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Peter J. Denning, Editor in ChiefThe digitally connected world has become a large, swirling sea of information stripped of context. We help our readers make sense of it, find meaning in it, learn what to trust, and speculate on our future.

Peter J. Denning,


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High Performance Synthetic Information Environments
An integrating architecture in the age of pervasive data and computing: Big Data (Ubiquity symposium)

by Christopher L. Barrett, Jeffrey Johnson, Madhav Marathe

The complexities of social and technological policy domains, such as the economy, the environment, and public health, present challenges that require a new approach to modeling and decision-making. The information required for effective policy and decision making in these complex domains is massive in scale, fine-grained in resolution, and distributed over many data sources. Thus, one of the key challenges in building systems to support policy informatics is information integration. Synthetic information environments (SIEs) present a methodological and technological solution that goes beyond the traditional approaches of systems theory, agent-based simulation, and model federation. An SIE is a multi-theory, multi-actor, multi-perspective system that supports continual data uptake, state assessment, decision analysis, and action assignment based on large-scale high-performance computing infrastructures. An SIE allows rapid course-of-action analysis to bound variances in outcomes of policy interventions, which in turn allows the short time-scale planning required in response to emergencies such as epidemic outbreaks.



Technology and Business Challenges of Big Data in the Digital Economy: Big Data (Ubiquity symposium)

by Dave Penkler

The early digital economy during the dot-com days of internet commerce successfully faced its first big data challenges of click-stream analysis with map-reduce technology. Since then the digital economy has been becoming much more pervasive. As the digital economy evolves, looking to benefit from its burgeoning big data assets, an important technical-business challenge is emerging: How to acquire, store, access, and exploit the data at a cost that is lower than the incremental revenue or GDP that its exploitation generates. Especially now that efficiency increases, which lasted for 50 years thanks to improvements in semiconductor manufacturing, is slowing and coming to an end.



Why putting yourself in the mind of your reader is easier---and more challenging---than you might have imagined

January 2018
by Philip Yaffe

Each "Communication Corner" essay is self-contained; however, they build on each other. For best results, before reading this essay and doing the exercise, go to the first essay "How an Ugly Duckling Became a Swan," then read each succeeding essay.



Big Data for Social Science Research: Big Data (Ubiquity symposium)

January 2018
by Mark Birkin

Academic studies exploiting novel data sources are scarce. Typically, data is generated by commercial businesses or government organizations with no mandate and little motivation to share their assets with academic partners---partial exceptions include social messaging data and some sources of open data. The mobilization of citizen sensors at a massive scale has allowed for the development of impressive infrastructures. However, data availability is driving applications---problems are prioritized because data is available rather than because they are inherently important or interesting. The U.K. is addressing this through investments by the Economic and Social Research Council in its Big Data Network. A group of Administrative Data Research Centres are tasked with improving access to data sets in central government, while a group of Business and Local Government Centres are tasked with improving access to commercial and regional sources. This initiative is described. It is illustrated by examples from health care, transport, and infrastructure. In all of these cases, the integration of data is a key consideration. For social science problems relevant to policy or academic studies, it is unlikely all the answers will be found in a single novel data source, but rather a combination of sources is required. Through such synthesis great leaps are possible by exploiting models that have been constructed and refined over extended periods of time e.g., microsimulation, spatial interaction models, agents, discrete choice, and input-output models. Although interesting and valuable new methods are appearing, any suggestion that a new box of magic tricks labeled "Big Data Analytics" that sits easily on top of massive new datasets can radically and instantly transform our long-term understanding of society is naïve and dangerous. Furthermore, the privacy and confidentiality of personal data is a great concern to both the individuals concerned and the data owners.