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.

Transforming the latent value of big data into real value requires the great human intelligence and application of human-data scientists. Data scientists are expected to have a wide range of technical skills alongside being passionate self-directed people who are able to work easily with others and deliver high quality outputs under pressure. There are hundreds of university, commercial, and online courses in data science and related topics. Apart from people with breadth and depth of knowledge and experience in data science, we identify a new educational path to train "bridge persons" who combine knowledge of an organization's business with sufficient knowledge and understanding of data science to "bridge" between non-technical people in the business with highly skilled data scientists who add value to the business. The increasing proliferation of big data and the great advances made in data science do not herald in an era where all problems can be solved by deep learning and artificial intelligence. Although data science opens up many commercial and social opportunities, data science must complement other science in the search for new theory and methods to understand and manage our complex world.

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.

While machine learning has proven to be promising in several application domains, our understanding of its behavior and limitations is still in its nascent stages. One such domain is that of cybersecurity, where machine learning models are replacing traditional rule based systems, owing to their ability to generalize and deal with large scale attacks which are not seen before. However, the naive transfer of machine learning principles to the domain of security needs to be taken with caution. Machine learning was not designed with security in mind and as such is prone to adversarial manipulation and reverse engineering. While most data based learning models rely on a static assumption of the world, the security landscape is one that is especially dynamic, with an ongoing never ending arms race between the system designer and the attackers. Any solution designed for such a domain needs to take into account an active adversary and needs to evolve over time, in the face of emerging threats. We term this as the "Dynamic Adversarial Mining" problem, and this paper provides motivation and foundation for this new interdisciplinary area of research, at the crossroads of machine learning, cybersecurity, and streaming data mining.

The ability to leverage diverse data types requires a robust and dynamic approach to systems design. The needs of a data scientist are as varied as the questions being explored. Compute systems have focused on the management and analysis of structured data as the driving force of analytics in business. As open source platforms have evolved, the ability to apply compute to unstructured information has exposed an array of platforms and tools available to the business and technical community. We have developed a platform that meets the needs of the analytics user requirements of both structured and unstructured data. This analytics workbench is based on acquisition, transformation, and analysis using open source tools such as Nutch, Tika, Elastic, Python, PostgreSQL, and Django to implement a cognitive compute environment that can handle widely diverse data, and can leverage the ever-expanding capabilities of infrastructure in order to provide intelligence augmentation.

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.

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.

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.

While attention has always been prized above money, few people have had the means to attract it to themselves. But the new digital economy has provided everyone with a loudspeaker; thus efforts at getting noticed have rapidly escalated in global society. The attention economy focuses on the mechanisms that mediate the allocation of this scarce entity. Social networks and big data play a role in determining what is noticed and acted upon.

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.

By many indications, signs, and signals there is writing on the wall: the Internet of Things (IoT) is coming! In this closing statement, Kemal Delic briefly reflects on two possible developments for the IoT and concludes with a third, and more likely, scenario.

This paper aims to provide the view of what means IoT (Internet of Things) in energy efficiency applications, of its technical and business impacts, of its opportunities and risks for the different market players. It is concluded by the author's long term vision about the use of IoT in energy efficiency applications.

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.

Industrial Internet of Things (IOT) is a distributed network of smart sensors that enables precise control and monitoring of complex processes over arbitrary distances. The great advantage of the industrial IoT is counterbalanced by a security weakness. The insertion of a smart device capable of extracting protected data or malicious actions can infect the whole network with relative ease. Thus it becomes imperative to discover whether or not new devices have the right capabilities and compatibilities with other sensors. This article presents a zero knowledge protocol that achieves precisely that objective while keeping the sensor data private.

Cyber-attacks and breaches are often detected too late to avoid damage. While "classical" reactive cyber defenses usually work only if we have some prior knowledge about the attack methods and "allowable" patterns, properly constructed redundancy-based anomaly detectors can be more robust and often able to detect even zero day attacks. They are a step toward an oracle that uses knowable behavior of a healthy system to identify abnormalities. In the world of Internet of Things (IoT), security, and anomalous behavior of sensors and other IoT components, will be orders of magnitude more difficult unless we make those elements security aware from the start. In this article we examine the ability of redundancy-based anomaly detectors to recognize some high-risk and difficult to detect attacks on web servers---a likely management interface for many IoT stand-alone elements. In real life, it has taken long, a number of years in some cases, to identify some of the vulnerabilities and related attacks. We discuss practical relevance of the approach in the context of providing high-assurance Web-services that may belong to autonomous IoT applications and devices.

The Internet of Things (IoT) envisages expanding the current Internet with a huge number of intelligent communicating devices. Wireless sensor networks (WSNs) integrating IoT will rely on a set of the open standards striving to offer scalability and reliability in a variety of operating scenarios and conditions. Standardized protocols will tackle some of the major WSN challenges like energy efficiency, intrinsic impairments of low-power wireless medium, and self-organization. After more then a decade of tremendous standardization efforts, we can finally witness an integral IP-based WSN standardized protocol stack for IoT. Nevertheless, the current state of standards has redundancy issues and can benefit from further improvements. We would like to highlight some of the cross-layer aspects that need to be considered to bring further improvements to the standardized WSN protocol stack for the IoT.

Between prophecies of revolutions and inertiae of legacies, the Internet of Things (IoT) has already become the brand under which light processing units communicate over complex networks. Network processing is caught between demands for computation, raised by the growing complexity of the networks, and limitations imposed by performance of lightweight devices on processing. In this contribution the potential for disruptive changes against the scaling of existing technologies is discussed, specifically three main aspects of the IoT that impact network protocols and their processing: the reversal of the client/server architectures, the scavenging of spectral bands, and the federation of Internet gateways.

The Internet of Everything (IoE) is more than a $19 trillion opportunity over 10 years. Fifty billions of devices will be connected to various networks in 2020. This is bringing new technical challenges in all domains and specifically in the data processing. Distributed intelligence is one of the key technological answers. We call it "fog computing." Fog can provide intelligent connection of people, processes, data, and things in hierarchical Internet of Things networks. By supplementing the cloud and providing intermediate layers of computation, networking, and storage, fog nodes can optimize IoE deployments---greatly enhancing latency, bandwidth, reliability, security, and overall IoE network performance. The article will analyze the architecture and main design choices of this technology.

With everything and everyone accessible as a virtual resource on the web, novel applications that are created out of existing capabilities will continue to emerge. This article discusses the role and challenges for APIs in transforming the IoT capabilities.

While much attention has been focused on building sensing systems and backing cloud infrastructure in the Internet of things/Web of things (IoT/WoT) community, enabling third-party applications and services that can operate across domains and across devices has not been given much consideration. The challenge for the community is to devise standards and practices that enable integration of data from sensors across devices, users, and domains to enable new types of applications and services that facilitate much more comprehensive understanding and quantitative insights into the world around us.

Over the decades, standards have been critical for defining how to interconnect computer and networking devices across different vendors so they can seamlessly work together. Standards have been critical, not only in networking and computer interfaces, but also at the operating system and systems software level. There are many examples, such as IEEE 802, POSIX, IETF, and W3C. There was always the question of the right time to standardize (not too early and not too late), and the time to complete a standardization project always seemed too long, but inevitable. However, the contemporary industry seems to be more dynamic and evolving than it has ever been, demanding more agile processes. Open source processes and software defined (networks, storage, data centers, etc.) offer alternatives to standards. In this article we attempt to envision the future role of standards, and how they will complement and enhance alternative choices toward the same goal. We first summarize traditional standards, then discuss alternatives and a couple of use cases, and conclude with some future directions and opportunities for standardization.

For years we humans have worried about plagues, asteroids, earthquakes, eruptions, fires, floods, famines, wars, genocides, and other uncontrollable events that could wipe away our civilization. In the modern age, with so much depending on computing and communications, we have added computers to our list of potential threats. Could we perish from the increasing intelligence of computers? Denning thinks that is less of a threat than the apparently mundane march of automated bureaucracies. He also asserts that none of the possible negative outcomes is a forgone conclusion because humans teaming with machines are far more intelligent than either one alone.

Liah Greenfeld and Mark Simes have long worked together, integrating the perspectives of two very different disciplinary traditions: cultural history/historical sociology and human neuroscience. The combination of their areas of expertise in the empirical investigation of mental disorders, which severely affect intelligence---among other things---has led them to certain conclusions that may throw a special light on the question of this symposium: Will computers outcompete us all?

The Priesthood of the Singularity posits a fast approaching prospect of machines overtaking human abilities (Ray Kurzweil's The Singularity is Near, Viking Press, 2006) on the basis of the exponential rate of electronic integration---memory and processing power. In fact, they directly correlate the growth of computing technology with that of machine intelligence as if the two were connected in some simple-to-understand and predictable way. Here we present a different view based upon the fundamentals of intelligence and a more likely relationship. We conclude that machine intelligence is growing in a logarithmic (or at best linear fashion) rather than the assumed exponential rate.

In this paper a look is taken at artificial intelligence and the ways it can be brought about, either by means of a computer or through biological growth. Ways of linking the two methods are also discussed, particularly the possibilities of linking human and artificial brains together. In this regard practical experiments are referred to in which human enhancement can be achieved though linking with artificial intelligence.

The state of the art in automating basic cognitive tasks, including vision and natural language understanding, is far below human abilities. Real-world reasoning, which is an unavoidable part of many advanced forms of computer vision and natural language understanding, is particularly difficult---suggesting the advent of computers with superhuman general intelligence is not imminent. The possibility of attaining a singularity by computers that lack these abilities is discussed briefly.

To jumpstart this symposium, Espen Andersen describes the debate surrounding "technological singularity" and questions whether this is something that will happen---and if so, what the consequences might be.

While evolution has inspired algorithmic methods of heuristic optimization, little has been done in the way of using concepts of computation to advance our understanding of salient aspects of biological phenomena. The authors argue under reasonable assumptions, interesting conclusions can be drawn that are of relevance to behavioral evolution. The authors will focus on two important features of life---robustness and fitness---which, they will argue, are related to algorithmic probability and to the thermodynamics of computation, disciplines that may be capable of modeling key features of living organisms, and which can be used in formulating new algorithms of evolutionary computation.

The goal of the LabRats Science Education Program is to inspire secondary school-age students from all backgrounds to love learning about science and technology. Shawn Carlson, the Executive Director of LabRats, presents five key insights that can be integrated into any science and technology program. The purpose of which is to overhaul students' attitudes and motivation to learn. Carlson also offers detailed suggestions on how educators can use these insights to inspire their students to become lifelong learners of science and technology.

Phil Yaffe has provided numerous commentaries on various aspects of professional communication, which have helped readers more effectively articulate their own ideas about the future of computing. Here he tells us about how scientists see the world---the "scientific approach," he calls it---because he thinks many non-scientists see the world in a similar way. This realization can lower barriers of communication with scientists.

Until 2000, computer science enrollments were steadily increasing. Then suddenly students started turning to other fields; by 2008, enrollments had dropped by 50 percent. To that end, Jan Cuny has been leading a program at the National Science Foundation to increase both the number and diversity of students in computing. In this interview with Ubiquity, she discusses the magnitude of the problem and the initiatives underway to turn it around.

In this ninth symposium article, Lukáš Sekanina addresses evolutionary and evolvable hardware; answering the questions what it means for a physical system to be designed evolutionarily and on what kinds of computations such physical systems perform will be answered.

In the third installment of this symposium, which originally appeared in the Communication the ACM, we go back to 1981. More than 30 years ago, I called on ACM members to employ more experimental methods and avoid confusing hacking (tinkering) with true science. Joining a long tradition of ACM Presidents speaking up about computing as science.

In this article, David Fogel discusses the relationship between evolutionary computation and evolutionary game theory. The mathematics of evolutionary game theory relies on assumptions that often fail to describe the real-world conditions that the theory is intended to model. This article highlights those assumptions and suggests evolutionary computation may ultimately serve as a more useful approach to understanding complex adaptive systems in nature.

The first article in this symposium, which originally appeared in the Communication the ACM, is courtesy of ACM President Vinton Cerf. Earlier this year, he called on all ACM members to commit to building a stronger science base for computer science. Cerf cites numerous open questions, mostly in software development, that cry out for experimental studies.

The recent interest in encouraging more middle and high school students to prepare for careers in science, technology, engineering, or mathematics (STEM) has rekindled the old debate about whether computer science is really science. It matters today because computing is such a central field, impacting so many other fields, and yet it is often excluded from high school curricula because it is not seen as a science. In this symposium, fifteen authors examine different aspects from what is science, to natural information processes, to new science-enabled approaches in STEM education.

In this article, Moshe Sipper discusses a foreseeable future in which an entirely new paradigm of producing software will emerge. Sipper calls this software engineering revolution, "Darwinian Software Engineering"---a time when it will be possible to program computers by means of evolution.

In this article evolutionary computation (EC) is considered as a kind of nature-inspired computing (NIC) paradigm. EC not only has great effect on the development of computing methods from structure to process, but also has great effect on many aspects of our society as a ubiquitous or general computational thinking. EC is still one of the best choices for problem solving among all methods when people face more and more complex problems.

During the past 35 years the evolutionary computation research community has been studying properties of evolutionary algorithms. Many claims have been made---these varied from a promise of developing an automatic programming methodology to solving virtually any optimization problem (as some evolutionary algorithms are problem independent). However, the most important claim was related to applicability of evolutionary algorithms to solving very complex business problems, i.e. problems, where other techniques failed. So it might be worthwhile to revisit this claim and to search for evolutionary algorithm-based software applications, which were accepted by businesses and industries. In this article Zbigniew Michalewicz attempts to identify reasons for the mismatch between the efforts of hundreds of researchers who make substantial contribution to the field of evolutionary computation and the number of real-world applications, which are based on concepts of evolutionary algorithms.

In this article in the ACM Ubiquity symposium on evolutionary computation Max H. Garzon presents reflections on the connections between evolutionary computation, natural computation, and current definitions of computer science. The primary aim and result is a more coherent, comprehensive and modern definition of computer science.

In this second article in the ACM Ubiquity symposium on evolutionary computation Hans-Paul Schwefel explores the effects of simulating evolutionary mechanisms.

In this third article in the ACM Ubiquity symposium on evolutionary computation Xin Yao provides a deeper understanding of evolutionary algorithms in the context of classical computational paradigms. This article discusses some of the most important issues in evolutionary computation. Three major areas are identified. The first is the theoretical foundation of evolutionary computation, especially the computational time complexity analysis. The second is on algorithm design, especially on hybridization, memetic algorithms, algorithm portfolios and ensembles of algorithms. The third is co-evolution, which seems to be under studied in both theory and practice. The primary aim of this article is to stimulate further discussions, rather than to offer any solutions.

Evolution is one of the indispensable processes of life. After biologists found basic laws of evolution, computer scientists began simulating evolutionary processes and using operations discovered in nature for solving problems with computers. As a result, they brought forth evolutionary computation, inventing different kinds operations and procedures, such as genetic algorithms or genetic programming, which imitated natural biological processes. Thus, the main goal of our Symposium is exploration of the essence and characteristic properties of evolutionary computation in the context of life and computation.

The "computation" symposium presents the reflections of thinkers from many sectors of computing on the fundamental question in the background of everything we do as computing professionals. While many of us have too many immediate tasks to allow us time for our own deep reflection, we do appreciate when others have done this for us. Peter Freeman points out, by analogy, that as citizens of democracies we do not spend a lot of time reflecting on the question, "What is a democracy," but from time to time we find it helpful to see what philosophers and political scientists are saying about the context in which we act as citizens.

Editor's Introduction This fourteenth piece is inspired by a question left over from the Ubiquity Symposium entitled What is Computation? Peter J. Denning Editor

Computing saw the light as a branch of mathematics in the '40s, and progressively revealed ever new aspects [gol97]. Nowadays even laymen have become aware of the broad assortment of functions achieved by systems, and the prismatic nature of computing challenges thinkers who explore the various topics that substantiate computer science [mul98].

In this thirteenth piece to the Ubiquity symposium discussing What is computation? Melanie Mitchell discusses the idea that biological computation is a process that occurs in nature, not merely in computer simulations of nature.
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In this twelfth piece to the Ubiquity symposium discussing What is computation? Erol Gelenbe reviews computation in natural systems, focusing mainly on biology and citing examples of the computation that is inherent in chemistry, natural selection, gene regulatory networks, and neuronal systems.
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In this eleventh piece to the Ubiquity symposium discussing What is computation? Jeffrey P. Buzen develops a new computational model for representing computations that arise when deterministic algorithms process workloads whose detailed structure is uncertain.
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In this tenth piece to the Ubiquity symposium discussing What is computation? Joseph Traub shares his views about using the Turing Machine model and the real number model for solving continuous scientific problems.
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In this ninth piece to the Ubiquity symposium discussing What is computation? Alfred V. Aho shares his views about the importance of computational thinking in answering the question.
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In this sixth article in the ACM Ubiquity symposium,What is Computation? Ruzena Bajcsy of the University of California-Berkeley explains that computation can be seen as a transformation or function of information.
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In this fifth article in the ACM Ubiquity symposium on What is computation? Paul S. Rosenbloom explains why he believes computing is the fourth great scientific domain, on par with the physical, life, and social sciences.
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In this seventh article in the ACM Ubiquity symposium, What is Computation?, Dave Bacon of University of Washington explains why he thinks discussing the question is as important as thinking about what it means to be self-aware. —Editor

Most people understand a computation as a process evoked when a computational agent acts on its inputs under the control of an algorithm. The classical Turing machine model has long served as the fundamental reference model because an appropriate Turing machine can simulate every other computational model known. The Turing model is a good abstraction for most digital computers because the number of steps to execute a Turing machine algorithm is predictive of the running time of the computation on a digital computer. However, the Turing model is not as well matched for the natural, interactive, and continuous information processes frequently encountered today. Other models whose structures more closely match the information processes involved give better predictions of running time and space. Models based on transforming representations may be useful.

In this second article in the ACM Ubiquity symposium on 'What is computation?' Peter Wegner provides a history of the evolution of comptuation. --Editor

Various authors define forms of computation as specialized types of processes. As the scope of computation widens, the range of such specialties increases. Dennis J. Frailey posits that the essence of computation can be found in any form of process, hence the title and the thesis of this paper in the Ubiquity symposium discussion what is computation. --Editor

In the second in the series of articles in the Ubiquity Symposium What is Computation?, Prof. John S. Conery of the University of Oregon explains why he believes computation can be seen as symbol manipulation. For more articles in this series, see table of contents in the http://ubiquity.acm.org/article.cfm?id=1870596 Editors Introduction to the symposium. --Editor