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

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

On exploiting social relationship and personal background for content discovery in P2P networks

International audienceContent discovery is a critical issue in unstructured Peer-to-Peer (P2P) networks as nodes maintain only local network information. However, similarly without global information about human networks, one still can find specific persons via his/her friends by using social information. Therefore, in this paper, we investigate the problem of how social information (i.e., friends and background information) could benefit content discovery in P2P networks. We collect social information of 384, 494 user profiles from Facebook, and build a social P2P network model based on the empirical analysis. In this model, we enrich nodes in P2P networks with social information and link nodes via their friendships. Each node extracts two types of social features-Knowledge and Similarity-and assigns more weight to the friends that have higher similarity and more knowledge. Furthermore, we present a novel content discovery algorithm which can explore the latent relationships among a node's friends. A node computes stable scores for all its friends regarding their weight and the latent relationships. It then selects the top friends with higher scores to query content. Extensive experiments validate performance of the proposed mechanism. In particular, for personal interests searching, the proposed mechanism can achieve 100% of Search Success Rate by selecting the top 20 friends within two-hop. It also achieves 6.5 Hits on average, which improves 8x the performance of the compared methods

On exploiting social relationship and personal background for content discovery in P2P networks

Content discovery is a critical issue in unstructured Peer-to-Peer (P2P) networks as nodes maintain only local network information. However, similarly without global information about human networks, one still can find specific persons via his/her friends by using social information. Therefore, in this paper, we investigate the problem of how social information (i.e., friends and background information) could benefit content discovery in P2P networks. We collect social information of 384,494 user profiles from Facebook, and build a social P2P network model based on the empirical analysis. In this model, we enrich nodes in P2P networks with social information and link nodes via their friendships. Each node extracts two types of social features – Knowledge and Similarity – and assigns more weight to the friends that have higher similarity and more knowledge. Furthermore, we present a novel content discovery algorithm which can explore the latent relationships among a node’s friends. A node computes stable scores for all its friends regarding their weight and the latent relationships. It then selects the top friends with higher scores to query content. Extensive experiments validate performance of the proposed mechanism. In particular, for personal interests searching, the proposed mechanism can achieve 100% of Search Success Rate by selecting the top 20 friends within two-hop. It also achieves 6.5 Hits on average, which improves 8x the performance of the compared methods.This work has been funded by the European Union under the project eCOUSIN (EU-FP7-318398) and the project SITAC (ITEA2-11020). It also has been partially funded by the Spanish Government through the MINEC eeCONTENT project (TEC2011-29688-C02-02)

Self-organizing peer-to-peer social networks

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2008 The Authors.Peer-to-peer (P2P) systems provide a new solution to distributed information and resource sharing because of its outstanding properties in decentralization, dynamics, flexibility, autonomy, and cooperation, summarized as DDFAC in this paper. After a detailed analysis of the current P2P literature, this paper suggests to better exploit peer social relationships and peer autonomy to achieve efficient P2P structure design. Accordingly, this paper proposes Self-organizing peer-to-peer social networks (SoPPSoNs) to self-organize distributed peers in a decentralized way, in which neuron-like agents following extended Hebbian rules found in the brain activity represent peers to discover useful peer connections. The self-organized networks capture social associations of peers in resource sharing, and hence are called P2P social networks. SoPPSoNs have improved search speed and success rate as peer social networks are correctly formed. This has been verified through tests on real data collected from the Gnutella system. Analysis on the Gnutella data has verified that social associations of peers in reality are directed, asymmetric and weighted, validating the design of SoPPSoN. The tests presented in this paper have also evaluated the scalability of SoPPSoN, its performance under varied initial network connectivity and the effects of different learning rules.National Natural Science of Foundation of Chin

Context-Aware Recommendation Systems in Mobile Environments

Nowadays, the huge amount of information available may easily overwhelm users when they need to take a decision that involves choosing among several options. As a solution to this problem, Recommendation Systems (RS) have emerged to offer relevant items to users. The main goal of these systems is to recommend certain items based on user preferences. Unfortunately, traditional recommendation systems do not consider the user’s context as an important dimension to ensure high-quality recommendations. Motivated by the need to incorporate contextual information during the recommendation process, Context-Aware Recommendation Systems (CARS) have emerged. However, these recent recommendation systems are not designed with mobile users in mind, where the context and the movements of the users and items may be important factors to consider when deciding which items should be recommended. Therefore, context-aware recommendation models should be able to effectively and efficiently exploit the dynamic context of the mobile user in order to offer her/him suitable recommendations and keep them up-to-date.The research area of this thesis belongs to the fields of context-aware recommendation systems and mobile computing. We focus on the following scientific problem: how could we facilitate the development of context-aware recommendation systems in mobile environments to provide users with relevant recommendations? This work is motivated by the lack of generic and flexible context-aware recommendation frameworks that consider aspects related to mobile users and mobile computing. In order to solve the identified problem, we pursue the following general goal: the design and implementation of a context-aware recommendation framework for mobile computing environments that facilitates the development of context-aware recommendation applications for mobile users. In the thesis, we contribute to bridge the gap not only between recommendation systems and context-aware computing, but also between CARS and mobile computing.<br /

Evaluation of clustering techniques for efficient searching in JXTA-based P2P systems

The efficient file searching is an essential feature in P2P systems. While many current approaches use brute force techniques to search files by meta information (file names, extensions or user-provided tags), the interest is in implementing techniques that allow content-based search in P2P systems. Recently, clustering techniques have been used for searching text documents to increase the efficiency of document discovery and retrieval. Integrating such techniques into P2P systems is important toenhance searching in P2P file sharing systems. While some effort has been done for content-based searching for text documents in P2P systems, there has been few research work for applying these techniques for multimedia content in P2P systems. In this paper we introduce two P2P content-based clustering techniques for multimedia documents. These techniques are an adaptation of the existing Class-based Semantic Search (CSS) algorithm for text documents. The proposed algorithms have been integrated into a JXTA-based Overlay P2P platform, and some initial evaluation results are provided. The JXTA-Overlay together with the considered clustering techniques is thus very useful for developing P2P multimedia applications requiring efficient searching of multimedia contents in peer nodesPeer ReviewedPostprint (published version

Distributed collaborative structuring

Making Inter- and Intranet resources available in a structured way is one of the most important and challenging problems today. An underlying structure allows users to search for information, documents or relationships without a clearly defined information need. While search and filtering technology is becoming more and more powerful, the development of such explorative access methods lacks behind. This work is concerned with the development of large-scale data mining methods that allow to structure information spaces based on loosely coupled user annotations and navigation patterns. An essential challenge, that was not yet fully realized in this context, is heterogeneity. Different users and user groups often have different preferences and needs on how to access an information collection. While current Business Intelligence, Information Retrieval or Content Management solutions allow for a certain degree of personalization, these approaches are still very static. This considerably limits their applicability in heterogeneous environments. This work is based on a novel paradigm, called collaborative structuring. This term is chosen as a generalization to the term collaborative filtering. Instead of only filtering items, collaborative structuring allows users to organize information spaces in a loosely coupled way, based on patterns emerging through data mining. A first contribution of the work is to define the conceptual notion of collaborative structuring as combinatorial optimization problem and to put it into relation with existing research in the areas of data and web mining. As second contribution, highly scalable, distributed optimization strategies are proposed and analyzed. Finally, the proposed approaches are quantitatively evaluated against existing methods using several real-world data sets. Also, practical experience from two application areas is given, namely information access for heterogeneous expert communities and collaborative media organization

Using two-tier bitwise interest oriented QRP data structure for high clustering through interest similarity in SWB P2P systems

The concept of “Small-World” paradigm has been used by many peer-to-peer (P2P) systems to achieve high clustering and low number of hops to arrive to its desired target. This approach intends to improve performance of P2P systems. The Small world concept requires that the architecture of a P2P system to achieve high cluster coefficient and low average hop between any two randomly chosen peers. In this research, we propose an enhanced hierarchical overlay network by incorporating the concept of Small world into the base hierarchical architecture. This research adopts the Query Routing Protocol (QRP) data structure of a hierarchical P2P network by storing the interest information of files in the leaf peers in the network. The QRP in the leaf peers will be aggregated and propagated to the SuperPeer so that interest information could be used to form short-range, medium-range and long-range links with other SuperPeers to achieve low average hop. As peers join and leave the network, a proposed rewiring protocol is used to ensure peers are clustered by interest to form high clustering coefficient so that search activities are yielding higher relevance results in a more predictable fashion. We simulated the porposed small world P2P using test scenarios to evaluate recall rate of the small world P2P system. Our simulated results evaluate and benchmark our proposed Small-World Bee (SWB) overlay network with iCluster [3], Firework Query Model [5] and Limewire [1] to assess performance obtained and goals achieved in the research work so that future research directions could be planned