Fred S. Roberts (editor) - Resilience in the Digital Age (Lecture Notes in Computer Science, 12660)

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We live in a more and more complex society, where science also reveals layer after layer of underlying complexity in physical and biological systems. Once we discovered this complexity we needed to deal with it, and this means that large volumes of data and the structural assistance of high-performance computers is now an intrinsic part of science, innovation and society as a whole. All of the major challenges, such as for instance formulated in the United Nations Sustainable Development Goals, will require complex, yet fast, research and development processes. The rapid development and roll out of COVID-19 vaccines is just one recent example of how advanced science and global collaboration are a day-to-day reality and how retractive nationalism is in essence ignoring the unavoidable reality of a global society. Digital Objects populating the Internet increasingly go way beyond the current Web of mostly narrative documents and comprise anything from single Unique Identifiers for concepts, to single, published assertions, to entire large data collections, as well as algorithms dealing with these vast amounts of data and information. This means that computers have become pivotal in the processing of data and need to be fully integrated in the research and innovation process.

This increasingly digital representation of reality should serve us by leading mankind to substantially improved and equally distributed quality of life and moving countries toward achieving the sustainable development goals established by the United Nations. By constructing, implementing, maintaining, and applying an Internet supporting an Internet-based global information infrastructure, we have already created, over the past 30 years or so, a new reality where billions of people and hundreds of billions of devices operate as a whole, interacting and cooperating with one another online. The vision of an Internet of FAIR data and services, as briefly described in [chapter 6] of this book, is in fact a realization of the vision recently expressed by George Strawn in his article [1]: The first generation [of the IT revolution] consisted of many independent computers. The second generation consists of one computer (the Internet network of computers) with many non-interoperable datasets [], the third generation will consist of one computer and one dataset. What is meant here is that data will increasingly stay where they are and will be visited for high performance analysis in situ. For this to happen, data and the algorithms and workflows encapsulated in virtual machines should find, access and re-use interoperable and machine actionable data.

However, the explosive growth of interconnectivity and complexity of digitalized sociotechnological systems also pose significant threats. One accidental or deliberate local malfunction of hardware or software may generate a flow of failures and damage, amplified by cascading effects and reaching objects and systems far beyond the place of the initial breakdown. The most dangerous threat is that consequences of such technological incidents may reach not only information objects and their storage, but transpose to physical space, producing cyberkinetic impacts on people, urban systems, and entire countries or regions. Similar issues arise with respect to complex linked natural and social systems at the center of changing climates, evolving urban environments, and questions about availability and equal access to food, water, and energy. The COVID-19 pandemic illustrates the interconnectedness of todays networks of trade, technology and medicine, and the importance of resilience of our infrastructure, supply chains, and human and social systems. That is why it is extremely necessary to support research efforts aimed at the development of better data stewardship, mathematical tools and models, providing assessment of resilience of the digitalized sociotechnological systems and their smart behavior, also under extreme and critical situations. Data science might help these systems to become more resilient, but only if a variety of challenges can be addressed. Examples of such challenges include the rapidly increasing need to re-use and combine data from numerous, distributed sources, the development of tools to address the explosion of alternatives available to policy makers, the need for algorithms to find anomalies in real-time in order to make instantaneous decisions to protect infrastructure, and the need to protect against the very vulnerabilities that the use of massive amounts of data introduce into sociotechnological systems.