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Kemal Delic Collection

  • COVID-19 and computation for policy

    Governments across the world are formulating and implementing medical, social, economic and other policies to manage the COVID-19 pandemic and protect their citizens. Many governments claim that their policies follow the best available scientific advice. Much of that advice comes from computational modeling. Two of the main types of model are presented: the SIR (Susceptible, Infected, Recovered) model developed by Kermack and McKendrick in the 1920s and the more recent Agent Based Models. The SIR model gives a good intuition of how epidemics spread; including how mass vaccination can contain them. It is less useful than Agent Based Modeling for investigating the effects of policies such as social distancing, self-isolation, wearing facemasks, and test-trace-isolate.

    Politicians and the public have been perplexed to observe the lack of consensus in the scientific community and there being no single 'best science' to follow. The outcome of computational models depends on the assumptions made and the data used. Different assumptions will lead to different computational outcomes, especially when the available data are so poor. This leads some commentators to argue that the models are wrong and dangerous. Some may be, but computational modeling is one of the few ways available to explore and try to understand the space of possible futures. This lack of certainty means that computational modeling must be seen as just one of many inputs into the political decision making process. Politicians must balance all the competing inputs and make timely decisions based on their conclusions---be they right or wrong. In the same way that democracy is the least worst form of government, computational modeling may be the least worst way of trying to understand the future for policy making.

  • Will post COVID-19 education be digital?: Virtual round table featuring Peter Denning, Andrew Odlyzko, Espen Andersen, and Jeffrey Johnson

    The world is experiencing large-scale social and behavioral changes in response to the COVID-19 pandemic. These changes have the potential to cause a fundamental and profound shift in the way we conduct our lives, which could have both positive and negative consequences.

    The need to function, both socially and at work, while sheltering at home and social distancing has led to the widespread realization that online meetings and remote working is viable. Digital education is particularly important, as millions of pupils and students worldwide struggle to continue studies in these difficult circumstances.

    We have posed four questions to our fellow Ubiquity editors, garnering a balanced view from academia and industry, from STEM and business (MBA) perspective, aiming to seed a follow-up debate from other editors, culminating in a free, one-hour webinar.

  • Big data: big data or big brother? that is the question now.

    This ACM Ubiquity Symposium presented some of the current thinking about big data developments across four topical dimensions: social, technological, application, and educational. While 10 articles can hardly touch the expanse of the field, we have sought to cover the most important issues and provide useful insights for the curious reader. More than two dozen authors from academia and industry provided shared their points of view, their current focus of interest and their outlines of future research. Big digital data has changed and will change the world in many ways. It will bring some big benefits in the future, but combined with big AI and big IoT devices creates several big challenges. These must be carefully addressed and properly resolved for the future benefit of humanity.

  • Corporate Security is a Big Data Problem: Big Data (Ubiquity symposium)

    In modern times, we have seen a major shift toward hybrid cloud architectures, where corporations operate in a large, highly extended eco-system. Thus, the traditional enterprise security perimeter is disappearing and evolving into the concept of security intelligence where the volume, velocity/rate, and variety of data have dramatically changed. Today, to cope with the fast-changing security landscape, we need to be able to transform huge data lakes via security analytics and big data technologies into effective security intelligence presented through a security "cockpit" to achieve a better corporate security and compliance level, support sound risk management and informed decision making. We present a high-level architecture for efficient security intelligence and the concept of a security cockpit as a point of control for the corporate security and compliance state. Therefore, we could conclude nowadays corporate security can be perceived as a big-data problem.

  • Big Data, Digitization, and Social Change: Big Data (Ubiquity symposium)

    We use the term "big data" with the understanding that the real game changer is the connection and digitization of everything. Every portfolio is affected: finance, transport, housing, food, environment, industry, health, welfare, defense, education, science, and more. The authors in this symposium will focus on a few of these areas to exemplify the main ideas and issues.

  • On Resilience of IoT Systems: The Internet of Things (Ubiquity symposium)

    At the very high level of abstraction, the Internet of Things (IoT) can be modeled as the hyper-scale, hyper-complex cyber-physical system. Study of resilience of IoT systems is the first step towards engineering of the future IoT eco-systems. Exploration of this domain is highly promising avenue for many aspiring Ph.D. and M.Sc. students.

  • On Quantum Computing: An interview with David Penkler
    In recent months, announcements on the progress toward harnessing quantum computing have solicited divers and sometimes strong reactions and opinions from academia and industry. Some say quantum computing is impossible, while others point to actual machines-raising the question as to whether they really are quantum computers. In this interview, Dave Penkler---an HP fellow whose primary interests are in cloud and data-center scale operating systems and networks---shares his view on the present and future of quantum computing. Penkler has 40 years of experience with computer hardware and software and has always had a keen interest in their evolution as enabled by the advances in science and technology. ...
  • Emergence of the Academic Computing Clouds

    Computational grids are very large-scale aggregates of communication and computation resources enabling new types of applications and bringing several benefits of economy-of-scale. The first computational grids were established in academic environments during the previous decade, and today are making inroads into the realm of corporate and enterprise computing.

    Very recently, we observe the emergence of cloud computing as a new potential super structure for corporate, enterprise and academic computing. While cloud computing shares the same original vision of grid computing articulated in the 1990s by Foster, Kesselman and others, there are significant differences.

    In this paper, we first briefly outline the architecture, technologies and standards of computational grids. We then point at some of notable examples of academic use of grids and sketch the future of research in grids. In the third section, we draw some architectural lines of cloud computing, hint at the design and technology choices and indicate some future challenges. In conclusion, we claim that academic computing clouds might appear soon, supporting the emergence of Science 2.0 activities, some of which we list shortly.

  • AI re-emerging as research in complex systems
    The history and the future of Artificial Intelligence could be summarized into three distinctive phases: embryonic, embedded and embodied. We briefly describe early efforts in AI aiming to mimic intelligent behavior, evolving later into a set of the useful, embedded and practical technologies. We project the possible future of embodied intelligent systems, able to model and understand the environment and learn from interactions, while learning and evolving in constantly changing circumstances. We conclude with the (heretical) thought that in the future, AI should re-emerge as research in complex systems. One particular embodiment of a complex system is the Intelligent Enterprise. ...
  • Corporate renewal engines
    The great, long-living companies are able to adapt to tectonic market shifts and historical changes while crossing different technological epochs. Several books have captured historical and anecdotal evidence about such extraordinary businesses, while we have a few crisp and simple business models of these great companies. ...
  • Science and Engineering of Large-Scale Complex Systems
    The world's economy can be seen as a an excellent playing field for the multiple, multi-faceted scientific disciplines and scientists. But for various reasons and causes, they are or disregarded or sometimes even carefully avoided. Kemal Delic, a lab scientist with Hewlett-Packard's R&D operations and a senior enterprise architect, explains. ...

2018 Symposia

Ubiquity symposium is an organized debate around a proposition or point of view. It is a means to explore a complex issue from multiple perspectives. An early example of a symposium on teaching computer science appeared in Communications of the ACM (December 1989).

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Ubiquity Symposium: Big Data

Table of Contents

  1. Big Data, Digitization, and Social Change (Opening Statement) by Jeffrey Johnson, Peter Denning, David Sousa-Rodrigues, Kemal A. Delic
  2. Big Data and the Attention Economy by Bernardo A. Huberman
  3. Big Data for Social Science Research by Mark Birkin
  4. Technology and Business Challenges of Big Data in the Digital Economy by Dave Penkler
  5. High Performance Synthetic Information Environments: An integrating architecture in the age of pervasive data and computing By Christopher L. Barrett, Jeffery Johnson, and Madhav Marathe
  6. Developing an Open Source "Big Data" Cognitive Computing Platform by Michael Kowolenko and Mladen Vouk
  7. When Good Machine Learning Leads to Bad Cyber Security by Tegjyot Singh Sethi and Mehmed Kantardzic
  8. Corporate Security is a Big Data Problem by Louisa Saunier and Kemal Delic
  9. Big Data: Business, technology, education, and science by Jeffrey Johnson, Luca Tesei, Marco Piangerelli, Emanuela Merelli, Riccardo Paci, Nenad Stojanovic, Paulo Leitão, José Barbosa, and Marco Amador
  10. Big Data or Big Brother? That is the question now (Closing Statement) by Jeffrey Johnson, Peter Denning, David Sousa-Rodrigues, Kemal A. Delic