Fairness in Network-Friendly Recommendations

As mobile traffic is dominated by content services (e.g., video), which typically use recommendation systems, the paradigm of network-friendly recommendations (NFR) has been proposed recently to boost the network performance by promoting content that can be efficiently delivered (e.g., cached at the edge). NFR increase the network performance, however, at the cost of being unfair towards certain contents when compared to the standard recommendations. This unfairness is a side effect of NFR that has not been studied in literature. Nevertheless, retaining fairness among contents is a key operational requirement for content providers. This paper is the first to study the fairness in NFR, and design fair-NFR. Specifically, we use a set of metrics that capture different notions of fairness, and study the unfairness created by existing NFR schemes. Our analysis reveals that NFR can be significantly unfair. We identify an inherent trade-off between the network gains achieved by NFR and the resulting unfairness, and derive bounds for this trade-off. We show that existing NFR schemes frequently operate far from the bounds, i.e., there is room for improvement. To this end, we formulate the design of Fair-NFR (i.e., NFR with fairness guarantees compared to the baseline recommendations) as a linear optimization problem. Our results show that the Fair-NFR can achieve high network gains (similar to non-fair-NFR) with little unfairness.Comment: IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), 202

A Survey of Deep Learning for Data Caching in Edge Network

The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network as well as reducing latency to access popular content. In that respect end user demand for popular content can be satisfied by proactively caching it at the network edge, i.e, at close proximity to the users. In addition to model based caching schemes learning-based edge caching optimizations has recently attracted significant attention and the aim hereafter is to capture these recent advances for both model based and data driven techniques in the area of proactive caching. This paper summarizes the utilization of deep learning for data caching in edge network. We first outline the typical research topics in content caching and formulate a taxonomy based on network hierarchical structure. Then, a number of key types of deep learning algorithms are presented, ranging from supervised learning to unsupervised learning as well as reinforcement learning. Furthermore, a comparison of state-of-the-art literature is provided from the aspects of caching topics and deep learning methods. Finally, we discuss research challenges and future directions of applying deep learning for cachin

A personalized system for scalable distribution of multimedia content in multicast wireless networks

The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-014-2139-3This paper presents a novel architecture for scalable multimedia content delivery over wireless networks. The architecture takes into account both the user preferences and context in order to provide personalized contents to each user. In this way, third-party applications filter the most appropriate contents for each client in each situation. One of the key characteristics of the proposal is the scalability, which is provided, apart from the use of filtering techniques, through the transmission in multicast networks. In this sense, content delivery is carried out by means of the FLUTE (File Delivery over Unidirectional Transport) protocol, which provides reliability in unidirectional environments through different mechanisms such as AL-FEC (Application Layer Forward Error Correction) codes, used in this paper. Another key characteristic is the context-awareness and personalization of content delivery, which is provided by means of context information, user profiles, and adaptation. The system proposed is validated through several empirical studies. Specifically, the paper presents evaluations of two types that collect objective and subjective measures. The first evaluate the efficiency of the transmission protocol, analyzing how the use of appropriate transmission parameters reduces the download time (and thus increasing the Quality of Experience), which can be minimized by using caching techniques. On the other hand, the subjective measures present a study about the user experience after testing the application and analyze the accuracy of the filtering process/strategy. Results show that using AL-FEC mechanisms produces download times until four times lower than when no protection is used. Also, results prove that there is a code rate that minimizes the download time depending on the losses and that, in general, code rates 0.7 and 0.9 provide good download times for a wide range of losses. On the other hand, subjective measures indicate a high user satisfaction (more than 80 %) and a relevant degree of accuracy of the content adaption.This work is supported in part by the Ministerio de Economia y Competitividad of the Government of Spain under project COMINN (IPT-2012-0883-430000) and by the project PAID/2012/313 from the PAID-05-12 program of the Vicerrectorado de Investigacion of the Universitat Politecnica de Valencia.De Fez Lava, I.; Gil Pascual, M.; Fons Cors, JJ.; Guerri Cebollada, JC.; Pelechano Ferragud, V. (2014). A personalized system for scalable distribution of multimedia content in multicast wireless networks. 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CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Contributions to the cornerstones of interaction in visualization: strengthening the interaction of visualization

Visualization has become an accepted means for data exploration and analysis. Although interaction is an important component of visualization approaches, current visualization research pays less attention to interaction than to aspects of the graphical representation. Therefore, the goal of this work is to strengthen the interaction side of visualization. To this end, we establish a unified view on interaction in visualization. This unified view covers four cornerstones: the data, the tasks, the technology, and the human.Visualisierung hat sich zu einem unverzichtbaren Werkzeug für die Exploration und Analyse von Daten entwickelt. Obwohl Interaktion ein wichtiger Bestandteil solcher Werkzeuge ist, wird der Interaktion in der aktuellen Visualisierungsforschung weniger Aufmerksamkeit gewidmet als Aspekten der graphischen Repräsentation. Daher ist es das Ziel dieser Arbeit, die Interaktion im Bereich der Visualisierung zu stärken. Hierzu wird eine einheitliche Sicht auf Interaktion in der Visualisierung entwickelt