A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing

Data Grids have been adopted as the platform for scientific communities that need to share, access, transport, process and manage large data collections distributed worldwide. They combine high-end computing technologies with high-performance networking and wide-area storage management techniques. In this paper, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks and distributed databases. We then provide comprehensive taxonomies that cover various aspects of architecture, data transportation, data replication and resource allocation and scheduling. Finally, we map the proposed taxonomy to various Data Grid systems not only to validate the taxonomy but also to identify areas for future exploration. Through this taxonomy, we aim to categorise existing systems to better understand their goals and their methodology. This would help evaluate their applicability for solving similar problems. This taxonomy also provides a "gap analysis" of this area through which researchers can potentially identify new issues for investigation. Finally, we hope that the proposed taxonomy and mapping also helps to provide an easy way for new practitioners to understand this complex area of research.Comment: 46 pages, 16 figures, Technical Repor

Distributed Information Retrieval using Keyword Auctions

This report motivates the need for large-scale distributed approaches to information retrieval, and proposes solutions based on keyword auctions

FinBook: literary content as digital commodity

This short essay explains the significance of the FinBook intervention, and invites the reader to participate. We have associated each chapter within this book with a financial robot (FinBot), and created a market whereby book content will be traded with financial securities. As human labour increasingly consists of unstable and uncertain work practices and as algorithms replace people on the virtual trading floors of the worlds markets, we see members of society taking advantage of FinBots to invest and make extra funds. Bots of all kinds are making financial decisions for us, searching online on our behalf to help us invest, to consume products and services. Our contribution to this compilation is to turn the collection of chapters in this book into a dynamic investment portfolio, and thereby play out what might happen to the process of buying and consuming literature in the not-so-distant future. By attaching identities (through QR codes) to each chapter, we create a market in which the chapter can ‘perform’. Our FinBots will trade based on features extracted from the authors’ words in this book: the political, ethical and cultural values embedded in the work, and the extent to which the FinBots share authors’ concerns; and the performance of chapters amongst those human and non-human actors that make up the market, and readership. In short, the FinBook model turns our work and the work of our co-authors into an investment portfolio, mediated by the market and the attention of readers. By creating a digital economy specifically around the content of online texts, our chapter and the FinBook platform aims to challenge the reader to consider how their personal values align them with individual articles, and how these become contested as they perform different value judgements about the financial performance of each chapter and the book as a whole. At the same time, by introducing ‘autonomous’ trading bots, we also explore the different ‘network’ affordances that differ between paper based books that’s scarcity is developed through analogue form, and digital forms of books whose uniqueness is reached through encryption. We thereby speak to wider questions about the conditions of an aggressive market in which algorithms subject cultural and intellectual items – books – to economic parameters, and the increasing ubiquity of data bots as actors in our social, political, economic and cultural lives. We understand that our marketization of literature may be an uncomfortable juxtaposition against the conventionally-imagined way a book is created, enjoyed and shared: it is intended to be

In Search of a New Model: Library Resource Sharing in China - A Comparative Study

This paper reviews the framework of library resource sharing (LRS) in China and examines, from a comparative perspective, cases of recent development, particularly in the 1990s and early 2000s. Highlights include: (1) historical review of LRS in the U.S. and China, particularly in the areas of print union catalogs and union lists, online bibliographic utilities, and interlibrary loan; (2) literature review of Chinese publications, and LRS issues and challenges in China; (3) Analysis of three LRS models to provide a contextual grasp of a paradigm shift taking place in China; and (4) comparative analysis of LRS objectives, structure, and governance, etc., in the U.S. and China. The study also underscores the imperative for building a national digital library system in China to gain a competitive edge in resource sharing and to support the country’s rapid social and economic growth. At this stage of development, the success of China Academic Library & Information System provides a convincing argument for a national digital library system with its methods of governing, financing, and development

Storage Solutions for Big Data Systems: A Qualitative Study and Comparison

Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed

Learning in an age of digital networks

The final years of the twentieth century and early years of the twenty first century have been marked by the rapid rise of digital and networked technologies. Some have even called it a paradigm shift and suggested that it will lead to a dramatic change in the way young people learn (Tapscott and Williams, 2010). As with all commentary on new technologies we should beware of being carried away with the excitement of the new. There is a recurrent innovation cycle beginning with over excitement followed by disappointment and once the reaction has set in against the new it is followed by a move away to yet another new technology, often before a proper assessment and evaluation of the previous cycle can take place. Equally we must be careful not to ignore the profound changes that are taking place and how they may affect universities and learning in society more generally. A recent description by a UK based think tank Demos characterized the kind of university that is emerging from the engagement with new digital and networked technologies as the 'edgeless university' (Bradwell, 2009). The term edgeless is borrowed from work on the city that suggests edgeless cities have the function of cities without being organized in their classic form. In the same way the Demos pamphlet suggests that the university retains an identifiable function but the functions of the university are no longer confined to a single institution nor are they confined to higher education institutions more broadly. Over a decade ago Brown and Duguid (2000) identified the core functions of universities as the capacity to grant degrees, to accredit students and to provide the warrant that guaranteed the credentials obtained by the students from the university. They also suggested that the introduction of what were then new technologies would lead to an increased focus on these core functions. The core role remains in the edgeless university but the boundaries to these may alter. This article tries to provide a way of thinking about new technologies that manages to balance these two conflicting needs. It identifies some current ways of thinking about the changes taking place in universities that are related to digital and networked technologies and to assess their impact. It then goes on to suggest the kinds of choices we may have to make in relation to new technologies at a variety of levels, the personal, the institutional and in terms of society in general. The edgeless university is associated with broad technological change but whether such change is inevitable is still an issue that needs to be discussed