On Mobile Cloud Computing in a Mobile Learning System

In the recent years, the nature of the Internet was constantly changing from a place used to read web pages to an environment that allows end-users to run software applications. Interactivity and collaboration have become the keywords of the new web content. This new environment supports the creation of a new generation of applications that are able to run on a wide range of hardware devices, like Mobile Phones or Personal Digital Assistants (PDAs) and this development gives rise to Mobile Cloud Computing. Mobile Cloud Computing at its simplest refers to an infrastructure where both the data storage and the data processing take place outside of the mobile device. Mobile cloud applications move the computing power and data storage away from mobile phones and into the cloud, bringing applications and mobile computing to not just smartphone users but a much broader range of mobile subscribers. In this work, Mobile learning system is designed based on electronic learning (e-learning) and mobility, within the context of mobile cloud computing. However, traditional m-learning applications have limitations in terms of high cost of devices and network, low network transmission rate, and limited educational resources; this cloud-based -learning application is introduced to solve these limitations. A mobile website is developed as well as a mobile application, this services which will be offered free, which will then gather relevant information in relation to the individuals’ topic of interest from a database located on a remote server and also web-links gotten from the cloud (internet) to expand the knowledge and understanding of the individual in the area of interest. Keyword: Cloud Computing, Mobile Learning System, Mobile Device

Taking the paper out of news: A case study of Taloussanomat, Europe's first online-only newspaper

Using in-depth interviews, newsroom observation, and internal documents, this case study presents and analyses changes that have taken place at Finnish financial daily Taloussanomat since it stopped printing on 28 December 2007 to focus exclusively on digital delivery via the Web, email, and mobile. It reveals the savings that can be achieved when a newspaper no longer prints and distributes a physical product; but also the revenue lost from subscriptions and print advertising. The consequences of a newspaper's decision to go online-only are examined as they relate to its business model, website traffic, and editorial practice. The findings illustrate the extent to which the medium rather than the content it carries determines news consumption patterns, show the differing attention a newspaper and its online substitute command, and reveal the changes to working patterns journalists can expect in the online-only environment

An Examination of Privacy Policies of US Government Senate Websites.

US Government websites are rapidly increasing the services they offer, but users express concerns about their personal privacy protection. To earn user's trust, these sites must show that personal data is protected, and the sites contain explicit privacy policies. This research studied privacy policy protection of 50 US Senate sites and found that few had comprehensive elements of privacy policies and a general lack of protection of personal data that could be obtain from the website. The study reviewed which specific privacy elements are most often mishandled, as well as suggestions for improving an overall online privacy practice

Of course we share! Testing Assumptions about Social Tagging Systems

Social tagging systems have established themselves as an important part in today's web and have attracted the interest from our research community in a variety of investigations. The overall vision of our community is that simply through interactions with the system, i.e., through tagging and sharing of resources, users would contribute to building useful semantic structures as well as resource indexes using uncontrolled vocabulary not only due to the easy-to-use mechanics. Henceforth, a variety of assumptions about social tagging systems have emerged, yet testing them has been difficult due to the absence of suitable data. In this work we thoroughly investigate three available assumptions - e.g., is a tagging system really social? - by examining live log data gathered from the real-world public social tagging system BibSonomy. Our empirical results indicate that while some of these assumptions hold to a certain extent, other assumptions need to be reflected and viewed in a very critical light. Our observations have implications for the design of future search and other algorithms to better reflect the actual user behavior

BDGS: A Scalable Big Data Generator Suite in Big Data Benchmarking

Data generation is a key issue in big data benchmarking that aims to generate application-specific data sets to meet the 4V requirements of big data. Specifically, big data generators need to generate scalable data (Volume) of different types (Variety) under controllable generation rates (Velocity) while keeping the important characteristics of raw data (Veracity). This gives rise to various new challenges about how we design generators efficiently and successfully. To date, most existing techniques can only generate limited types of data and support specific big data systems such as Hadoop. Hence we develop a tool, called Big Data Generator Suite (BDGS), to efficiently generate scalable big data while employing data models derived from real data to preserve data veracity. The effectiveness of BDGS is demonstrated by developing six data generators covering three representative data types (structured, semi-structured and unstructured) and three data sources (text, graph, and table data)

Scraping the Social? Issues in live social research

What makes scraping methodologically interesting for social and cultural research? This paper seeks to contribute to debates about digital social research by exploring how a ‘medium-specific’ technique for online data capture may be rendered analytically productive for social research. As a device that is currently being imported into social research, scraping has the capacity to re-structure social research, and this in at least two ways. Firstly, as a technique that is not native to social research, scraping risks to introduce ‘alien’ methodological assumptions into social research (such as an pre-occupation with freshness). Secondly, to scrape is to risk importing into our inquiry categories that are prevalent in the social practices enabled by the media: scraping makes available already formatted data for social research. Scraped data, and online social data more generally, tend to come with ‘external’ analytics already built-in. This circumstance is often approached as a ‘problem’ with online data capture, but we propose it may be turned into virtue, insofar as data formats that have currency in the areas under scrutiny may serve as a source of social data themselves. Scraping, we propose, makes it possible to render traffic between the object and process of social research analytically productive. It enables a form of ‘real-time’ social research, in which the formats and life cycles of online data may lend structure to the analytic objects and findings of social research. By way of a conclusion, we demonstrate this point in an exercise of online issue profiling, and more particularly, by relying on Twitter to profile the issue of ‘austerity’. Here we distinguish between two forms of real-time research, those dedicated to monitoring live content (which terms are current?) and those concerned with analysing the liveliness of issues (which topics are happening?)