21,121 research outputs found
It's written in the cloud: The hype and promise of cloud computing
Purpose of paper: This viewpoint discusses the emerging IT platform of Cloud Computing and discusses where and how this has developed in terms of the collision between internet and enterprise computing paradigms – and hence why cloud computing will be driven not by computing architectures but more fundamental ICT consumption behaviours. Design/methodology/approach: The approach has been based upon the discussion and recent developments of Software as a Service (SaaS) and associated ICT computing metaphors and is largely based upon the contemporary discussion at the moment of the impact of social, open source and configurable technology services. Findings: It is suggested that whilst cloud computing and SaaS are indeed innovations within ICT, the real innovation will come when such platforms allow new industries, sectors, ways of doing business, connecting with and engaging with people to emerge. Thus looking beyond the technology itself.
Research limitations/applications: Author viewpoint only, not research based. Practical applications: Brings together some of the recent discussions within the popular as well as business and computing press on social networking, open source and utility computing. Social implications: Suggests that cloud computing can potentially transform and change the way in which IS and IT are accessed, consumed, configured and used in daily life. Originality / value of paper: Author viewpoint on a contemporary subject
RFCs, MOOs, LMSs: Assorted Educational Devices\ud
This paper discusses implicit social consequences of four basic internet protocols. The results are then related to the field of computer-assisted teaching. An educational on-line community is described and compared to the emerging standard of web-based learning management.\u
End-to-End Privacy for Open Big Data Markets
The idea of an open data market envisions the creation of a data trading
model to facilitate exchange of data between different parties in the Internet
of Things (IoT) domain. The data collected by IoT products and solutions are
expected to be traded in these markets. Data owners will collect data using IoT
products and solutions. Data consumers who are interested will negotiate with
the data owners to get access to such data. Data captured by IoT products will
allow data consumers to further understand the preferences and behaviours of
data owners and to generate additional business value using different
techniques ranging from waste reduction to personalized service offerings. In
open data markets, data consumers will be able to give back part of the
additional value generated to the data owners. However, privacy becomes a
significant issue when data that can be used to derive extremely personal
information is being traded. This paper discusses why privacy matters in the
IoT domain in general and especially in open data markets and surveys existing
privacy-preserving strategies and design techniques that can be used to
facilitate end to end privacy for open data markets. We also highlight some of
the major research challenges that need to be address in order to make the
vision of open data markets a reality through ensuring the privacy of
stakeholders.Comment: Accepted to be published in IEEE Cloud Computing Magazine: Special
Issue Cloud Computing and the La
State intervention, local indebtedness, investment overheating and their systemic background during global crisis in China
This paper focuses on the immediate economic and systemic reasons of steadily increasing local government indebtedness and investment overheating in China. These two phenomena emerged between 2008 and 2011 as a direct consequence of an external shock caused by the global crisis and the subsequent internal reaction in the form of intensified stimulating state intervention. New chances for resource distribution and investments through state intervention mobilized distribution priorities and politically rational economic behavior of actors, characteristic to party-state systems. Locations of mobilization were defined by the decentralized Chinese system specifics along the intertwined institutional party-state structure. Systemic characteristics and its Chinese specifics together resulted in investment overheating, and steadily growing local indebtedness through large and state-owned enterprises and local governments. This process was further amplified by the characteristics of transforming economy in China as actors in the private sphere were mobilized by the increased input demand of those privileged by the systemic priorities of state intervention
Engineering Crowdsourced Stream Processing Systems
A crowdsourced stream processing system (CSP) is a system that incorporates
crowdsourced tasks in the processing of a data stream. This can be seen as
enabling crowdsourcing work to be applied on a sample of large-scale data at
high speed, or equivalently, enabling stream processing to employ human
intelligence. It also leads to a substantial expansion of the capabilities of
data processing systems. Engineering a CSP system requires the combination of
human and machine computation elements. From a general systems theory
perspective, this means taking into account inherited as well as emerging
properties from both these elements. In this paper, we position CSP systems
within a broader taxonomy, outline a series of design principles and evaluation
metrics, present an extensible framework for their design, and describe several
design patterns. We showcase the capabilities of CSP systems by performing a
case study that applies our proposed framework to the design and analysis of a
real system (AIDR) that classifies social media messages during time-critical
crisis events. Results show that compared to a pure stream processing system,
AIDR can achieve a higher data classification accuracy, while compared to a
pure crowdsourcing solution, the system makes better use of human workers by
requiring much less manual work effort
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