135,977 research outputs found
From the Hands of an Early Adopter's Avatar to Virtual Junkyards: Analysis of Virtual Goods' Lifetime Survival
One of the major questions in the study of economics, logistics, and business
forecasting is the measurement and prediction of value creation, distribution,
and lifetime in the form of goods. In "real" economies, a perfect model for the
circulation of goods is impossible. However, virtual realities and economies
pose a new frontier for the broad study of economics, since every good and
transaction can be accurately tracked. Therefore, models that predict goods'
circulation can be tested and confirmed before their introduction to "real
life" and other scenarios. The present study is focused on the characteristics
of early-stage adopters for virtual goods, and how they predict the lifespan of
the goods. We employ machine learning and decision trees as the basis of our
prediction models. Results provide evidence that the prediction of the lifespan
of virtual objects is possible based just on data from early holders of those
objects. Overall, communication and social activity are the main drivers for
the effective propagation of virtual goods, and they are the most expected
characteristics of early adopters.Comment: 28 page
Quality assessment and usage behavior of a mobile voice-over-IP service
Voice-over-IP (VoIP) services offer users a cheap alternative to the traditional mobile operators to make voice calls. Due to the increased capabilities and connectivity of mobile devices, these VoIP services are becoming increasingly popular on the mobile platform. Understanding the user's usage behavior and quality assessment of the VoIP service plays a key role in optimizing the Quality of Experience (QoE) and making the service to succeed or to fail. By analyzing the usage and quality assessments of a commercial VoIP service, this paper identifies device characteristics, context parameters, and user aspects that influence the usage behavior and experience during VoIP calls. Whereas multimedia services are traditionally evaluated by monitoring usage and quality for a limited number of test subjects and during a limited evaluation period, this study analyzes the service usage and quality assessments of more than thousand users over a period of 120 days. This allows to analyze evolutions in the usage behavior and perceived quality over time, which has not been done up to now for a widely-used, mobile, multimedia service. The results show a significant evolution over time of the number of calls, the call duration, and the quality assessment. The time of the call, the used network, and handovers during the call showed to have a significant influence on the users' quality assessments
Towards Economic Models for MOOC Pricing Strategy Design
MOOCs have brought unprecedented opportunities of making high-quality courses
accessible to everybody. However, from the business point of view, MOOCs are
often challenged for lacking of sustainable business models, and academic
research for marketing strategies of MOOCs is also a blind spot currently. In
this work, we try to formulate the business models and pricing strategies in a
structured and scientific way. Based on both theoretical research and real
marketing data analysis from a MOOC platform, we present the insights of the
pricing strategies for existing MOOC markets. We focus on the pricing
strategies for verified certificates in the B2C markets, and also give ideas of
modeling the course sub-licensing services in B2B markets
User-Based Data Collection Techniques and Strategies for Evaluating Networked Information Services
published or submitted for publicatio
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
Service Migration from Cloud to Multi-tier Fog Nodes for Multimedia Dissemination with QoE Support.
A wide range of multimedia services is expected to be offered for mobile users via various wireless access networks. Even the integration of Cloud Computing in such networks does not support an adequate Quality of Experience (QoE) in areas with high demands for multimedia contents. Fog computing has been conceptualized to facilitate the deployment of new services that cloud computing cannot provide, particularly those demanding QoE guarantees. These services are provided using fog nodes located at the network edge, which is capable of virtualizing their functions/applications. Service migration from the cloud to fog nodes can be actuated by request patterns and the timing issues. To the best of our knowledge, existing works on fog computing focus on architecture and fog node deployment issues. In this article, we describe the operational impacts and benefits associated with service migration from the cloud to multi-tier fog computing for video distribution with QoE support. Besides that, we perform the evaluation of such service migration of video services. Finally, we present potential research challenges and trends
Towards Psychometrics-based Friend Recommendations in Social Networking Services
Two of the defining elements of Social Networking Services are the social
profile, containing information about the user, and the social graph,
containing information about the connections between users. Social Networking
Services are used to connect to known people as well as to discover new
contacts. Current friend recommendation mechanisms typically utilize the social
graph. In this paper, we argue that psychometrics, the field of measuring
personality traits, can help make meaningful friend recommendations based on an
extended social profile containing collected smartphone sensor data. This will
support the development of highly distributed Social Networking Services
without central knowledge of the social graph.Comment: Accepted for publication at the 2017 International Conference on AI &
Mobile Services (IEEE AIMS
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