, nous nous sommes intéressés à la qualité de servicedu réseau de distribution live Sopcast, car plus de 60% des événements live diffusés en P2P le sont sur ce réseau. Notre analyse approfondie de ces deux modes de distribution nous a fait nous recentrer sur BitTorrent, qui est à la base de tous les protocoles P2P Live, et est efficace en partage de fichier et complètement open source. Dans la seconde partie de la thèse, nous avons proposé et implémenté dansun environnement contrôlé un nouveau protocole sur la base de BitTorrent avec des mécanismes protocolaires impliquant tous les pairs dans la gestion du réseau. Ces nouveaux mécanismes permettent d’augmenter l’efficacité du protocole via une meilleure diffusion, tant pour le live que le partage de fichier, de métadonnées (la pièce la plus rare) et via une méthode dite de push, par laquelle un client va envoyer du contenu aux pairs les plus dans le besoin
In this study, we focused on peer-to-peer protocols (P2P), which represent a promising solution for data dissemination and content delivery at low-cost in the Internet. We performed, initially, a behavioral study of various P2P protocols for file sharing (content distribution without time constraint) and live streaming. Concerning file sharing, we have shown the impact of Hadopi on users’ behavior and discussed the effectiveness of protocols according to content type, based on users’ choice. BitTorrent appeared as the most efficient approach during our study, especially when it comes to large content. As for streaming, we studied the quality of service of Sopcast, a live distribution network that accounts for more than 60% of P2P broadcast live events. Our in-depth analysis of these two distributionmodes led us to focus on the BitTorrent protocol because of its proven efficiency in file sharing and the fact that it is open source. In the second part of the thesis, we proposed and implemented a new protocol based on BitTorrent, in a controlled environment. The modifications that we proposed allow to increase the efficiency of the protocol through improved dissemination of metadata (the rarest piece), both for live and file sharing. An enhanced version is introduced with a push method, where nodes that lag behind receive an extra service so as to improve the overall performance

Les systèmes d’information décisionnels dans le cloud Computing sont des solutions de plus en plus répandues. En effet, ces dernières offrent des capacités pour l’aide à la décision via l’élasticité des ressources pay-per-use du Cloud. Toutefois, les questions de sécurité des données demeurent une des principales préoccupations notamment lorsqu'il s’agit de traiter des données sensibles de l’entreprise. Beaucoup de questions de sécurité sont soulevées en terme de stockage, de protection, de disponibilité, d'intégrité, de sauvegarde et de récupération des données ainsi que des transferts des données dans un Cloud public. Les risques de sécurité peuvent provenir non seulement des fournisseurs de services de cloud computing mais aussi d’intrus malveillants. Les entrepôts de données dans les nuages devraient contenir des données sécurisées afin de permettre à la fois le traitement d'analyse en ligne hautement protégé et efficacement rafraîchi. Et ceci à plus faibles coûts de stockage et d'accès avec le modèle de paiement à la demande. Dans cette thèse, nous proposons deux nouvelles approches pour la sécurisation des entrepôts de données dans les nuages basées respectivement sur le partage vérifiable de clé secrète (bpVSS) et le partage vérifiable et flexible de clé secrète (fVSS). L’objectif du partage de clé cryptée et la distribution des données auprès de plusieurs fournisseurs du cloud permet de garantir la confidentialité et la disponibilité des données. bpVSS et fVSS abordent cinq lacunes des approches existantes traitant de partage de clés secrètes. Tout d'abord, ils permettent le traitement de l’analyse en ligne. Deuxièmement, ils garantissent l'intégrité des données à l'aide de deux signatures interne et externe. Troisièmement, ils aident les utilisateurs à minimiser le coût de l’entreposage du cloud en limitant le volume global de données cryptées. Sachant que fVSS fait la répartition des volumes des données cryptées en fonction des tarifs des fournisseurs. Quatrièmement, fVSS améliore la sécurité basée sur le partage de clé secrète en imposant une nouvelle contrainte : aucun groupe de fournisseurs de service ne peut contenir suffisamment de volume de données cryptées pour reconstruire ou casser le secret. Et cinquièmement, fVSS permet l'actualisation de l'entrepôt de données, même si certains fournisseurs de services sont défaillants. Pour évaluer l'efficacité de bpVSS et fVSS, nous étudions théoriquement les facteurs qui influent sur nos approches en matière de sécurité, de complexité et de coût financier dans le modèle de paiement à la demande. Nous validons également expérimentalement la pertinence de nos approches avec le Benchmark schéma en étoile afin de démontrer son efficacité par rapport aux méthodes existantes
Cloud business intelligence is an increasingly popular solution to deliver decision support capabilities via elastic, pay-per-use resources. However, data security issues are one of the top concerns when dealing with sensitive data. Many security issues are raised by data storage in a public cloud, including data privacy, data availability, data integrity, data backup and recovery, and data transfer safety. Moreover, security risks may come from both cloud service providers and intruders, while cloud data warehouses should be both highly protected and effectively refreshed and analyzed through on-line analysis processing. Hence, users seek secure data warehouses at the lowest possible storage and access costs within the pay-as-you-go paradigm.In this thesis, we propose two novel approaches for securing cloud data warehouses by base-p verifiable secret sharing (bpVSS) and flexible verifiable secret sharing (fVSS), respectively. Secret sharing encrypts and distributes data over several cloud service providers, thus enforcing data privacy and availability. bpVSS and fVSS address five shortcomings in existing secret sharing-based approaches. First, they allow on-line analysis processing. Second, they enforce data integrity with the help of both inner and outer signatures. Third, they help users minimize the cost of cloud warehousing by limiting global share volume. Moreover, fVSS balances the load among service providers with respect to their pricing policies. Fourth, fVSS improves secret sharing security by imposing a new constraint: no cloud service provide group can hold enough shares to reconstruct or break the secret. Five, fVSS allows refreshing the data warehouse even when some service providers fail. To evaluate bpVSS' and fVSS' efficiency, we theoretically study the factors that impact our approaches with respect to security, complexity and monetary cost in the pay-as-you-go paradigm. Moreover, we also validate the relevance of our approaches experimentally with the Star Schema Benchmark and demonstrate its superiority to related, existing methods

La société moderne est confrontée à plusieurs défis causés par les voitures. Dans les villes urbaines, trop de voitures sur la route créent des embouteillages, de la pollution de l'air et du bruit. Dans les villages ruraux, les habitants deviennent de plus en plus dépendants de la voiture, ce qui limite le droit à la mobilité de la population vulnérable. Le covoiturage pourrait être une solution aux deux défis. La thèse tente de comprendre le comportement des participants au covoiturage. Les deux premiers articles sont deux expériences de terrain avec Ecov sur les motivations monétaires et prosociales des conducteurs. Pour les voyages de très courtes distances, les motivations prosociales sont plus importantes pour promouvoir la pratique, tandis que pour les voyages de moyenne distance, les incitations monétaires sont plus importantes. Cependant, mettre un incitatif monétaire très élevé ne surpasse pas un incitatif inférieur mais suffisant. Le troisième article examine l'impact de la grève des cheminots français en 2018 sur le covoiturage en utilisant les données de BlaBlaCar. En moyenne, un jour de grève fait augmenter l'offre de sièges en covoiturage de 7% et la demande de sièges de 29%. Le surplus du consommateur, dans toute la France, augmente 17 753€, lors d'une journée de grève moyenne
Modern societies are faced with multiple challenges caused by widespread car usage. In urban cities, too many cars on the road are creating traffic jams, air pollution, and noise. In rural villages, residents are becoming more and more dependent on cars, limiting the mobility rights of a vulnerable population. Ridesharing could be a solution to both challenges. The thesis adds to the understanding of the behavior of ridesharing participants. The first two papers are two field experiments with Ecov on the monetary and prosocial motivations of drivers. For trips of very short distances, prosocial motivations are more salient for promoting the practice, while for middle-distance trips, monetary incentives are more salient. However, putting a very high monetary incentive does not outperform a lower but sufficient one. The third paper examines the impact of the French railway worker strike in 2018 on long-distance ridesharing using data from BlaBlaCar. An average strike day induces the ridesharing seat supply to increase by 7% and the seat demand to increase by 29%. The ridesharing passengers' consumer surplus also increases by 17,753€ during an average strike day across the whole of France

Les réseaux de capteurs sont utilisés dans l’IoT pour collecter des données. Cependant, une expertise envers les réseaux de capteurs est requise pour interagir avec ces infrastructures. Pour un ingénieur logiciel, cibler de tels systèmes est difficile. Les spécifications des plateformes composant l'infrastructure de capteurs les obligent à travailler à un bas niveau d'abstraction et à utiliser des plateformes hétérogènes. Cette fastidieuse activité peut conduire à un code exploitant de manière non optimisée l’infrastructure. En étant spécifiques à une infrastructure, ces applications ne peuvent également pas être réutilisées facilement vers d’autres infrastructures. De plus, le déploiement de ces applications est hors du champ de compétences d’un ingénieur logiciel car il doit identifier la ou les plateforme(s) requise(s) pour supporter l’application. Enfin, l’architecture peut ne pas être conçue pour supporter l’exécution simultanée d’application, engendrant des déploiements redondants lorsqu’une nouvelle application est identifiée. Dans cette thèse, nous présentons une approche qui supporte (i) la définition de politiques de collecte de données à haut niveau d’abstraction et réutilisables, (ii) leur déploiement sur une infrastructure hétérogène dirigée par des modèles apportés par des experts réseau et (iii) la composition automatique de politiques sur des infrastructures hétérogènes. De ces contributions, un ingénieur peut dès lors manipuler un réseau de capteurs sans en connaitre les détails, en réutilisant des abstractions architecturales disponibles lors de l'expression des politiques, des politiques qui pourront également coexister au sein d'un même réseau
Sensing infrastructures are classically used in the IoT to collect data. However, a deep knowledge of sensing infrastructures is needed to properly interact with the deployed systems. For software engineers, targeting these systems is tedious. First, the specifies of the platforms composing the infrastructure compel them to work with little abstractions and heterogeneous devices. This can lead to code that badly exploit the network infrastructure. Moreover, by being infrastructure specific, these applications cannot be easily reused across different systems. Secondly, the deployment of an application is outside the domain expertise of a software engineer as she needs to identify the required platform(s) to support her application. Lastly, the sensing infrastructure might not be designed to support the concurrent execution of various applications leading to redundant deployments when a new application is contemplated. In this thesis we present an approach that supports (i) the definition of data collection policies at high level of abstraction with a focus on their reuse, (ii) their deployment over a heterogeneous infrastructure driven by models designed by a network export and (iii) the automatic composition of the policy on top of the heterogeneous sensing infrastructures. Based on these contributions, a software engineer can exploit sensor networks without knowing the associated details, while reusing architectural abstractions available off-the-shelf in their policy. The network will also be shared automatically between the policies

Le secteur médical est un domaine dynamique en constante évolution, nécessitant des améliorations continues de ses processus métier et une assistance intelligente aux acteurs impliqués. Ce travail de thèse se focalise sur le processus de soins nécessitant l’implantation d’une prothèse. La particularité de ce processus est qu’il met en interaction deux cycles de vie appartenant respectivement au domaine médical et celui de l’ingénierie. Ceci implique plusieurs actions de collaboration entre des acteurs métier très variés. Cependant, des problèmes de communication et de partage de connaissances peuvent exister en raison de l’hétérogénéité de la sémantique utilisée et des pratiques métiers propres à chaque domaine.Dans ce contexte, ce travail de thèse s’intéresse aux apports des approches d’ingénierie des connaissances et de gestion du cycle de vie du produit pour répondre aux problématiques sous-jacentes au processus de soins médicaux nécessitant l’implantation d’une prothèse. Pour se faire, un cadre conceptuel est proposé pour analyser les connexions entre les cycles de vie de maladie (domaine Médical)et de la prothèse (domaine d’ingénierie). Sur la base de cette analyse, un modèle sémantique sous forme d’une ontologie pour le domaine médical est définit dans le cadre de la construction d’une approche PLM à base de connaissances. L’application de cette proposition est démontrée à travers l’implémentation de quelques fonctions utiles dans un outil PLM du marché nommé AUDROS
Medical sector is a dynamic domain that requires continuous improvement of its business processes and assistance to the actors involved. This research focuses on the medical treatment process requiring prosthesis implantation. The specificity of such a process is that it makes in connection two lifecyclesbelonging to medical and engineering domains respectively. This implies several collaborative actions between stake holders from heterogeneous disciplines. However, several problems of communication and knowledge sharing may occur because of the variety of semantic used and the specific business practices in each domain. In this context, this PhD work is interested in the potential of knowledge engineering and product lifecycle management approaches to cope with the above problems. To do so, a conceptual framework is proposed for the analysis of links between the disease (medicaldomain) and the prosthesis (engineering domain) lifecycles. Based on this analysis, a semantic ontology model for medical domain is defined as part of a global knowledge-based PLM approach proposition. The application of the proposition is demonstrated through an implementation of useful function in the AUDROS PLM software

碩士
國立交通大學
資訊工程系所
92
In recent years, because the speed of network transmission increases, a lot of time-sensitive applications begin to prevail. The most obvious example is the network telephone (Voice over IP). The advantages of voice over IP network are lower cost and flexible. The IP network transmits packet using the “best effort” method, so the quality of VoIP should be taken in consideration by the supplier. More and more network attacks are discovered in recent years, like Deny of Service (DoS), Man-In-Middle attack, and the eavesdropping program (Wiretap).In this thesis, we will focus on the security of voice data in public IP network. Traditional methods used for secure transmission, such as encryption, spend too much time in data encryption and decryption. Those encryption methods also spend much time in key establishment, key exchange and protection. When using visual cryptography and data sharing methods, we can reduce the influence of those problems that mentioned above. Therefore we use this concept in voice data transmission over Internet to prevent from the wiretap attack, and to transit the voice data through the network more efficiently. In addition, we utilize the disjoint multipath routing algorithm to send data, so we can reduce the occurrence of network hot-spot.

碩士
國立東華大學
自然資源與環境學系
99
Ecology is a discipline emphasizing integrative and collaborative approaches, and data sharing could facilitate integration among different ecology subdisciplines. However, the lack of semantics and unified access platform in prevalent ecological information management systems hamper the data sharing considerably. In this study, a newly developing framework to connect data on the Web, Linked Data, is proposed to be a suitable practice for exposing, sharing and connecting pieces of ecological data. The four principles built upon standard Web technologies and Resource Description Framework (RDF) make data linkable and machine-readable in the finest level of granularity, interweaving data silos into the Web of Data. The new-proposed framework for sharing ecological data is named Linked Open Data of Ecology (LODE). As a case study, five existing datasets with different but related contents was published as LODE. The data sets are forest fire database, plant and insect specimen databases established byTFRI, the Nanshi forest dynamics plot census data provided by Providence University, and the database of catalogue of life in Taiwan provided by Academia Sinica. The data publishing workflow can be summarized into 4 steps: 1) to prepare raw data and to build general data models, 2) to transform raw data into RDF data fitting models from step 1, 3) to load RDF data and models into RDF repositories with reasoners to merge data and to complete semantics,4) to publish through Linked Data interface. Using the above schema links among 5 source datasets as well as other datasets on the Web of Data could then be established. The scalability and interpretability of this data framework were demonstrated with dynamic data discovery by link traversing, federated querying multiple static datasets, exploring relationships among data instances, and advanced queries. The suitability of this new approach in ecological data sharing is then discussed. Problems on data quality, integrating taxonomic data, analysis of ecological data automatically under LODE framework using scientific workflow tools such as Kepler, and the need to improve current data models were also explored. This study concludes that Linked Data is suitable for ecological data sharing but improvement of data publishing workflow and the user interface are needed.

The concept of grid computing has permeated all areas of distributed computing, changing the way in which distributed systems are designed, developed and implemented. At the same time ‘geobrowsers’, such as Google Earth, NASA World Wind and Virtual Earth, along with in-vehicle navigation, handheld GPS devices and maps on mobile phones, have made interactive maps and geographic information an everyday experience. Behind these maps lies a wealth of spatial data that is collated from a vast number of different sources. A spatial data infrastructure (SDI) aims to make spatial data from multiple sources available to as wide an audience as possible. Current research indicates that, due to a number of reasons, data sharing in these SDIs is still not common. This dissertation presents an analysis of the data grid approach for SDIs. Starting off, two imaginary scenarios spell out for the first time how data grids can be applied to enable the sharing of address data in an SDI. The work in this dissertation spans two disciplines: Computer Science (CS) and Geographic Information Science (GISc). A study of related work reveals that the data grid approach in SDIs is both a novel application for data grids (CS), as well as a novel technology in SDI environments (GISc), and this dissertation advances mutual understanding between the two disciplines. The novel evaluation framework for national address databases in an SDI is used to evaluate existing information federation models against the data grid approach. This evaluation, as well as an analysis of address data in an SDI, confirms that there are similarities between the data grid approach and the requirement for consolidated address data in an SDI. The evaluation further shows that where a large number of organizations are involved, such as for a national address database, and where there is a lack of a single organization tasked with the management of a national address database, the data grid is an attractive alternative to other models. The Compartimos (Spanish for ‘we share’) reference model was developed to identify the components with their capabilities and relationships that are required to grid-enable address data sharing in an SDI. The definition of an address in the broader sense (i.e. not only for postal delivery), the notion of an address as a reference and the definition of an addressing system and its comparison to a spatial reference system contribute towards the understanding of what an address is. A novel address data model shows that it is possible to design a data model for sharing and exchange of address data, despite diverse addressing systems and without impacting on, or interfering with, local laws for address allocation. The analysis in this dissertation confirms the need for standardization of domain specific geographic information, such as address data, and their associated services in order to integrate data from distributed heterogeneous sources. In conclusion, results are presented and recommendations for future work, drawn from the experience on the work in this dissertation, are made.
Thesis (PhD)--University of Pretoria, 2009.
Computer Science
unrestricted

Many metropolitan cities motivate people to exploit public bike-sharing programs as alternative transportation for many reasons. Due to its’ popularity, multiple types of research on optimizing public bike-sharing systems is conducted on city-level, neighborhood-level, station-level, or user-level to predict the public bike demand. Previously, the research on the public bike demand prediction primarily focused on discovering a relationship with weather as an external factor that possibly impacted the bike usage or analyzing the bike user trend in one aspect. This work hypothesizes two external factors that are likely to affect public bike demand: weather and air pollution. This study uses a public bike data set, daily temperature, precipitation data, and air condition data to discover the trend of bike usage using multiple machine learning techniques such as Decision Tree, Naïve Bayes, and Random Forest. After conducting the research, each algorithm’s output is evaluated with performance comparisons such as accuracy, precision, or sensitivity. As a result, Random Forest is an efficient classifier for the bike demand prediction by weather and precipitation, and Decision Tree performs best for the bike demand prediction by air pollutants. Also, the three class labelings in the daily bike demand has high specificity, and is easy to trace the trend of the public bike system.

abstract: Sensitive data sharing presents many challenges in case of unauthorized disclosures, including stigma and discrimination for patients with behavioral health conditions (BHCs). Sensitive information (e.g. mental health) warrants consent-based sharing to achieve integrated care. As many patients with BHCs receive cross-organizational behavioral and physical health care, data sharing can improve care quality, patient-provider experiences, outcomes, and reduce costs. Granularity in data sharing further allows for privacy satisfaction. Though the subjectivity in information patients consider sensitive and related sharing preferences are rarely investigated. Research, federal policies, and recommendations demand a better understanding of patient perspectives of data sensitivity and sharing. The goal of this research is to enhance the understanding of data sensitivity and related sharing preferences of patients with BHCs. The hypotheses are that 1) there is a diversity in medical record sensitivity and sharing preferences of patients with BHCs concerning the type of information, information recipients, and purpose of sharing; and 2) there is a mismatch between the existing sensitive data categories and the desires of patients with BHCs. A systematic literature review on methods assessing sensitivity perspectives showed a lack of methodologies for characterizing patient perceptions of sensitivity and assessing the variations in perceptions from clinical interpretations. Novel informatics approaches were proposed and applied using patients’ medical records to assess data sensitivity, sharing perspectives and comparing those with healthcare providers’ views. Findings showed variations in perceived sensitivity and sharing preferences. Patients’ sensitivity perspectives often varied from standard clinical interpretations. Comparison of patients’ and providers’ views on data sensitivity found differences in sensitivity perceptions of patients. Patients’ experiences (family history as genetic data), stigma towards category definitions or labels (drug “abuse”), and self-perceptions of information applicability (alcohol dependency) were influential factors in patients’ sensitivity determination. This clinical informatics research innovation introduces new methods using medical records to study data sensitivity and sharing. The outcomes of this research can guide the development of effective data sharing consent processes, education materials to inform patients and providers, granular technologies segmenting electronic health data, and policies and recommendations on sensitive data sharing.
Dissertation/Thesis
Doctoral Dissertation Biomedical Informatics 2020