Managing the outsourcing of information security processes: the 'cloud' solution

Information security processes and systems are relevant for any organization and involve medium-to-high investment; however, the current economic downturn is causing a dramatic reduction in spending on Information Technology (IT). Cloud computing (i.e., externalization of one or more IT services) might be a solution for organizations keen to maintain a good level of security. In this paper we discuss whether cloud computing is a valid alternative to in-house security processes and systems drawing on four mini-case studies of higher education institutions in New England, US. Our findings show that the organization’s IT spending capacity affects the choice to move to the cloud; however, the perceived security of the cloud and the perceived in-house capacity to provide high quality IT (and security) services moderate this relationship. Moreover, other variables such as (low) quality of technical support, relatively incomplete contracts, poor defined Service License Agreements (SLA), and ambiguities over data ownership affect the choice to outsource IT (and security) using the cloud. We suggest that, while cloud computing could be a useful means of IT outsourcing, there needs to be a number of changes and improvements to how the service is currently delivered

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

Strange Bedfellows in the Personal Computer Industry: Technology Alliances between IBM and Apple

Until recently technological development in the personal computer industry could be characterized by the competition between two basic designs. The current dominant design in this industry is associated with the IBM and Microsoft personal computing architecture. The other version of personal computing originated in the Macintosh computer from Apple Computer Company. In recent years we also see an increasing number of alliances between IBM and Apple. Joint technological development appears to be a major and somewhat surprising objective of these alliances. This paper analyzes the technology alliances between these companies in the context of recent technological changes, focusing on the timing and the objectives of these alliances. Technology partnering between these proponents of competing basic designs are found to only materialize several years after the DOS-based design of IBM and Microsoft had become dominant. This study is of a qualitative and exploratory nature, using both a small data set and two case studies.management and organization theory ;

Taxonomy of Technological IT Outsourcing Risks: Support for Risk Identification and Quantification

The past decade has seen an increasing interest in IT outsourcing as it promises companies many economic benefits. In recent years, IT paradigms, such as Software-as-a-Service or Cloud Computing using third-party services, are increasingly adopted. Current studies show that IT security and data privacy are the dominant factors affecting the perceived risk of IT outsourcing. Therefore, we explicitly focus on determining the technological risks related to IT security and quality of service characteristics associated with IT outsourcing. We conducted an extensive literature review, and thoroughly document the process in order to reach high validity and reliability. 149 papers have been evaluated based on a review of the whole content and out of the finally relevant 68 papers, we extracted 757 risk items. Using a successive refinement approach, which involved reduction of similar items and iterative re-grouping, we establish a taxonomy with nine risk categories for the final 70 technological risk items. Moreover, we describe how the taxonomy can be used to support the first two phases of the IT risk management process: risk identification and quantification. Therefore, for each item, we give parameters relevant for using them in an existing mathematical risk quantification model

ERP implementation methodologies and frameworks: a literature review

Enterprise Resource Planning (ERP) implementation is a complex and vibrant process, one that involves a combination of technological and organizational interactions. Often an ERP implementation project is the single largest IT project that an organization has ever launched and requires a mutual fit of system and organization. Also the concept of an ERP implementation supporting business processes across many different departments is not a generic, rigid and uniform concept and depends on variety of factors. As a result, the issues addressing the ERP implementation process have been one of the major concerns in industry. Therefore ERP implementation receives attention from practitioners and scholars and both, business as well as academic literature is abundant and not always very conclusive or coherent. However, research on ERP systems so far has been mainly focused on diffusion, use and impact issues. Less attention has been given to the methods used during the configuration and the implementation of ERP systems, even though they are commonly used in practice, they still remain largely unexplored and undocumented in Information Systems research. So, the academic relevance of this research is the contribution to the existing body of scientific knowledge. An annotated brief literature review is done in order to evaluate the current state of the existing academic literature. The purpose is to present a systematic overview of relevant ERP implementation methodologies and frameworks as a desire for achieving a better taxonomy of ERP implementation methodologies. This paper is useful to researchers who are interested in ERP implementation methodologies and frameworks. Results will serve as an input for a classification of the existing ERP implementation methodologies and frameworks. Also, this paper aims also at the professional ERP community involved in the process of ERP implementation by promoting a better understanding of ERP implementation methodologies and frameworks, its variety and history

2011 Strategic roadmap for Australian research infrastructure

The 2011 Roadmap articulates the priority research infrastructure areas of a national scale (capability areas) to further develop Australia’s research capacity and improve innovation and research outcomes over the next five to ten years. The capability areas have been identified through considered analysis of input provided by stakeholders, in conjunction with specialist advice from Expert Working Groups   It is intended the Strategic Framework will provide a high-level policy framework, which will include principles to guide the development of policy advice and the design of programs related to the funding of research infrastructure by the Australian Government. Roadmapping has been identified in the Strategic Framework Discussion Paper as the most appropriate prioritisation mechanism for national, collaborative research infrastructure. The strategic identification of Capability areas through a consultative roadmapping process was also validated in the report of the 2010 NCRIS Evaluation. The 2011 Roadmap is primarily concerned with medium to large-scale research infrastructure. However, any landmark infrastructure (typically involving an investment in excess of $100 million over five years from the Australian Government) requirements identified in this process will be noted. NRIC has also developed a ‘Process to identify and prioritise Australian Government landmark research infrastructure investments’ which is currently under consideration by the government as part of broader deliberations relating to research infrastructure. NRIC will have strategic oversight of the development of the 2011 Roadmap as part of its overall policy view of research infrastructure

State of the Industry 4.0 in the Andalusian food sector

The food industry is a key issue in the economic structure of Andalusia, due to both the weight and position of this industry in the economy and its advantages and potentials. The term Industry 4.0 carries many meanings. It seeks to describe the intelligent factory, with all the processes interconnected by Internet of things (IOT). Early advances in this field have involved the incorporation of greater flexibility and individualization of the manufacturing processes. The implementation of the framework proposed by Industry 4.0. is a need for the industry in general, and for Andalusian food industry in particular, and should be seen as a great opportunity of progress for the sector. It is expected that, along with others, the food and beverage industry will be pioneer in the adoption of flexible and individualized manufacturing processes. This work constitutes the state of the art, through bibliographic review, of the application of the proposed paradigm by the Industry 4.0. to the food industry.Telefónica, through the “Cátedra de Telefónica Inteligencia en la Red”Paloma Luna Garrid

e-Science Infrastructure for the Social Sciences

When the term „e-Science“ became popular, it frequently was referred to as “enhanced science” or “electronic science”. More telling is the definition ‘e-Science is about global collaboration in key areas of science and the next generation of infrastructure that will enable it’ (Taylor, 2001). The question arises to what extent can the social sciences profit from recent developments in e- Science infrastructure? While computing, storage and network capacities so far were sufficient to accommodate and access social science data bases, new capacities and technologies support new types of research, e.g. linking and analysing transactional or audio-visual data. Increasingly collaborative working by researchers in distributed networks is efficiently supported and new resources are available for e-learning. Whether these new developments become transformative or just helpful will very much depend on whether their full potential is recognized and creatively integrated into new research designs by theoretically innovative scientists. Progress in e-Science was very much linked to the vision of the Grid as “a software infrastructure that enables flexible, secure, coordinated resource sharing among dynamic collections of individuals, institutions and resources’ and virtually unlimited computing capacities (Foster et al. 2000). In the Social Sciences there has been considerable progress in using modern IT- technologies for multilingual access to virtual distributed research databases across Europe and beyond (e.g. NESSTAR, CESSDA – Portal), data portals for access to statistical offices and for linking access to data, literature, project, expert and other data bases (e.g. Digital Libraries, VASCODA/SOWIPORT). Whether future developments will need GRID enabling of social science databases or can be further developed using WEB 2.0 support is currently an open question. The challenges here are seamless integration and interoperability of data bases, a requirement that is also stipulated by internationalisation and trans-disciplinary research. This goes along with the need for standards and harmonisation of data and metadata. Progress powered by e- infrastructure is, among others, dependent on regulatory frameworks and human capital well trained in both, data science and research methods. It is also dependent on sufficient critical mass of the institutional infrastructure to efficiently support a dynamic research community that wants to “take the lead without catching up”.