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

Patent Analytics Based on Feature Vector Space Model: A Case of IoT

The number of approved patents worldwide increases rapidly each year, which requires new patent analytics to efficiently mine the valuable information attached to these patents. Vector space model (VSM) represents documents as high-dimensional vectors, where each dimension corresponds to a unique term. While originally proposed for information retrieval systems, VSM has also seen wide applications in patent analytics, and used as a fundamental tool to map patent documents to structured data. However, VSM method suffers from several limitations when applied to patent analysis tasks, such as loss of sentence-level semantics and curse-of-dimensionality problems. In order to address the above limitations, we propose a patent analytics based on feature vector space model (FVSM), where the FVSM is constructed by mapping patent documents to feature vectors extracted by convolutional neural networks (CNN). The applications of FVSM for three typical patent analysis tasks, i.e., patents similarity comparison, patent clustering, and patent map generation are discussed. A case study using patents related to Internet of Things (IoT) technology is illustrated to demonstrate the performance and effectiveness of FVSM. The proposed FVSM can be adopted by other patent analysis studies to replace VSM, based on which various big data learning tasks can be performed

Knowledge management, innovation and big data: Implications for sustainability, policy making and competitiveness

This Special Issue of Sustainability devoted to the topic of “Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness” attracted exponential attention of scholars, practitioners, and policy-makers from all over the world. Locating themselves at the expanding cross-section of the uses of sophisticated information and communication technology (ICT) and insights from social science and engineering, all papers included in this Special Issue contribute to the opening of new avenues of research in the field of innovation, knowledge management, and big data. By triggering a lively debate on diverse challenges that companies are exposed to today, this Special Issue offers an in-depth, informative, well-structured, comparative insight into the most salient developments shaping the corresponding fields of research and policymaking

The Effect of the Dynamics of Knowledge Base Complexity on Schumpeterian patterns of Innovation: the upstream petroleum industry

This paper addresses important changes in innovation patterns in the upstream petroleum industry over the period from the 1970s to 2005. It argues that the shifts in patterns of innovation over that period can be explained by the dynamics of knowledge base complexity (KBC). We develop a quantitative method to explore KBC and show that increasing KBC has shifted innovation patterns, from a broadly Schumpeter Mark I to a 'modified' form of Schumpeter Mark II, led less by the established oil majors, but by a new class of integrated service providers

Technology upgrading of middle income economies: A new approach and results

We explore issues of measurement for technology upgrading of the economies moving from middle to high-income status. In exploring this issue, we apply theoretically relevant and empirically grounded middle level conceptual and statistical framework based on three dimensions: (i) Intensity (ii) breadth of technological upgrading, and (iii) technology and knowledge exchange. As an outcome, we construct a three-pronged composite indicator of technology upgrading based on 35 indicators which reflect different drivers and patterns of technology upgrading of countries at different income levels. We show that technology upgrading of middle-income economies is distinctively different from that of low and high-income economies. Our results suggest the existence of middle-income trap in technology upgrading - i.e. countries' technology upgrading activities are not reflected in their income levels. Based on the simple statistical analysis we show that the middle-income trap is present in all three aspects of technology upgrading, but their importance varies across different aspects. A trap seems to be higher for 'breadth' of technology upgrading than for 'intensity' of technology upgrading and is by far the highest for the dimension of knowledge and technology interaction with the global economy. Finally, our research shows that technology upgrading is a multidimensional process and that it would be methodologically wrong to aim for an aggregate index

Prospect patents, data markets, and the commons in data-driven medicine : openness and the political economy of intellectual property rights

Scholars who point to political influences and the regulatory function of patent courts in the USA have long questioned the courts’ subjective interpretation of what ‘things’ can be claimed as inventions. The present article sheds light on a different but related facet: the role of the courts in regulating knowledge production. I argue that the recent cases decided by the US Supreme Court and the Federal Circuit, which made diagnostics and software very difficult to patent and which attracted criticism for a wealth of different reasons, are fine case studies of the current debate over the proper role of the state in regulating the marketplace and knowledge production in the emerging information economy. The article explains that these patents are prospect patents that may be used by a monopolist to collect data that everybody else needs in order to compete effectively. As such, they raise familiar concerns about failure of coordination emerging as a result of a monopolist controlling a resource such as datasets that others need and cannot replicate. In effect, the courts regulated the market, primarily focusing on ensuring the free flow of data in the emerging marketplace very much in the spirit of the ‘free the data’ language in various policy initiatives, yet at the same time with an eye to boost downstream innovation. In doing so, these decisions essentially endorse practices of personal information processing which constitute a new type of public domain: a source of raw materials which are there for the taking and which have become most important inputs to commercial activity. From this vantage point of view, the legal interpretation of the private and the shared legitimizes a model of data extraction from individuals, the raw material of information capitalism, that will fuel the next generation of data-intensive therapeutics in the field of data-driven medicine

China's absorptive State: research, innovation and the prospects for China-UK collaboration

China's innovation system is advancing so rapidly in multiple directions that the UK needs to develop a more ambitious and tailored strategy, able to maximise opportunities and minimise risks across the diversity of its innovation links to China. For the UK, the choice is not whether to engage more deeply with the Chinese system, but how. This report analyses the policies, prospects and dilemmas for Chinese research and innovation over the next decade. It is designed to inform a more strategic approach to supporting China-UK collaboration

Measuring Infringement of Intellectual Property Rights

© Crown Copyright 2014. You may re-use this information (excluding logos) free of charge in any format or medium, under the terms of the Open Government Licence. To view this licence, visit http://www.nationalarchives.gov. uk/doc/open-government-licence/ Where we have identified any third party copyright information you will need to obtain permission from the copyright holders concernedThe review is wide-ranging in scope and overall our findings evidence a lack of appreciation among those producing research for the high-level principles of measurement and assessment of scale. To date, the approaches adopted by industry seem more designed for internal consumption and are usually contingent on particular technologies and/or sector perspectives. Typically, there is a lack of transparency in the methodologies and data used to form the basis of claims, making much of this an unreliable basis for policy formulation. The research approaches we found are characterised by a number of features that can be summarised as a preference for reactive approaches that look to establish snapshots of an important issue at the time of investigation. Most studies are ad hoc in nature and on the whole we found a lack of sustained longitudinal approaches that would develop the appreciation of change. Typically the studies are designed to address specific hypotheses that might serve to support the position of the particular commissioning body. To help bring some structure to this area, we propose a framework for the assessment of the volume of infringement in each different area. The underlying aim is to draw out a common approach wherever possible in each area, rather than being drawn initially to the differences in each field. We advocate on-going survey tracking of the attitudes, perceptions and, where practical, behaviours of both perpetrators and claimants in IP infringement. Clearly, the nature of perpetrators, claimants and enforcement differs within each IPR but in our view the assessment for each IPR should include all of these elements. It is important to clarify that the key element of the survey structure is the adoption of a survey sampling methodology and smaller volumes of representative participation. Once selection is given the appropriate priority, a traditional offline survey will have a part to play, but as the opportunity arises, new technological methodologies, particularly for the voluntary monitoring of online behaviour, can add additional detail to the overall assessment of the scale of activity. This framework can be applied within each of the IP right sectors: copyright, trademarks,patents, and design rights. It may well be that the costs involved with this common approach could be mitigated by a syndicated approach to the survey elements. Indeed, a syndicated approach has a number of advantages in addition to cost. It could be designed to reduce any tendency either to hide inappropriate/illegal activity or alternatively exaggerate its volume to fit with the theme of the survey. It also has the scope to allow for monthly assessments of attitudes rather than being vulnerable to unmeasured seasonal impacts