Evaluation of unidirectional background push content download services for the delivery of television programs

Este trabajo de tesis presenta los servicios de descarga de contenido en modo push como un mecanismo eficiente para el envío de contenido de televisión pre-producido sobre redes de difusión. Hoy en día, los operadores de red dedican una cantidad considerable de recursos de red a la entrega en vivo de contenido televisivo, tanto sobre redes de difusión como sobre conexiones unidireccionales. Esta oferta de servicios responde únicamente a requisitos comerciales: disponer de los contenidos televisivos en cualquier momento y lugar. Sin embargo, desde un punto de vista estrictamente académico, el envío en vivo es únicamente un requerimiento para el contenido en vivo, no para contenidos que ya han sido producidos con anterioridad a su emisión. Más aún, la difusión es solo eficiente cuando el contenido es suficientemente popular. Los servicios bajo estudio en esta tesis utilizan capacidad residual en redes de difusión para enviar contenido pre-producido para que se almacene en los equipos de usuario. La propuesta se justifica únicamente por su eficiencia. Por un lado, genera valor de recursos de red que no se aprovecharían de otra manera. Por otro lado, realiza la entrega de contenidos pre-producidos y populares de la manera más eficiente: sobre servicios de descarga de contenidos en difusión. Los resultados incluyen modelos para la popularidad y la duración de contenidos, valiosos para cualquier trabajo de investigación basados en la entrega de contenidos televisivos. Además, la tesis evalúa la capacidad residual disponible en redes de difusión, por medio de estudios empíricos. Después, estos resultados son utilizados en simulaciones que evalúan las prestaciones de los servicios propuestos en escenarios diferentes y para aplicaciones diferentes. La evaluación demuestra que este tipo de servicios son un recurso muy útil para la entrega de contenido televisivo.This thesis dissertation presents background push Content Download Services as an efficient mechanism to deliver pre-produced television content through existing broadcast networks. Nowadays, network operators dedicate a considerable amount of network resources to live streaming live, through both broadcast and unicast connections. This service offering responds solely to commercial requirements: Content must be available anytime and anywhere. However, from a strictly academic point of view, live streaming is only a requirement for live content and not for pre-produced content. Moreover, broadcasting is only efficient when the content is sufficiently popular. The services under study in this thesis use residual capacity in broadcast networks to push popular, pre-produced content to storage capacity in customer premises equipment. The proposal responds only to efficiency requirements. On one hand, it creates value from network resources otherwise unused. On the other hand, it delivers popular pre-produced content in the most efficient way: through broadcast download services. The results include models for the popularity and the duration of television content, valuable for any research work dealing with file-based delivery of television content. Later, the thesis evaluates the residual capacity available in broadcast networks through empirical studies. These results are used in simulations to evaluate the performance of background push content download services in different scenarios and for different applications. The evaluation proves that this kind of services can become a great asset for the delivery of television contentFraile Gil, F. (2013). Evaluation of unidirectional background push content download services for the delivery of television programs [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/31656TESI

Popular Content Distribution in Public Transportation Using Artificial Intelligence Techniques

Outdoor wireless networks suffer nowadays from an increasing data traffic demand which comes at the time where almost no vacant frequency spectrum has been left. A vast majority of this demand comes from popular content generated by video streaming and social media sites. In the future, other sources will generate even more demand with emerging applications such as virtual reality, connected cars and environmental sensing. While a significant progress has been made to address this network saturation in indoor environments, current outdoor solutions, based on fixed network deployments, are expensive to build and maintain. They tend to be immobile and therefore are inflexible in coping with the dynamics of outdoor data demand. On the other hand, Vehicular Ad-hoc NETworks (VANETs) are in nature more scalable, dynamic, flexible, and therefore more promising in terms of addressing such demand. This is especially feasible if we take advantage of public transportation vehicles and stops. These vehicles and stops are often owned and operated by the same administrative entity which overcomes the routing selfishness issue. Moreover, the mobility patterns of these vehicles are highly predictable given their regular schedules; their locations are publicly-sharable and their location distribution is uniform throughout space and time. Given these factors, a system that utilizes public transportation vehicles and stops to build a reliable, scalable and dynamic VANET for wireless network offloading in outdoor environments is proposed. This is done by exploiting the predictability demonstrated by such vehicles using an Artificial-Intelligence (AI) based system for wireless network offloading via popular content distribution. The AI techniques used are the Upper Popularity Bound (UPB) collaborative and group-based recommender based on multi-armed bandits for content recommendation and bayesian optimization based on batch-based Random Forest (RF) regression for content routing. They are used after analyzing the mobility data of public transportation vehicles and stops. This analysis includes both preprocessing and processing the data in order to select the optimal set of stops and clustering vehicles and stops based on cumulative contact duration thresholds. The final system has shown the promising networking potential of public transportation. It incorporates a recommender that has shown a versatile performance under different consumer interest and network capacity scenarios. It has also demonstrated a superior performance using a bayesian optimization technique that offloads as high as 95% of the wireless network load in an interference and collision free manner

Contextual Social Networking

The thesis centers around the multi-faceted research question of how contexts may be detected and derived that can be used for new context aware Social Networking services and for improving the usefulness of existing Social Networking services, giving rise to the notion of Contextual Social Networking. In a first foundational part, we characterize the closely related fields of Contextual-, Mobile-, and Decentralized Social Networking using different methods and focusing on different detailed aspects. A second part focuses on the question of how short-term and long-term social contexts as especially interesting forms of context for Social Networking may be derived. We focus on NLP based methods for the characterization of social relations as a typical form of long-term social contexts and on Mobile Social Signal Processing methods for deriving short-term social contexts on the basis of geometry of interaction and audio. We furthermore investigate, how personal social agents may combine such social context elements on various levels of abstraction. The third part discusses new and improved context aware Social Networking service concepts. We investigate special forms of awareness services, new forms of social information retrieval, social recommender systems, context aware privacy concepts and services and platforms supporting Open Innovation and creative processes. This version of the thesis does not contain the included publications because of copyrights of the journals etc. Contact in terms of the version with all included publications: Georg Groh, [email protected] zentrale Gegenstand der vorliegenden Arbeit ist die vielschichtige Frage, wie Kontexte detektiert und abgeleitet werden können, die dazu dienen können, neuartige kontextbewusste Social Networking Dienste zu schaffen und bestehende Dienste in ihrem Nutzwert zu verbessern. Die (noch nicht abgeschlossene) erfolgreiche Umsetzung dieses Programmes führt auf ein Konzept, das man als Contextual Social Networking bezeichnen kann. In einem grundlegenden ersten Teil werden die eng zusammenhängenden Gebiete Contextual Social Networking, Mobile Social Networking und Decentralized Social Networking mit verschiedenen Methoden und unter Fokussierung auf verschiedene Detail-Aspekte näher beleuchtet und in Zusammenhang gesetzt. Ein zweiter Teil behandelt die Frage, wie soziale Kurzzeit- und Langzeit-Kontexte als für das Social Networking besonders interessante Formen von Kontext gemessen und abgeleitet werden können. Ein Fokus liegt hierbei auf NLP Methoden zur Charakterisierung sozialer Beziehungen als einer typischen Form von sozialem Langzeit-Kontext. Ein weiterer Schwerpunkt liegt auf Methoden aus dem Mobile Social Signal Processing zur Ableitung sinnvoller sozialer Kurzzeit-Kontexte auf der Basis von Interaktionsgeometrien und Audio-Daten. Es wird ferner untersucht, wie persönliche soziale Agenten Kontext-Elemente verschiedener Abstraktionsgrade miteinander kombinieren können. Der dritte Teil behandelt neuartige und verbesserte Konzepte für kontextbewusste Social Networking Dienste. Es werden spezielle Formen von Awareness Diensten, neue Formen von sozialem Information Retrieval, Konzepte für kontextbewusstes Privacy Management und Dienste und Plattformen zur Unterstützung von Open Innovation und Kreativität untersucht und vorgestellt. Diese Version der Habilitationsschrift enthält die inkludierten Publikationen zurVermeidung von Copyright-Verletzungen auf Seiten der Journals u.a. nicht. Kontakt in Bezug auf die Version mit allen inkludierten Publikationen: Georg Groh, [email protected]

Evaluating collaborative filtering over time

Recommender systems have become essential tools for users to navigate the plethora of content in the online world. Collaborative filtering—a broad term referring to the use of a variety, or combination, of machine learning algorithms operating on user ratings—lies at the heart of recommender systems’ success. These algorithms have been traditionally studied from the point of view of how well they can predict users’ ratings and how precisely they rank content; state of the art approaches are continuously improved in these respects. However, a rift has grown between how filtering algorithms are investigated and how they will operate when deployed in real systems. Deployed systems will continuously be queried for personalised recommendations; in practice, this implies that system administrators will iteratively retrain their algorithms in order to include the latest ratings. Collaborative filtering research does not take this into account: algorithms are improved and compared to each other from a static viewpoint, while they will be ultimately deployed in a dynamic setting. Given this scenario, two new problems emerge: current filtering algorithms are neither (a) designed nor (b) evaluated as algorithms that must account for time. This thesis addresses the divergence between research and practice by examining how collaborative filtering algorithms behave over time. Our contributions include: 1. A fine grained analysis of temporal changes in rating data and user/item similarity graphs that clearly demonstrates how recommender system data is dynamic and constantly changing. 2. A novel methodology and time-based metrics for evaluating collaborative filtering over time, both in terms of accuracy and the diversity of top-N recommendations. 3. A set of hybrid algorithms that improve collaborative filtering in a range of different scenarios. These include temporal-switching algorithms that aim to promote either accuracy or diversity; parameter update methods to improve temporal accuracy; and re-ranking a subset of users’ recommendations in order to increase diversity. 4. A set of temporal monitors that secure collaborative filtering from a wide range of different temporal attacks by flagging anomalous rating patterns. We have implemented and extensively evaluated the above using large-scale sets of user ratings; we further discuss how this novel methodology provides insight into dimensions of recommender systems that were previously unexplored. We conclude that investigating collaborative filtering from a temporal perspective is not only more suitable to the context in which recommender systems are deployed, but also opens a number of future research opportunities

A generic approach to the evolution of interaction in ubiquitous systems

This dissertation addresses the challenge of the configuration of modern (ubiquitous, context-sensitive, mobile et al.) interactive systems where it is difficult or impossible to predict (i) the resources available for evolution, (ii) the criteria for judging the success of the evolution, and (iii) the degree to which human judgements must be involved in the evaluation process used to determine the configuration. In this thesis a conceptual model of interactive system configuration over time (known as interaction evolution) is presented which relies upon the follow steps; (i) identification of opportunities for change in a system, (ii) reflection on the available configuration alternatives, (iii) decision-making and (iv) implementation, and finally iteration of the process. This conceptual model underpins the development of a dynamic evolution environment based on a notion of configuration evaluation functions (hereafter referred to as evaluation functions) that provides greater flexibility than current solutions and, when supported by appropriate tools, can provide a richer set of evaluation techniques and features that are difficult or impossible to implement in current systems. Specifically this approach has support for changes to the approach, style or mode of use used for configuration - these features may result in more effective systems, less effort involved to configure them and a greater degree of control may be offered to the user. The contributions of this work include; (i) establishing the the need for configuration evolution through a literature review and a motivating case study experiment, (ii) development of a conceptual process model supporting interaction evolution, (iii) development of a model based on the notion of evaluation functions which is shown to support a wide range of interaction configuration approaches, (iv) a characterisation of the configuration evaluation space, followed by (v) an implementation of these ideas used in (vi) a series of longitudinal technology probes and investigations into the approaches

Bayesian Network Trust Model for Certificate Revocation in Adhoc Wireless Networks

An Ad hoc wireless network has no infrastructure. It is formed dynamically by a group of moving nodes. There is no backbone or central point of communication. The media of communication is a shared wireless channel; hence extruders can easily penetrate through such a network. A distributed trust model is therefore employed to authenticate the nodes communicating in the network. Whenever a node observes a malicious activity, it floods an accusation in the network which is recorded in a certificate revocation list maintained in the local environment of each node in the network. The certificates for the nodes in the network are renewed if there is no entry against the node in the list. Hence, malicious nodes are removed from the network. However, this scheme has several drawbacks, the most important one being the removal of innocent nodes due to wrong accusations. This work proposes a Bayesian network trust model for certificate revocation. This will establish trust relations among the nodes to overcome the problems faced by the distributed trust model. The ad hoc network is modeled using the Random waypoint mobility model. The two trust models are analyzed and the performance is compared. The proposed trust model outperformed the distributed trust model in identifying and removing the malicious nodes as well as protecting the innocent nodes form malicious accusations against them. Furthermore, in the proposed approach, the performance was high in terms of availability and quality of service. All these were achieved due to proper trust relations among the nodes in the network. Malicious attacks including Hijacking, DoS, spoofing and Time delay were simulated. The proposed model performed much better than the distributed trust model in revoking the certificates of malicious nodes and hence removing them from the network. The trust relationships lead to better security and performance in the network.Computer Science Departmen

Proceedings of the ECMLPKDD 2015 Doctoral Consortium

ECMLPKDD 2015 Doctoral Consortium was organized for the second time as part of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), organised in Porto during September 7-11, 2015. The objective of the doctoral consortium is to provide an environment for students to exchange their ideas and experiences with peers in an interactive atmosphere and to get constructive feedback from senior researchers in machine learning, data mining, and related areas. These proceedings collect together and document all the contributions of the ECMLPKDD 2015 Doctoral Consortium