Content Discovery Research Articles

PanDelos: a dictionary-based method for pan-genome content discovery

BackgroundPan-genome approaches afford the discovery of homology relations in a set of genomes, by determining how some gene families are distributed among a given set of genomes. The retrieval of a complete gene distribution among a class of genomes is an NP-hard problem because computational costs increase with the number of analyzed genomes, in fact, all-against-all gene comparisons are required to completely solve the problem. In presence of phylogenetically distant genomes, due to the variability introduced in gene duplication and transmission, the task of recognizing homologous genes becomes even more difficult. A challenge on this field is that of designing fast and adaptive similarity measures in order to find a suitable pan-genome structure of homology relations.ResultsWe present PanDelos, a stand alone tool for the discovery of pan-genome contents among phylogenetic distant genomes. The methodology is based on information theory and network analysis. It is parameter-free because thresholds are automatically deduced from the context. PanDelos avoids sequence alignment by introducing a measure based on k-mer multiplicity. The k-mer length is defined according to general arguments rather than empirical considerations. Homology candidate relations are integrated into a global network and groups of homologous genes are extracted by applying a community detection algorithm.ConclusionsPanDelos outperforms existing approaches, Roary and EDGAR, in terms of running times and quality content discovery. Tests were run on collections of real genomes, previously used in analogous studies, and in synthetic benchmarks that represent fully trusted golden truth. The software is available at https://github.com/GiugnoLab/PanDelos.

Pictures tell a story: antecedents of rich-media curation in social network sites

ABSTRACTThis study explores a new aspect of online social networking, in which textual information has been largely replaced by the curation of rich-media content. We examine these ‘curation-based’ networks (CBN) and develop a theoretical model of the antecedents of intentions to curate rich-media content. Results from the empirical analysis of 449 users of Pinterest, a popular CBN, show that users undertake curation tasks to achieve objectives around the management, discovery, and presentation of rich-media content. The findings have implications for researchers and practitioners interested in the changing nature of online social networks and the role of rich-media in online communication.

Machine-to-Machine Content Retrieval in Wireless Networks

Machine-to-Machine (M2M) communication is a distinct capability that enables communications with anything. With the expected connection among several tens of billions of addressable mobile devices and the exchange of a huge amount of content, the Internet and mobile communication are entering a new era that inevitably faces several challenges, especially from a content dissemination perspective. It has been exposed to many limitations such as high control overhead, frequent location updates, and network congestion. To resolve such limitations, this paper proposes an efficient hierarchical routing and content retrieval mechanism, which is called Hierarchical Routing (H-Routing) for the wireless network based on Content-Centric Networking (CCN). The H-Routing is composed of a novel content discovery approach and a common service platform for generic communications and the integration of heterogeneous devices. The proposed mechanism uses CCN and Chord-like lookup in a hierarchical M2M system in the wireless mobile environment. The H-Routing mechanism reduces unnecessary flooding of content request messages, control overhead, and the waste of resources. We also implemented the proposed mechanism in the simulation environment and analyze the performance in terms of throughput, Interest packet overhead, and Interest error ratio.

Big Data Deliverables through Cross-Platform Interest-Based content discovery

Big Data Deliverables through Cross-Platform Interest-Based content discovery

Understanding Scoped-Flooding for Content Discovery and Caching in Content Networks

Scoped-flooding is used for content discovery in a broad networking context and it has significant impact on the design of caching algorithms in a communication network. Despite its wide usage, a thorough analysis on how scoped-flooding affects a network’s performance, e.g., caching and content discovery efficiency, is missing. To develop a better understanding, we first model the behavior of scoped-flooding by the help of a theoretical model on network growth and utility. Next, we investigate the effects of scoped-flooding on various topologies in information-centric networks (ICNs). Using the proposed ring model, we show that flooding can be constrained within a small neighborhood to achieve most of the gains which come from areas with relatively low growth rate, i.e., the network edge. We also study two flooding strategies and compare their behaviors. Given that caching schemes favor more popular items in competition for cache space, popular items are expected to be stored in diverse parts of the network compared to the less popular items. We propose to exploit the resulting divergence in availability along with the routers’ topological properties to fine tune the flooding radius. Our results shed light on designing both efficient content discovery mechanism and effective caching algorithms for future ICN.

A Method of Content Discovery in CDN

The CDN management system distributes content objects to the edge of the Internet to achieve the user’s near access. The content discovery is an important issue in content delivery network. The content t discovery algorithm is used to find the location of the content in the content distribution network. In this paper, we design a content resource discovery algorithm Global-hop, which optimizes the propagation process of topology change event messages. The events that cause the topology change are divided into two categories: one is the increase of nodes, and the other is the reduction of nodes. The reduction of nodes is divided into node withdrawal and node failure. Different from One-hop, the broadcast path of topology change event message in the Global-hop algorithm is tree-like broadcast. The broadcast topological information in the original algorithm divided nodes into several Slice, and each Slice was divided into several Unit. Each Slice has a Slice Leader, and each Unit also has a Unit Leader with other nodes being ordinary nodes. The message propagation delay of Global-hop algorithm is less than that of One-hop algorithm.

Addressed Query Gossip Resource Discovery Protocol for Mobile P2P Networks and Its Performance in Diverse Mobility Models

This article describes how P2P resource discovery protocols perform poorly over mobile ad hoc networks mainly due to the frequent network dynamics. Peer-to-Peer search techniques including structured and unstructured can be employed over MANETs. Empirical studies indicate that searching in such resultant networks are not efficient and effective due to peer discovery, connectivity and mobility issues. The author proposes Addressed Query Gossip Resource Discovery algorithm, a lightweight resource discovery designed to suit the mobility requirements of ad hoc networks to optimize the search performance while at the same time minimize the extra usage of mobile and network resources. Mobility models represent the movements of mobile nodes. Such models are used to represent how the location, velocity and acceleration change over time. The author conducts performance analyses of the proposed protocol and widely used unstructured search techniques over MANET under 2 realistic mobility models, i.e., Random Waypoint and Reference Point Group Mobility models. The results show that the proposed protocol has the best performance in almost every measured metrics. In addition, the protocol consumes less energy, has less message overhead and good success ratio for random waypoint movement as compared to the reference group mobility model. After extensive evaluations, the simulation results clarify that the mobility models have a significant impact on the performance of peer-to-peer content discovery protocols over mobile ad hoc networks.

Automated Anatomic Labeling Architecture for Content Discovery in Medical Imaging Repositories.

The combination of textual data with visual features is known to enhance medical image search capabilities. However, the most advanced imaging archives today only index the studies' available meta-data, often containing limited amounts of clinically useful information. This work proposes an anatomic labeling architecture, integrated with an open source archive software, for improved multimodal content discovery in real-world medical imaging repositories. The proposed solution includes a technical specification for classifiers in an extensible medical imaging archive, a classification database for querying over the extracted information, and a set of proof-of-concept convolutional neural network classifiers for identifying the presence of organs in computed tomography scans. The system automatically extracts the anatomic region features, which are saved in the proposed database for later consumption by multimodal querying mechanisms. The classifiers were evaluated with cross-validation, yielding a best F1-score of 96% and an average accuracy of 97%. We expect these capabilities to become common-place in production environments in the future, as automated detection solutions improve in terms of accuracy, computational performance, and interoperability.

Interest-Aware Content Discovery in Peer-to-Peer Social Networks

With the increasing popularity and rapid development of Online Social Networks (OSNs), OSNs not only bring fundamental changes to information and communication technologies, but also make an extensive and profound impact on all aspects of our social life. Efficient content discovery is a fundamental challenge for large-scale distributed OSNs. However, the similarity between social networks and online social networks leads us to believe that the existing social theories are useful for improving the performance of social content discovery in online social networks. In this article, we propose an interest-aware social-like peer-to-peer (IASLP) model for social content discovery in OSNs by mimicking ten different social theories and strategies. In the IASLP network, network nodes with similar interests can meet, help each other, and co-operate autonomously to identify useful contents. The presented model has been evaluated and simulated in a dynamic environment with an evolving network. The experimental results show that the recall of IASLP is 20% higher than the existing method SESD while the overhead is 10% lower. The IASLP can generate higher flexibility and adaptability and achieve better performance than the existing methods.

From Editorial Obligation to Procedural Accountability: New Policy Approaches to Online Content in the Era of Information Intermediaries

**This is an early working version of a paper subsequently revised and published in Journal of Cyber Policy 3(2) at doi: 10.1080/23738871.2018.1519030 ** Like all markets, online platforms need rules. Their rules are both written, in the form of community standards, terms of use, commercial contracts and policies, and unwritten, in the code that shapes their interfaces and the algorithms that bring market participants together. When online information intermediaries govern the exchange of news, content and speech, their rules (in this broad Lessigian sense) can raise profound issues of social welfare, cybersecurity and human rights. Information intermediaries are not publishers, but their role in facilitating content discovery means their rules have inevitable ethical implications; they are not merely neutral conduits. The past two years has seen heated debate about intermediaries’ responsibilities with respect to online content. Commentators have alleged that in a wide range of areas, intermediaries’ commercial incentives are insufficient to address harmful or illegal content, and that regulation is required. Arguments for content regulation need to be assessed on a case-by-case basis, and this paper does not seek to give a general view on the desirability of regulation. However, given that tensions between private market governance and law are inevitable, and will continue to raise issues of public interest, it is important to consider possible regulatory strategies; that is the purpose of this paper.

Take your time, get it closer: content dissemination within mobile pedestrian crowds

The explosion of traffic demands in the edge of the Internet, mostly by mobile users, is putting under pressure current networking infrastructures. This is particularly acute when huge amounts of users and active wireless devices gather in reduced geographical spaces, increasing the risk of exceeding planned capacity of deployed infrastructure. This trend motivates research on edge computing, and in particular, on mechanisms to offload or address locally part of the user injected traffic at the access infrastructure, thus reducing the need of Internet requests and retrievals. This paper concentrates on the ability of mobile crowds –and corresponding access networks—to fulfill content requests originated within the mesh, with minimal intervention of the Internet infrastructure. Simple heuristics are revisited, proposed, discussed and evaluated to improve autonomous content discovery and dissemination within high-density, low-mobility crowds, by combining notions already explored for MANET routing: deliberate jittering and autonomous distance-based overlay pruning. Results over synthetic networks and real mobility traces indicate that these mechanisms improve efficiency and quality of content request discoveries, by reducing significantly collisions and increasing stability of discovered paths in dense pedestrian crowds.

Data Connectivity and Smart Group Formation in Wi-Fi Direct Multi-Group Networks

Users of device-to-device (D2D) communication need efficient content discovery mechanisms to steer their requests toward the node in their neighborhood that is most likely to satisfy them. The problem is further compounded by the lack of a central coordination entity as well as by the inherent mobility of devices, which leads to volatile topologies. In this paper, we first discuss group-based communication among non-rooted Android devices using Wi-Fi direct, a protocol recently standardized by the Wi-Fi alliance. We propose intra- and inter-group communication methodologies, which we validate through a simple testbed where content-centric routing is used. Next, we address the autonomous formation of groups with the goal of achieving efficient device resource utilization as well as full connectivity. Finally, we evaluate the performance of our group formation procedure both in simulation and in a real testbed involving Android devices in different topologies.

Digital Inclusion in Library Context: A Perspective from Users with Print Disability

ABSTRACTDigital inclusion is a strategy pursued to foster social inclusion of those who have been sidelined from the mainstream of information society due to lack of access to digital technologies and the skills to use them. Libraries have been working to close the gap by providing access to computers, the Internet, digital content, and digital literacy programs. However, research reveals a new type of divide where digitally competent people could be digitally excluded. This shows the need for a better conceptualization of access and literacy. This paper commits to exploring the concept of access to digital content from the perspective of people with print disability. It involved in-depth interviews with ten participants with print disability where the International Classification of Functioning, Disability, and Health was used as a framework for data analysis. The overall analysis shows that digital inclusion would be more meaningful if it involved providing content in alternative formats and empowering users to make informed choices. Issues such as flexibility and adaptability in content presentation and design of content discovery tools are among those discussed.

Through the Smoke: A Look At Industry Trends

Ultrahigh-definition services are poised for greater adoption and drive customer engagement, especially among sports fans. Meanwhile, consumers are turning to over-the-top (OTT) and “second screens” to broaden the scope of their viewing experiences and engage more interactively with content and communities. These evolving viewing behaviors bring about new opportunities in the area of social analytics. Broadcasting companies are also focusing on content planning, supply-chain, discovery, and production. Telcos and new-age OTT entrants are changing the content rights landscape. Change remains constant.

Adaptive Real-Time Predictive Collaborative Content Discovery and Retrieval in Mobile Disconnection Prone Networks

Emerging mobile environments motivate the need for the development of new distributed technologies which are able to support dynamic peer to peer content sharing, decrease high operating costs, and handle intermittent disconnections. In this paper, we investigate complex challenges related to the mobile disconnection tolerant discovery of content that may be stored in mobile devices and its delivery to the requesting nodes in mobile resource-constrained heterogeneous environments. We propose a new adaptive real-time predictive multi-layer caching and forwarding approach, CafRepCache, which is collaborative, resource, latency, and content aware. CafRepCache comprises multiple multi-layer complementary real-time distributed predictive heuristics which allow it to respond and adapt to time-varying network topology, dynamically changing resources, and workloads while managing complex dynamic tradeoffs between them in real time. We extensively evaluate our work against three competitive protocols across a range of metrics over three heterogeneous real-world mobility traces in the face of vastly different workloads and content popularity patterns. We show that CafRepCache consistently maintains higher cache availability, efficiency and success ratios while keeping lower delays, packet loss rates, and caching footprint compared to the three competing protocols across three traces when dynamically varying content popularity and dynamic mobility of content publishers and subscribers. We also show that the computational cost and network overheads of CafRepCache are only marginally increased compared with the other competing protocols.

Caching and Placement for In‐Network Caching in Device‐to‐Device Communications

Caching content by users constitutes a promising solution to decrease the costly transmissions with going through the base stations (BSs). To improve the performance of in‐network caching in device‐to‐device (D2D) communications, caching placement and content delivery should be jointly optimized. To this end, we jointly optimize caching decision and content discovery strategies by considering the successful content delivery in D2D links for maximizing the in‐network caching gain through D2D communications. Moreover, an in‐network caching placement problem is formulated as an integer nonlinear optimization problem. To obtain the optimal solution for the proposed problem, Lagrange dual decomposition is applied in order to reduce the complexity. Simulation results show that the proposed algorithm has a near‐optimal performance, approaching that of the exhaustive search method. Furthermore, the proposed scheme has a notable in‐network caching gain and an improvement in traffic offloading compared to that of other caching placement schemes.

WeBrowse

One of the limits of web content discovery tools, let them be recommender systems or content curation tools such as social rating, social bookmarking and other social media, is the scarcity of user input (e.g. rate, submit, share). This problem is even worse in the case of what we call communities of a place: people who study, live or work at the same place. Such people often share common interests but either do not know each other or fail to actively engage in submitting and relaying information. In this paper, we investigate the feasibility of using the aggregated clicks of entire communities of users to passively emulate a content curation service a la Reddit. To this end, we prototype and deploy WeBrowse, a content curation service based on the processing of raw HTTP logs. Evaluation based on our deployments demonstrates feasibility at scale while respecting user privacy. The majority of WeBrowse's users welcome the quality of content it promotes.

Discovering topic structures of a temporally evolving document corpus

In this paper we describe a novel framework for the discovery of the topical content of a data corpus, and the tracking of its complex structural changes across the temporal dimension. In contrast to previous work our model does not impose a prior on the rate at which documents are added to the corpus nor does it adopt the Markovian assumption which overly restricts the type of changes that the model can capture. Our key technical contribution is a framework based on (i) discretization of time into epochs, (ii) epoch-wise topic discovery using a hierarchical Dirichlet process-based model, and (iii) a temporal similarity graph which allows for the modelling of complex topic changes: emergence and disappearance, evolution, splitting, and merging. The power of the proposed framework is demonstrated on two medical literature corpora concerned with the autism spectrum disorder (ASD) and the metabolic syndrome (MetS)—both increasingly important research subjects with significant social and healthcare consequences. In addition to the collected ASD and metabolic syndrome literature corpora which we made freely available, our contribution also includes an extensive empirical analysis of the proposed framework. We describe a detailed and careful examination of the effects that our algorithms’s free parameters have on its output and discuss the significance of the findings both in the context of the practical application of our algorithm as well as in the context of the existing body of work on temporal topic analysis. Our quantitative analysis is followed by several qualitative case studies highly relevant to the current research on ASD and MetS, on which our algorithm is shown to capture well the actual developments in these fields.

A capable multimedia content discovery platform based on visual content analysis and intelligent data enrichment

A new capable content discovery platform based on multimedia data enrichment is presented in this paper. The platform, known as the IMCOP system, refers to the concept of intelligent discovery and delivery of multimedia content. Relevant state-of-the-art solutions are described in detail in the background section. The overall architecture and the main components of the IMCOP system are presented next. An original concept of Complex Multimedia Objects which extend the MPEG-7 standard to hold the processed data and bind it into content related collections is introduced. Selected results of tests illustrating how the IMCOP system performs in terms of responsiveness and stability under a particular workload are reported. Finally, IMCOP’s advantages in reference to other content discovery platforms are discussed and summed up.

Resolution-Based Content Discovery in Network of Caches: Is the Control Traffic an Issue?

As networking attempts to cleanly separate the control plane and forwarding plane abstractions, it also defines a clear interface between these two layers. An underlying network state is represented as a view to act upon in the control plane. We are interested in studying some fundamental properties of this interface, both in a general framework, and in the specific case of content routing. We try to evaluate the traffic between the two planes based on allowing a minimum level of acceptable distortion in the network state representation in the control plane. We apply our framework to content distribution, and see how we can compute the overhead of maintaining the location of content in the control plane. This is of importance to evaluate resolution-based content discovery in content-oriented network architectures: we identify scenarios where the cost of updating the control plane for content routing overwhelms the benefit of fetching the nearest copy. We also show how to minimize the cost of this overhead when associating costs to peering traffic and to internal traffic for network of caches.