Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

1st INCF Workshop on Sustainability of Neuroscience Databases

The goal of the workshop was to discuss issues related to the sustainability of neuroscience databases, identify problems and propose solutions, and formulate recommendations to the INCF. The report summarizes the discussions of invited participants from the neuroinformatics community as well as from other disciplines where sustainability issues have already been approached. The recommendations for the INCF involve rating, ranking, and supporting database sustainability

Social Capital, R&D and Industrial Districts

The main idea behind this paper is that social capital is not, as generally suggested by the socio-economic literature, an individual attitude towards something which does not imply privately appropriable economic benefits. Actually, SC might and should be interpreted as a public component of an investment which implies private and public benefits entangled with each other. In order to put forward this idea, a dynamic theoretical model that assumes social capital as the public component of the impure public good R&D is developed. It shows that the ‘civic culture’ of the district area in which the firm works is not sufficient as an incentive to increase its investment in social capital, because this investment strictly depends on the economic convenience of investing in the impure public good. Social capital /networking dynamics might positively and complementarily evolve only if the opportunity cost of investing in innovation is sufficiently low. We consequently focus our attention on a specialized industrial district located in the Emilia Romagna region – the biomedical district of Mirandola (Modena) – characterised by a strong pattern of innovative activity. Using a proxy for innovative activity as dependant variable, we observe that R&D and networking/social capital arise as complementary driving forces for innovation outputs. When empirical evidence confirms that this complementarity plays a key role, and consequently strong links exist between market and non-market dynamics relating to firms, the role for policy actions targeted to social capital is larger. The policy effort should be targeted toward both market and non-market characteristics taken together, rather than solely to the production of (local) public goods (social capital) or innovation inputs as independent elements of firm processes. The input of SC alone is not sufficient to ensure innovation and growth: economic incentives matter. On the other hand, whenever SC dynamics are crucial for R&D private investments, the effect of economic incentives depends on the presence and degree of their complementarity.Social capital, R&D, Technological innovation, Industrial districts

Does fragmented or heterogeneous IP ownership stifle investments in innovation?

Thickets of partially overlapping patent rights raise costs to secure IPR for innovation. Fragmented IP ownership raises coordination costs to resolve mutual blockades. Inadvertent patent infringement poses the risk of fruits from investments to be exploited. A gap in economic commitment levels may be exploited if capital-intensive innovators have more invested application-specifically than inadvertently infringed IPR owners. I study whether fragmentation or heterogeneous capital-intensities among owners of overlapping patents affect propensities to invest in innovation. I find that firms with small patent portfolios are less likely to invest in innovation if IPR is fragmented. Firms with large patent portfolios are less likely to invest in innovation if cited patent owners have smaller stocks of fixed capital. This suggests that effects of patent thickets on innovation are not evenly spread among innovating firms

Science-Based R&D in Schumpeterian Growth

Firm success is often associated with the development of better products. Private firms undertake applied R&D seeking market advantage, by capitalizing on the freely accessible results of basic research. But unpatentable basic research often fails to address applied R&D open problems. What is the role of the incentives in improving the innovative performance of an economy by matching partially motivated public researchers to their mission? Sometimes government funded research projects are mission-directed, yet in many cases the public sector academics indulge in carrier-driven research. An innovation system where, as in the US, also basic research is driven by patents, implicitly sets an ex-post incentive to the researchers guided by invisible hand. For a public innovation system - like the European one - designing an incentive scheme to motivate public researchers is of key importance for fostering the performance of the economic system. This paper extends the Schumpeterian multisector growth model with vertical innovation by highlighting a link between the degree of "targetness" of public research and aggregate innovation. A positive effect of social capital is also proved.Sequential Innovation, Research Tools, Basic Research, Knowledge Management, Social Capital.

Big Data for the Sustainability of Healthcare Project Financing

This study aims to detect if and how big data can improve the quality and timeliness of information in infrastructural healthcare Project Finance (PF) investments, making them more sustainable and increasing overall efficiency. Interactions with telemedicine or disease management and prediction are promising but still underexploited. However, given the rising health expenditure and shrinking budgets, data-driven cost-cutting is inevitably required. An interdisciplinary approach combines complementary aspects concerning big data, healthcare information technology, and PF investments. The methodology is based on a business plan of a standard healthcare Public-Private Partnership (PPP) investment, compared with a big data-driven business model that incorporates predictive analytics in different scenarios. When Public and Private Partners interact through networking big data and interoperable databases, they boost value co-creation, improving Value for Money and reducing risk. Big data can also help shortening supply chain steps, expanding economic marginality and easing the sustainable planning of smart healthcare investments. Flexibility, driven by timely big data feedbacks, contributes to reducing the intrinsic rigidity of long-termed PF healthcare investments. Healthcare is a highly networked and systemic industry that can benefit from interacting with big data that provide timely feedbacks for continuous business model re-engineering, reducing the distance between forecasts and actual occurrences. Risk shrinks and sustainability is fostered, together with the bankability of the infrastructural investment