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This study aims to focus on asset management of large‐scale information systems supporting infrastructures and especially seeks to address a methodology of their statistical deterioration prediction based on their historical inspection data. Information systems are composed of many devices. Deterioration process i.e. wear‐out failure generation process of those devices is formulated by a Weibull hazard model. Furthermore, in order to consider the heterogeneity of the hazard rate of each device, the random proportional Weibull hazard model, which expresses the heterogeneity of the hazard rate as random variables, is to be proposed.

Large‐scale information systems comprise many components, and different types of components might have different hazard rates. Therefore, when analyzing faults of information systems that comprise various types of devices and components, it is important to consider the heterogeneity of the hazard rates that exist between the different types of components. In this study, with this in consideration, the random proportional Weibull hazard model, whose heterogeneity of hazard rates is subject to a gamma distribution, is formulated and a methodology is proposed which estimates the failure rate of various components comprising an information system.

Through a case study using a traffic control system for expressways, the validity of the proposed model is empirically verified. Concretely, as for HDD, the service life at which the survival probability is 50 percent is estimated as 158 months. However, even for the same HDD, use environment differs according to usage. Actually, among the three different usages (PC, server, others), failures happen earliest in the case of PCs, which have the highest heterogeneity parameter and a survival probability of 50 percent after 135 months of usage. On the other hand, as for others, its survival probability is 50 percent at 303 months.

To operationally express the heterogeneity of failure rates, the Weibull hazard model is employed as a base, and a random proportional Weibull hazard model expressing the proportional heterogeneity of hazard rates with a standard gamma distribution is formulated. By estimating the parameter of the standard proportional Weibull hazard function and the parameter of the probability distribution that expresses the heterogeneity of the proportionality constant between the types, the random proportional Weibull hazard model can easily express the heterogeneity of the hazard rates between types and components.

The purpose of this paper is to explore the challenges posed by Big Data to current trends in computation, networking and storage technology at various stages of Big Data analysis. The work aims to bridge the gap between theory and practice, and highlight the areas of potential research.

The study employs a systematic and critical review of the relevant literature to explore the challenges posed by Big Data to hardware technology, and assess the worthiness of hardware technology at various stages of Big Data analysis. Online computer-databases were searched to identify the literature relevant to: Big Data requirements and challenges; and evolution and current trends of hardware technology.

The findings reveal that even though current hardware technology has not evolved with the motivation to support Big Data analysis, it significantly supports Big Data analysis at all stages. However, they also point toward some important shortcomings and challenges of current technology trends. These include: lack of intelligent Big Data sources; need for scalable real-time analysis capability; lack of support (in networks) for latency-bound applications; need for necessary augmentation (in network support) for peer-to-peer networks; and rethinking on cost-effective high-performance storage subsystem.

The study suggests that a lot of research is yet to be done in hardware technology, if full potential of Big Data is to be unlocked.

The study suggests that practitioners need to meticulously choose the hardware infrastructure for Big Data considering the limitations of technology.

This research arms industry, enterprises and organizations with the concise and comprehensive technical-knowledge about the capability of current hardware technology trends in solving Big Data problems. It also highlights the areas of potential research and immediate attention which researchers can exploit to explore new ideas and existing practices.

The purpose of this study is to analyze the natural gas sector in Italy, with a specific focus on the regulatory framework, the development of infrastructures and the supply and demand balance.

The research has been developed by applying qualitative and quantitative methodologies. In particular, a review and a qualitative analysis have been proposed to analyze the regulatory framework and the development of infrastructure, whereas the consumption trend is quantitatively examined by means of the decomposition analysis.

This study highlights how the legislation is complete and in line with European Union (EU) prescriptions, and how the infrastructure is quite well-developed, even though many expected developments are on hold due to the current market conditions. The supply and demand balance highlights a decreasing trend of the consumption owing to the aggressive development of renewables and permanence of a weak economic growth. Finally, the decomposition analysis of total and sectorial natural gas demand has emphasized that the increase of energy intensity can be considered responsible for a large share of consumption between 1995 and 2014.

The paper details the status of the natural gas sector in Italy, which is one of the largest consumers of natural gas within the EU. Despite its importance, the Italian natural gas sector has not been subjected to extensive research, and this paper represents a first attempt to provide an overview of the sector.

Increasingly, the content that libraries collect is no longer on paper, a long-lived, medium whose technology changes very slowly and with which they have centuries of experience. Instead, it is stored on relatively short-lived digital media whose technology appears to change rapidly and with which they have little history. The paper aims to discuss this issue.

The storage media industry is highly competitive and is currently evolving rapidly as flash, a solid state medium, displaces spinning disk from many applications. Long-term archival storage is a small part of the total storage market. It typically re-uses media and systems intended for more general bulk storage.

What are the medium-term prospects for change in this market?

Much of this material has appeared in blog posts and talks aimed at storage experts, such as the recent DARPA workshop on future of storage. It is presented here for a librarian audience with the necessary additional exposition and background.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting‐edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

According to Alan Kay, inventing the future is the best way of predicting what is going to happen. However, the wise words of this eminent US computer scientist are not taken on board in every company. Such firms are skeptical towards innovation and accordingly cagey in their approach. There is no denying that innovation equals uncertainty and is clearly a risk‐laden pursuit at the best of times. Many organizations therefore play safe by confining their search for new knowledge to existing domains. This means that specific ways of thinking dominate and management is biased towards channels it is already familiar with. Some might argue that sticking to the tried and trusted makes sense. However, any reluctance to look further afield for solutions hardly maximizes the chances of success. And adopting a narrow focus raises the danger of complacency creeping into the equation. Why? Because some leaders suppose that familiarity with the research domain will make innovations easier to identify. In the long run, that can prove a costly assumption to make.

Organizations can enhance prospects of successful innovation by setting broader objectives and considering knowledge and information from a wide variety of sources. A funding bias towards projects with greater potential should be adopted.

Exploring potential new energy sources and technological advancements can help firms to operate more efficiently and reduce their carbon footprint.

The briefing saves busy executives and researchers' hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy‐to‐digest format.

This paper aims to forecast the future of data storage technologies, using the case of two major technologies driving the data storage world; hard disk drive (HDD) or conventional longitudinal recording and flash memory.

Four principal tools of forecasting technology are applied to present the coming future of data storage technologies. These tools consist of bibliometric trend analysis, patent trend analysis, technology cycle time (TCT), and growth curve. Numbers of publications each year and cumulative patents are used to analyze the future of these competitive technologies. The median age of the patents is applied to find the technology lifecycle of both technologies. Finally, areal density of HDD is plotted on the growth curve to forecast the saturation point of researching on conventional longitudinal recording.

The results produced from utilizing these tools indicate that these two technologies are continuously under development. It is found that the growth of improving areal density of conventional recording will be nearing zero by year 2013 and the maximum growth rate of development was in year 2006.

The forecasting is based on principal technologies of conventional longitudinal recording of HDD. Also, NOR and NAND are considered as two main technologies dominating flash memory. Other emerging data storage technologies such as holographic data storage, phase‐change memory (PCM) are not included in this paper. Likewise, the growth curve of flash memory is not analyzed as part of this paper.

This paper forecasts the future of data storage technologies. Integration of scientific indicators and growth curves is demonstrating a powerful tool for forecasting technology futures.

The purpose of this paper is to examine empirically the basis risk and hedging efficiency of temperature‐indexed standardized weather derivatives in hedging weather risks in the US energy industry.

Within the risk minimization framework, using power load and temperature data, this research analyzes both linear and nonlinear hedging strategies using the two most popular types of standardized indexes – city indexes and regional indexes.

The results indicate that the city indexes and regional indexes are not consistently superior to each other and the regional indexes should be a good complement to the current exchange‐listed indexes. The results also document that the basis risk is sufficiently low for the diversified power producers serving the US Northeast or Mid‐Atlantic regions in both the summer and winter seasons and California in the summer season. However, the basis risk is very high for the diversified power producers serving California in hedging the weather risk in the winter season. More discrepancies are observed in the hedging efficiency among the power producers serving the Texas region.

This research provides important implications about the survivability and superiority of current and proposed standardized weather contracts and the design of effective standardized weather derivatives for the extant and potential weather markets.