35,076 research outputs found
Moving from Data-Constrained to Data-Enabled Research: Experiences and Challenges in Collecting, Validating and Analyzing Large-Scale e-Commerce Data
Widespread e-commerce activity on the Internet has led to new opportunities
to collect vast amounts of micro-level market and nonmarket data. In this paper
we share our experiences in collecting, validating, storing and analyzing large
Internet-based data sets in the area of online auctions, music file sharing and
online retailer pricing. We demonstrate how such data can advance knowledge by
facilitating sharper and more extensive tests of existing theories and by
offering observational underpinnings for the development of new theories. Just
as experimental economics pushed the frontiers of economic thought by enabling
the testing of numerous theories of economic behavior in the environment of a
controlled laboratory, we believe that observing, often over extended periods
of time, real-world agents participating in market and nonmarket activity on
the Internet can lead us to develop and test a variety of new theories.
Internet data gathering is not controlled experimentation. We cannot randomly
assign participants to treatments or determine event orderings. Internet data
gathering does offer potentially large data sets with repeated observation of
individual choices and action. In addition, the automated data collection holds
promise for greatly reduced cost per observation. Our methods rely on
technological advances in automated data collection agents. Significant
challenges remain in developing appropriate sampling techniques integrating
data from heterogeneous sources in a variety of formats, constructing
generalizable processes and understanding legal constraints. Despite these
challenges, the early evidence from those who have harvested and analyzed large
amounts of e-commerce data points toward a significant leap in our ability to
understand the functioning of electronic commerce.Comment: Published at http://dx.doi.org/10.1214/088342306000000231 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
A Comparison of Big Data Frameworks on a Layered Dataflow Model
In the world of Big Data analytics, there is a series of tools aiming at
simplifying programming applications to be executed on clusters. Although each
tool claims to provide better programming, data and execution models, for which
only informal (and often confusing) semantics is generally provided, all share
a common underlying model, namely, the Dataflow model. The Dataflow model we
propose shows how various tools share the same expressiveness at different
levels of abstraction. The contribution of this work is twofold: first, we show
that the proposed model is (at least) as general as existing batch and
streaming frameworks (e.g., Spark, Flink, Storm), thus making it easier to
understand high-level data-processing applications written in such frameworks.
Second, we provide a layered model that can represent tools and applications
following the Dataflow paradigm and we show how the analyzed tools fit in each
level.Comment: 19 pages, 6 figures, 2 tables, In Proc. of the 9th Intl Symposium on
High-Level Parallel Programming and Applications (HLPP), July 4-5 2016,
Muenster, German
MOSDEN: A Scalable Mobile Collaborative Platform for Opportunistic Sensing Applications
Mobile smartphones along with embedded sensors have become an efficient
enabler for various mobile applications including opportunistic sensing. The
hi-tech advances in smartphones are opening up a world of possibilities. This
paper proposes a mobile collaborative platform called MOSDEN that enables and
supports opportunistic sensing at run time. MOSDEN captures and shares sensor
data across multiple apps, smartphones and users. MOSDEN supports the emerging
trend of separating sensors from application-specific processing, storing and
sharing. MOSDEN promotes reuse and re-purposing of sensor data hence reducing
the efforts in developing novel opportunistic sensing applications. MOSDEN has
been implemented on Android-based smartphones and tablets. Experimental
evaluations validate the scalability and energy efficiency of MOSDEN and its
suitability towards real world applications. The results of evaluation and
lessons learned are presented and discussed in this paper.Comment: Accepted to be published in Transactions on Collaborative Computing,
2014. arXiv admin note: substantial text overlap with arXiv:1310.405
NOMA based resource allocation and mobility enhancement framework for IoT in next generation cellular networks
With the unprecedented technological advances witnessed in the last two decades, more devices are connected to the internet, forming what is called internet of things (IoT). IoT devices with heterogeneous characteristics and quality of experience (QoE) requirements may engage in dynamic spectrum market due to scarcity of radio resources. We propose a framework to efficiently quantify and supply radio resources to the IoT devices by developing intelligent systems. The primary goal of the paper is to study the characteristics of the next generation of cellular networks with non-orthogonal multiple access (NOMA) to enable connectivity to clustered IoT devices. First, we demonstrate how the distribution and QoE requirements of IoT devices impact the required number of radio resources in real time. Second, we prove that using an extended auction algorithm by implementing a series of complementary functions, enhance the radio resource utilization efficiency. The results show substantial reduction in the number of sub-carriers required when compared to conventional orthogonal multiple access (OMA) and the intelligent clustering is scalable and adaptable to the cellular environment. Ability to move spectrum usages from one cluster to other clusters after borrowing when a cluster has less user or move out of the boundary is another soft feature that contributes to the reported radio resource utilization efficiency. Moreover, the proposed framework provides IoT service providers cost estimation to control their spectrum acquisition to achieve required quality of service (QoS) with guaranteed bit rate (GBR) and non-guaranteed bit rate (Non-GBR)
Benefits and challenges of applying Semantic Web Services in the e-Government domain
Joining up services in e-Government usually implies governmental agencies acting in concert without a central control regime. This requires the sharing of scattered and heterogeneous data. Semantic Web Service (SWS) technology can help to integrate, mediate and reason between these datasets. However, since few real-world applications have been developed, it is still unclear which are the actual benefits and issues of adopting such a technology in the e-Government domain. In this paper, we contribute to raising awareness of the potential benefits in the e-Government community by analyzing motivations, requirements, and expected results, before proposing a reusable SWS-based framework. We demonstrate the application of this framework by a compelling use case: a GIS-based emergency planning system. We illustrate the obtained benefits and the key challenges which remain to be addressed
WikiSensing: A collaborative sensor management system with trust assessment for big data
Big Data for sensor networks and collaborative systems have become ever more important in the digital economy and is a focal point of technological interest while posing many noteworthy challenges. This research addresses some of the challenges in the areas of online collaboration and Big Data for sensor networks.
This research demonstrates WikiSensing (www.wikisensing.org), a high performance, heterogeneous, collaborative data cloud for managing and analysis of real-time sensor data. The system is based on the Big Data architecture with comprehensive functionalities for smart city sensor data integration and analysis. The system is fully functional and served as the main data management platform for the 2013 UPLondon Hackathon.
This system is unique as it introduced a novel methodology that incorporates online collaboration with sensor data. While there are other platforms available for sensor data management WikiSensing is one of the first platforms that enable online collaboration by providing services to store and query dynamic sensor information without any restriction of the type and format of sensor data.
An emerging challenge of collaborative sensor systems is modelling and assessing the trustworthiness of sensors and their measurements. This is with direct relevance to WikiSensing as an open collaborative sensor data management system. Thus if the trustworthiness of the sensor data can be accurately assessed, WikiSensing will be more than just a collaborative data management system for sensor but also a platform that provides information to the users on the validity of its data. Hence this research presents a new generic framework for capturing and analysing sensor trustworthiness considering the different forms of evidence available to the user. It uses an extensible set of metrics that can represent such evidence and use Bayesian analysis to develop a trust classification model.
Based on this work there are several publications and others are at the final stage of submission. Further improvement is also planned to make the platform serve as a cloud service accessible to any online user to build up a community of collaborators for smart city research.Open Acces
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Multimedia delivery in the future internet
The term “Networked Media” implies that all kinds of media including text, image, 3D graphics, audio
and video are produced, distributed, shared, managed and consumed on-line through various networks,
like the Internet, Fiber, WiFi, WiMAX, GPRS, 3G and so on, in a convergent manner [1]. This white
paper is the contribution of the Media Delivery Platform (MDP) cluster and aims to cover the Networked
challenges of the Networked Media in the transition to the Future of the Internet.
Internet has evolved and changed the way we work and live. End users of the Internet have been confronted
with a bewildering range of media, services and applications and of technological innovations concerning
media formats, wireless networks, terminal types and capabilities. And there is little evidence that the pace
of this innovation is slowing. Today, over one billion of users access the Internet on regular basis, more
than 100 million users have downloaded at least one (multi)media file and over 47 millions of them do so
regularly, searching in more than 160 Exabytes1 of content. In the near future these numbers are expected
to exponentially rise. It is expected that the Internet content will be increased by at least a factor of 6, rising
to more than 990 Exabytes before 2012, fuelled mainly by the users themselves. Moreover, it is envisaged
that in a near- to mid-term future, the Internet will provide the means to share and distribute (new)
multimedia content and services with superior quality and striking flexibility, in a trusted and personalized
way, improving citizens’ quality of life, working conditions, edutainment and safety.
In this evolving environment, new transport protocols, new multimedia encoding schemes, cross-layer inthe
network adaptation, machine-to-machine communication (including RFIDs), rich 3D content as well as
community networks and the use of peer-to-peer (P2P) overlays are expected to generate new models of
interaction and cooperation, and be able to support enhanced perceived quality-of-experience (PQoE) and
innovative applications “on the move”, like virtual collaboration environments, personalised services/
media, virtual sport groups, on-line gaming, edutainment. In this context, the interaction with content
combined with interactive/multimedia search capabilities across distributed repositories, opportunistic P2P
networks and the dynamic adaptation to the characteristics of diverse mobile terminals are expected to
contribute towards such a vision.
Based on work that has taken place in a number of EC co-funded projects, in Framework Program 6 (FP6)
and Framework Program 7 (FP7), a group of experts and technology visionaries have voluntarily
contributed in this white paper aiming to describe the status, the state-of-the art, the challenges and the way
ahead in the area of Content Aware media delivery platforms
Multi-user video streaming using unequal error protection network coding in wireless networks
In this paper, we investigate a multi-user video streaming system applying unequal error protection (UEP) network coding (NC) for simultaneous real-time exchange of scalable video streams among multiple users. We focus on a simple wireless scenario where users exchange encoded data packets over a common central network node (e.g., a base station or an access point) that aims to capture the fundamental system behaviour. Our goal is to present analytical tools that provide both the decoding probability analysis and the expected delay guarantees for different importance layers of scalable video streams. Using the proposed tools, we offer a simple framework for design and analysis of UEP NC based multi-user video streaming systems and provide examples of system design for video conferencing scenario in broadband wireless cellular networks
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