Topics: big data

'Big Data' Will Change How You Play, See the Doctor, Even Eat

Curated on Posted on by Stefaan Verhulst

We’re entering an age of personal big data, and its impact on our lives will surpass that of the Internet. Data will answer questions we could never before answer with certainty—everyday questions like whether that dress actually makes you look fat, or profound questions about precisely how long you will live.

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Every 20 years or so, a powerful technology moves from the realm of backroom expertise and into the hands of the masses. In the late-1970s, computing made that transition—from mainframes in glass-enclosed rooms to personal computers on desks. In the late 1990s, the first web browsers made networks, which had been for science labs and the military, accessible to any of us, giving birth to the modern Internet.

Each transition touched off an explosion of innovation and reshaped work and leisure. In 1975, 50,000 PCs were in use worldwide. Twenty years later: 225 million. The number of Internet users in 1995 hit 16 million. Today it’s more than 3 billion. In much of the world, it’s hard to imagine life without constant access to both computing and networks.

The 2010s will be the coming-out party for data. Gathering, accessing and gleaning insights from vast and deep data has been a capability locked inside enterprises long enough. Cloud computing and mobile devices now make it possible to stand in a bathroom line at a baseball game while tapping into massive computing power and databases. On the other end, connected devices such as the Nest thermostat or Fitbit health monitor and apps on smartphones increasingly collect new kinds of information about everyday personal actions and habits, turning it into data about ourselves.

More than 80 percent of data today is unstructured: tangles of YouTube videos, news stories, academic papers, social network comments. Unstructured data has been almost impossible to search for, analyze and mix with other data. A new generation of computers—cognitive computing systems that learn from data—will read tweets or e-books or watch video, and comprehend its content. Somewhat like brains, these systems can link diverse bits of data to come up with real answers, not just search results.

Such systems can work in natural language. The progenitor is the IBM Watson computer that won on Jeopardy in 2011. Next-generation Watsons will work like a super-powered Google. (Google today is a data-searching wimp compared with what’s coming.)

Sports offers a glimpse into the data age. Last season the NBA installed in every arena technology that can “watch” a game and record, in 48 minutes of action, more than 4 million data points about every movement and shot. That alone could yield new insights for NBA coaches, such as which group of five players most efficiently passes the ball around….

Think again about life before personal computing and the Internet. Even if someone told you that you’d eventually carry a computer in your pocket that was always connected to global networks, you would’ve had a hard time imagining what that meant—imagining WhatsApp, Siri, Pandora, Uber, Evernote, Tinder.

As data about everything becomes ubiquitous and democratized, layered on top of computing and networks, it will touch off the most spectacular technology explosion yet. We can see the early stages now. “Big data” doesn’t even begin to describe the enormity of what’s coming next.”

Big Money, Uncertain Return

Curated on Posted on by Stefaan Verhulst

Mary K. Pratt  in a MIT Technology Review Special Report on Data-Driven Health Care: “Hospitals are spending billions collecting and analyzing medical data. The one data point no one is tracking: the payoff…. Ten years ago, Kaiser Permanente began building a $4 billion electronic-health-record system that includes a comprehensive collection of health-care data ranging from patients’ treatment records to research-based clinical advice. Now Kaiser has added advanced analytics tools and data from more sources, including a pilot program that integrates information from patients’ medical devices.

Faced with new government regulations and insurer pressure to control costs, other health-care organizations are following Kaiser’s example and increasing their use of analytics. The belief: that mining their vast quantities of patient data will yield insights into the best treatments at the lowest cost.

But just how big will the financial payoff be? Terhilda Garrido, vice president of health IT transformation and analytics at Kaiser, admits she doesn’t know. Nor do other health-care leaders. The return on investment for health-care analytics programs remains elusive and nearly impossible for most to calculate…

Opportunities to identify the most effective treatments could slip away if CIOs and their teams aren’t able to quantify the return on their analytics investments. Health-care providers are under increasing pressure to cut costs in an era of capped billing, and executives at medical organizations won’t okay spending their increasingly limited dollars on data warehouses, analytics software, and data scientists if they can’t be sure they’ll see real benefit.

A new initiative at Cleveland Clinic shows the opportunities and challenges. By analyzing patients’ records on their overall health and medical conditions, the medical center determines which patients coming in for hip and knee replacements can get postoperative services in their own homes (the most cost-effective option), which ones will need a short stay in a skilled nursing facility, and which ones will have longer stints in a skilled nursing facility (the most costly option). The classifications control costs while still ensuring the best possible medical outcomes, says CIO C. Martin Harris.

That does translate into real—and significant—financial benefits, but Harris wonders how to calculate the payoff from his data investment. Should the costs of every system from which patient data is pulled be part of the equation in addition to the costs of the data warehouse and analytics tools? Calculating how much money is saved by implementing better protocols is not straightforward either. Harris hesitates to attribute better, more cost-effective patient outcomes solely to analytics when many other factors are also likely contributors…”

No silver bullet: De-identification still doesn’t work

Curated on Posted on by Stefaan Verhulst

Arvind Narayanan and Edward W. Felten: “Paul Ohm’s 2009 article Broken Promises of Privacy spurred a debate in legal and policy circles on the appropriate response to computer science research on re-identification techniques. In this debate, the empirical research has often been misunderstood or misrepresented. A new report by Ann Cavoukian and Daniel Castro is full of such inaccuracies, despite its claims of “setting the record straight.” In a response to this piece, Ed Felten and I point out eight of our most serious points of disagreement with Cavoukian and Castro. The thrust of our arguments is that (i) there is no evidence that de-identification works either in theory or in practice and (ii) attempts to quantify its efficacy are unscientific and promote a false sense of security by assuming unrealistic, artificially constrained models of what an adversary might do. Specifically, we argue that:

  1. There is no known effective method to anonymize location data, and no evidence that it’s meaningfully achievable.
  2. Computing re-identification probabilities based on proof-of-concept demonstrations is silly.
  3. Cavoukian and Castro ignore many realistic threats by focusing narrowly on a particular model of re-identification.
  4. Cavoukian and Castro concede that de-identification is inadequate for high-dimensional data. But nowadays most interesting datasets are high-dimensional.
  5. Penetrate-and-patch is not an option.
  6. Computer science knowledge is relevant and highly available.
  7. Cavoukian and Castro apply different standards to big data and re-identification techniques.
  8. Quantification of re-identification probabilities, which permeates Cavoukian and Castro’s arguments, is a fundamentally meaningless exercise.

Data privacy is a hard problem. Data custodians face a choice between roughly three alternatives: sticking with the old habit of de-identification and hoping for the best; turning to emerging technologies like differential privacy that involve some trade-offs in utility and convenience; and using legal agreements to limit the flow and use of sensitive data. These solutions aren’t fully satisfactory, either individually or in combination, nor is any one approach the best in all circumstances. Change is difficult. When faced with the challenge of fostering data science while preventing privacy risks, the urge to preserve the status quo is understandable. However, this is incompatible with the reality of re-identification science. If a “best of both worlds” solution exists, de-identification is certainly not that solution. Instead of looking for a silver bullet, policy makers must confront hard choices.”

To improve quality, value of patient data, get them involved, study says

Curated on Posted on by Stefaan Verhulst

Joseph Conn at Vital Signs: “The key to the future use of patient-generated data is focusing on data that patients want to produce, own and use and making it easy for them to produce it.
At least, that’s the take of four co-authors from Duke University in an article in this month’s healthcare policy journal Health Affairs. The July issue is chockablock with articles on the many forms and uses of Big Data.
“We observe that the key to high-quality, patient-generated data is to have immediate and actionable data so that patients experience the importance of the data for their own care as well as research purposes,” the authors said in “Assessing the Value of Patient-Generated Data to Comparative Effectiveness Research.”
Patient-generated data, which the authors describe as patient-reported outcomes, or PRO will be “critical to developing the evidence base that informs decisions made by patients, providers and policymakers in pursuit of high-value medical care,” they predict.
“The easier it is for patients and clinicians to navigate (personal data) the more relevant that information will be to patient care, the more invested patients and clinics will be in contributing high-quality data, and the better the data in the big-data ecosystem will be,” they write.
“Analysis show that data quality improves over time and that the amount of missing data declines as patients experience the attention to their symptoms and actions that result from the information they provide,” the authors say…”

EU: Communication on data-driven economy

Curated on Posted on by Stefaan Verhulst
Press Release: “The Commission adopted the Communication on the data-driven economy as a response to the European Council’s conclusions of October 2013, which focused on the digital economy, innovation and services as drivers for growth and jobs and called for EU action to provide the right framework conditions for a single market for big data and cloud computing. This Communication describes the features of the data-driven economy of the future and sets out operational conclusions to support and accelerate the transition towards it. It also sets out current and future activities in the field of cloud computing.”
Related Documents:
Communication data-driven economy
Staff Working Document accompanying the Communication on the data-driven economy
Recommended reading: Action 3: Open up public data resources for re-use

Diffusers of Useful Knowledge

Curated on Posted on by Stefaan Verhulst

Book review of Visions of Science: Books and Readers at the Dawn of the Victorian Age (By James A Secord): “For a moment in time, just before Victoria became queen, popular science seemed to offer answers to everything. Around 1830, revolutionary information technology – steam-powered presses and paper-making machines – made possible the dissemination of ‘useful knowledge’ to a mass public. At that point professional scientists scarcely existed as a class, but there were genteel amateur researchers who, with literary panache, wrote for a fascinated lay audience.
The term ‘scientist’ was invented only in 1833, by the polymath William Whewell, who gave it a faintly pejorative odour, drawing analogies to ‘journalist’, ‘sciolist’, ‘atheist’, and ‘tobacconist’. ‘Better die … than bestialise our tongue by such barbarisms,’ scowled the geologist Adam Sedgwick. ‘To anyone who respects the English language,’ said T H Huxley, ‘I think “Scientist” must be about as pleasing a word as “Electrocution”.’ These men preferred to call themselves ‘natural philosophers’ and there was a real distinction. Scientists were narrowly focused utilitarian data-grubbers; natural philosophers thought deeply and wrote elegantly about the moral, cosmological and metaphysical implications of their work….
Visions of Science offers vignettes of other pre-Darwin scientific writers who generated considerable buzz in their day. Consolations in Travel, a collection of meta-scientific musings by the chemist Humphry Davy, published in 1830, played a salient role in the plot of The Tenant of Wildfell Hall (1848), with Anne Brontë being reasonably confident that her readers would get the reference. The general tone of such works was exemplified by the astronomer John Herschel in Preliminary Discourse on the Study of Natural Philosophy (1831) – clear, empirical, accessible, supremely rational and even-tempered. These authors communicated a democratic faith that science could be mastered by anyone, perhaps even a woman.
Mary Somerville’s On the Connexion of the Physical Sciences (1834) pulled together mathematics, astronomy, electricity, light, sound, chemistry and meteorology in a grand middlebrow synthesis. She even promised her readers that the sciences were converging on some kind of unified field theory, though that particular Godot has never arrived. For several decades the book sold hugely and was pirated widely, but as scientists became more specialised and professional, it began to look like a hodgepodge. Writing in Nature in 1874, James Clerk Maxwell could find no theme in her pudding, calling it a miscellany unified only by the bookbinder.
The same scientific populism made possible the brief supernova of phrenology. Anyone could learn the fairly simple art of reading bumps on the head once the basics had been broadcast by new media. The first edition of George Combe’s phrenological treatise The Constitution of Man, priced at six shillings, sold barely a hundred copies a year. But when the state-of-the-art steam presses of Chambers’s Edinburgh Journal (the first mass-market periodical) produced a much cheaper version, 43,000 copies were snapped up in a matter of months. What the phrenologists could not produce were research papers backing up their claims, and a decade later the movement was moribund.
Charles Babbage, in designing his ‘difference engine’, anticipated all the basic principles of the modern computer – including ‘garbage in, garbage out’. In Reflections on the Decline of Science in England (1830) he accused his fellow scientists of routinely suppressing, concocting or cooking data. Such corruption (he confidently insisted) could be cleaned up if the government generously subsidised scientific research. That may seem naive today, when we are all too aware that scientists often fudge results to keep the research money flowing. Yet in the era of the First Reform Act, everything appeared to be reformable. Babbage even stood for parliament in Finsbury, on a platform of freedom of information for all. But he split the scientific radical vote with Thomas Wakley, founder of The Lancet, and the Tory swept home.
After his sketches of these forgotten bestsellers, Secord concludes with the literary bomb that blew them all up. In Sartor Resartus Thomas Carlyle fiercely deconstructed everything the popular scientists stood for. Where they were cool, rational, optimistic and supremely organised, he was frenzied, mystical, apocalyptic and deliberately nonsensical. They assumed that big data represented reality; he saw that it might be all pretence, fabrication, image – in a word, ‘clothes’. A century and a half before Microsoft’s emergence, Carlyle grasped the horror of universal digitisation: ‘Shall your Science proceed in the small chink-lighted, or even oil-lighted, underground workshop of Logic alone; and man’s mind become an Arithmetical Mill?’ That was a dig at the clockwork utilitarianism of both John Stuart Mill and Babbage: the latter called his central processing unit a ‘mill’.
The scientific populists sincerely aimed to democratise information. But when the movement was institutionalised in the form of mechanics’ institutes and the Society for the Diffusion of Useful Knowledge, did it aim at anything more than making workers more productive? Babbage never completed his difference engine, in part because he treated human beings – including the artisans who were supposed to execute his designs – as programmable machines. And he was certain that Homo sapiens was not the highest form of intelligence in the universe. On another planet somewhere, he suggested, the Divine Programmer must have created Humanity 2.0….”

Big Data from the bottom up

Curated on Posted on by Stefaan Verhulst

Paper by Nick Couldry and Alison Powell in the Journal Big Data and Society: “This short article argues that an adequate response to the implications for governance raised by ‘Big Data’ requires much more attention to agency and reflexivity than theories of ‘algorithmic power’ have so far allowed. It develops this through two contrasting examples: the sociological study of social actors used of analytics to meet their own social ends (for example, by community organisations) and the study of actors’ attempts to build an economy of information more open to civic intervention than the existing one (for example, in the environmental sphere). The article concludes with a consideration of the broader norms that might contextualise these empirical studies, and proposes that they can be understood in terms of the notion of voice, although the practical implementation of voice as a norm means that voice must sometimes be considered via the notion of transparency”

Mass Big Data Report

Curated on Posted on by Stefaan Verhulst
Mass Tech: “The 2014 Mass Big Data Report: A Foundation for Global Leadership has been assembled and released with support from the Innovation Institute at the Massachusetts Technology Collaborative and the Massachusetts Competitive Partnership.  This report represents a foundational analysis of the regional Mass Big Data ecosystem and its position as a global leader in the expanding fields of big data, open data, and analytics.
As a special project of the Governor’s Mass Big Data Initiative, this report seeks to provide an initial baseline understanding of the landscape of the Mass Big Data ecosystem and its challenges, opportunities, and strong potential for growth.
Through this work, we are pleased to report that the Mass Big Data ecosystem represents an extraordinarily fertile region for growth in data-driven enterprise and offers a unique combination of advantages on which to build the future of our data-rich world.
With strengths across the spectrum of big data industry sectors and in key supporting areas such as talent development, research, and innovation, our region is producing the people, businesses, and products that fuel the explosive growth in this expanding field.
To download the report, click on the image below:

Urban Analytics (Updated and Expanded)

Curated on Posted on by Andrew Young

As part of an ongoing effort to build a knowledge base for the field of opening governance by organizing and disseminating its learnings, the GovLab Selected Readings series provides an annotated and curated collection of recommended works on key opening governance topics. In this edition, we explore the literature on Urban Analytics. To suggest additional readings on this or any other topic, please email [email protected].

Data and its uses for Governance

Urban Analytics places better information in the hands of citizens as well as government officials to empower people to make more informed choices. Today, we are able to gather real-time information about traffic, pollution, noise, and environmental and safety conditions by culling data from a range of tools: from the low-cost sensors in mobile phones to more robust monitoring tools installed in our environment. With data collected and combined from the built, natural and human environments, we can develop more robust predictive models and use those models to make policy smarter.

With the computing power to transmit and store the data from these sensors, and the tools to translate raw data into meaningful visualizations, we can identify problems as they happen, design new strategies for city management, and target the application of scarce resources where they are most needed.

Selected Reading List (in alphabetical order)

Annotated Selected Reading List (in alphabetical order)
Amini, L., E. Bouillet, F. Calabrese, L. Gasparini, and O. Verscheure. “Challenges and Results in City-scale Sensing.” In IEEE Sensors, 59–61, 2011. http://bit.ly/1doodZm.

  • This paper examines “how city requirements map to research challenges in machine learning, optimization, control, visualization, and semantic analysis.”
  • The authors raises several research challenges including how to extract accurate information when the data is noisy and sparse; how to represent findings from digital pervasive technologies; and how people interact with one another and their environment.

Batty, M., K. W. Axhausen, F. Giannotti, A. Pozdnoukhov, A. Bazzani, M. Wachowicz, G. Ouzounis, and Y. Portugali. “Smart Cities of the Future.” The European Physical Journal Special Topics 214, no. 1 (November 1, 2012): 481–518. http://bit.ly/HefbjZ.

  • This paper explores the goals and research challenges involved in the development of smart cities that merge ICT with traditional infrastructures through digital technologies.
  • The authors put forth several research objectives, including: 1) to explore the notion of the city as a laboratory for innovation; 2) to develop technologies that ensure equity, fairness and realize a better quality of city life; and 3) to develop technologies that ensure informed participation and create shared knowledge for democratic city governance.
  • The paper also examines several contemporary smart city initiatives, expected paradigm shifts in the field, benefits, risks and impacts.

Budde, Paul. “Smart Cities of Tomorrow.” In Cities for Smart Environmental and Energy Futures, edited by Stamatina Th Rassia and Panos M. Pardalos, 9–20. Energy Systems. Springer Berlin Heidelberg, 2014. http://bit.ly/17MqPZW.

  • This paper examines the components and strategies involved in the creation of smart cities featuring “cohesive and open telecommunication and software architecture.”
  • In their study of smart cities, the authors examine smart and renewable energy; next-generation networks; smart buildings; smart transport; and smart government.
  • They conclude that for the development of smart cities, information and communication technology (ICT) is needed to build more horizontal collaborative structures, useful data must be analyzed in real time and people and/or machines must be able to make instant decisions related to social and urban life.

Cardone, G., L. Foschini, P. Bellavista, A. Corradi, C. Borcea, M. Talasila, and R. Curtmola. “Fostering Participaction in Smart Cities: a Geo-social Crowdsensing Platform.” IEEE Communications
Magazine 51, no. 6 (2013): 112–119. http://bit.ly/17iJ0vZ.

  • This article examines “how and to what extent the power of collective although imprecise intelligence can be employed in smart cities.”
  • To tackle problems of managing the crowdsensing process, this article proposes a “crowdsensing platform with three main original technical aspects: an innovative geo-social model to profile users along different variables, such as time, location, social interaction, service usage, and human activities; a matching algorithm to autonomously choose people to involve in participActions and to quantify the performance of their sensing; and a new Android-based platform to collect sensing data from smart phones, automatically or with user help, and to deliver sensing/actuation tasks to users.”

Chen, Chien-Chu. “The Trend towards ‘Smart Cities.’” International Journal of Automation and Smart Technology. June 1, 2014. http://bit.ly/1jOOaAg.

  • In this study, Chen explores the ambitions, prevalence and outcomes of a variety of smart cities, organized into five categories:
    • Transportation-focused smart cities
    • Energy-focused smart cities
    • Building-focused smart cities
    • Water-resources-focused smart cities
    • Governance-focused smart cities
  • The study finds that the “Asia Pacific region accounts for the largest share of all smart city development plans worldwide, with 51% of the global total. Smart city development plans in the Asia Pacific region tend to be energy-focused smart city initiatives, aimed at easing the pressure on energy resources that will be caused by continuing rapid urbanization in the future.”
  • North America, on the other hand is generally more geared toward energy-focused smart city development plans. “In North America, there has been a major drive to introduce smart meters and smart electric power grids, integrating the electric power sector with information and communications technology (ICT) and replacing obsolete electric power infrastructure, so as to make cities’ electric power systems more reliable (which in turn can help to boost private-sector investment, stimulate the growth of the ‘green energy’ industry, and create more job opportunities).”
  • Looking to Taiwan as an example, Chen argues that, “Cities in different parts of the world face different problems and challenges when it comes to urban development, making it necessary to utilize technology applications from different fields to solve the unique problems that each individual city has to overcome; the emphasis here is on the development of customized solutions for smart city development.”

Domingo, A., B. Bellalta, M. Palacin, M. Oliver and E. Almirall. “Public Open Sensor Data: Revolutionizing Smart Cities.” Technology and Society Magazine, IEEE 32, No. 4. Winter 2013. http://bit.ly/1iH6ekU.

  • In this article, the authors explore the “enormous amount of information collected by sensor devices” that allows for “the automation of several real-time services to improve city management by using intelligent traffic-light patterns during rush hour, reducing water consumption in parks, or efficiently routing garbage collection trucks throughout the city.”
  • They argue that, “To achieve the goal of sharing and open data to the public, some technical expertise on the part of citizens will be required. A real environment – or platform – will be needed to achieve this goal.” They go on to introduce a variety of “technical challenges and considerations involved in building an Open Sensor Data platform,” including:
    • Scalability
    • Reliability
    • Low latency
    • Standardized formats
    • Standardized connectivity
  • The authors conclude that, despite incredible advancements in urban analytics and open sensing in recent years, “Today, we can only imagine the revolution in Open Data as an introduction to a real-time world mashup with temperature, humidity, CO2 emission, transport, tourism attractions, events, water and gas consumption, politics decisions, emergencies, etc., and all of this interacting with us to help improve the future decisions we make in our public and private lives.”

Harrison, C., B. Eckman, R. Hamilton, P. Hartswick, J. Kalagnanam, J. Paraszczak, and P. Williams. “Foundations for Smarter Cities.” IBM Journal of Research and Development 54, no. 4 (2010): 1–16. http://bit.ly/1iha6CR.

  • This paper describes the information technology (IT) foundation and principles for Smarter Cities.
  • The authors introduce three foundational concepts of smarter cities: instrumented, interconnected and intelligent.
  • They also describe some of the major needs of contemporary cities, and concludes that Creating the Smarter City implies capturing and accelerating flows of information both vertically and horizontally.

Hernández-Muñoz, José M., Jesús Bernat Vercher, Luis Muñoz, José A. Galache, Mirko Presser, Luis A. Hernández Gómez, and Jan Pettersson. “Smart Cities at the Forefront of the Future Internet.” In The Future Internet, edited by John Domingue, Alex Galis, Anastasius Gavras, Theodore Zahariadis, Dave Lambert, Frances Cleary, Petros Daras, et al., 447–462. Lecture Notes in Computer Science 6656. Springer Berlin Heidelberg, 2011. http://bit.ly/HhNbMX.

  • This paper explores how the “Internet of Things (IoT) and Internet of Services (IoS), can become building blocks to progress towards a unified urban-scale ICT platform transforming a Smart City into an open innovation platform.”
  • The authors examine the SmartSantander project to argue that, “the different stakeholders involved in the smart city business is so big that many non-technical constraints must be considered (users, public administrations, vendors, etc.).”
  • The authors also discuss the need for infrastructures at the, for instance, European level for realistic large-scale experimentally-driven research.

Hoon-Lee, Jung, Marguerite Gong Hancock, Mei-Chih Hu. “Towards an effective framework for building smart cities: Lessons from Seoul and San Francisco.” Technological Forecasting and Social Change. Ocotober 3, 2013. http://bit.ly/1rzID5v.

  • In this study, the authors aim to “shed light on the process of building an effective smart city by integrating various practical perspectives with a consideration of smart city characteristics taken from the literature.”
  • They propose a conceptual framework based on case studies from Seoul and San Francisco built around the following dimensions:
    • Urban openness
    • Service innovation
    • Partnerships formation
    • Urban proactiveness
    • Smart city infrastructure integration
    • Smart city governance
  • The authors conclude with a summary of research findings featuring “8 stylized facts”:
    • Movement towards more interactive services engaging citizens;
    • Open data movement facilitates open innovation;
    • Diversifying service development: exploit or explore?
    • How to accelerate adoption: top-down public driven vs. bottom-up market driven partnerships;
    • Advanced intelligent technology supports new value-added smart city services;
    • Smart city services combined with robust incentive systems empower engagement;
    • Multiple device & network accessibility can create network effects for smart city services;
    • Centralized leadership implementing a comprehensive strategy boosts smart initiatives.

Kamel Boulos, Maged N. and Najeeb M. Al-Shorbaji. “On the Internet of Things, smart cities and the WHO Healthy Cities.” International Journal of Health Geographics 13, No. 10. 2014. http://bit.ly/Tkt9GA.

  • In this article, the authors give a “brief overview of the Internet of Things (IoT) for cities, offering examples of IoT-powered 21st century smart cities, including the experience of the Spanish city of Barcelona in implementing its own IoT-driven services to improve the quality of life of its people through measures that promote an eco-friendly, sustainable environment.”
  • The authors argue that one of the central needs for harnessing the power of the IoT and urban analytics is for cities to “involve and engage its stakeholders from a very early stage (city officials at all levels, as well as citizens), and to secure their support by raising awareness and educating them about smart city technologies, the associated benefits, and the likely challenges that will need to be overcome (such as privacy issues).”
  • They conclude that, “The Internet of Things is rapidly gaining a central place as key enabler of the smarter cities of today and the future. Such cities also stand better chances of becoming healthier cities.”

Keller, Sallie Ann, Steven E. Koonin, and Stephanie Shipp. “Big Data and City Living – What Can It Do for Us?” Significance 9, no. 4 (2012): 4–7. http://bit.ly/166W3NP.

  • This article provides a short introduction to Big Data, its importance, and the ways in which it is transforming cities. After an overview of the social benefits of big data in an urban context, the article examines its challenges, such as privacy concerns and institutional barriers.
  • The authors recommend that new approaches to making data available for research are needed that do not violate the privacy of entities included in the datasets. They believe that balancing privacy and accessibility issues will require new government regulations and incentives.

Kitchin, Rob. “The Real-Time City? Big Data and Smart Urbanism.” SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, July 3, 2013. http://bit.ly/1aamZj2.

  • This paper focuses on “how cities are being instrumented with digital devices and infrastructure that produce ‘big data’ which enable real-time analysis of city life, new modes of technocratic urban governance, and a re-imagining of cities.”
  • The authors provide “a number of projects that seek to produce a real-time analysis of the city and provides a critical reflection on the implications of big data and smart urbanism.”

Mostashari, A., F. Arnold, M. Maurer, and J. Wade. “Citizens as Sensors: The Cognitive City Paradigm.” In 2011 8th International Conference Expo on Emerging Technologies for a Smarter World (CEWIT), 1–5, 2011. http://bit.ly/1fYe9an.

  • This paper argues that. “implementing sensor networks are a necessary but not sufficient approach to improving urban living.”
  • The authors introduce the concept of the “Cognitive City” – a city that can not only operate more efficiently due to networked architecture, but can also learn to improve its service conditions, by planning, deciding and acting on perceived conditions.
  • Based on this conceptualization of a smart city as a cognitive city, the authors propose “an architectural process approach that allows city decision-makers and service providers to integrate cognition into urban processes.”

Oliver, M., M. Palacin, A. Domingo, and V. Valls. “Sensor Information Fueling Open Data.” In Computer Software and Applications Conference Workshops (COMPSACW), 2012 IEEE 36th Annual, 116–121, 2012. http://bit.ly/HjV4jS.

  • This paper introduces the concept of sensor networks as a key component in the smart cities framework, and shows how real-time data provided by different city network sensors enrich Open Data portals and require a new architecture to deal with massive amounts of continuously flowing information.
  • The authors’ main conclusion is that by providing a framework to build new applications and services using public static and dynamic data that promote innovation, a real-time open sensor network data platform can have several positive effects for citizens.

Perera, Charith, Arkady Zaslavsky, Peter Christen and Dimitrios Georgakopoulos. “Sensing as a service model for smart cities supported by Internet of Things.” Transactions on Emerging Telecommunications Technologies 25, Issue 1. January 2014. http://bit.ly/1qJLDP9.

  • This paper looks into the “enormous pressure towards efficient city management” that has “triggered various Smart City initiatives by both government and private sector businesses to invest in information and communication technologies to find sustainable solutions to the growing issues.”
  • The authors explore the parallel advancement of the Internet of Things (IoT), which “envisions to connect billions of sensors to the Internet and expects to use them for efficient and effective resource management in Smart Cities.”
  • The paper proposes the sensing as a service model “as a solution based on IoT infrastructure.” The sensing as a service model consists of four conceptual layers: “(i) sensors and sensor owners; (ii) sensor publishers (SPs); (iii) extended service providers (ESPs); and (iv) sensor data consumers. They go on to describe how this model would work in the areas of waste management, smart agriculture and environmental management.

Privacy, Big Data, and the Public Good: Frameworks for Engagement. Edited by Julia Lane, Victoria Stodden, Stefan Bender, and Helen Nissenbaum; Cambridge University Press, 2014. http://bit.ly/UoGRca.

  • This book focuses on the legal, practical, and statistical approaches for maximizing the use of massive datasets while minimizing information risk.
  • “Big data” is more than a straightforward change in technology.  It poses deep challenges to our traditions of notice and consent as tools for managing privacy.  Because our new tools of data science can make it all but impossible to guarantee anonymity in the future, the authors question whether it possible to truly give informed consent, when we cannot, by definition, know what the risks are from revealing personal data either for individuals or for society as a whole.
  • Based on their experience building large data collections, authors discuss some of the best practical ways to provide access while protecting confidentiality.  What have we learned about effective engineered controls?  About effective access policies?  About designing data systems that reinforce – rather than counter – access policies?  They also explore the business, legal, and technical standards necessary for a new deal on data.
  • Since the data generating process or the data collection process is not necessarily well understood for big data streams, authors discuss what statistics can tell us about how to make greatest scientific use of this data. They also explore the shortcomings of current disclosure limitation approaches and whether we can quantify the extent of privacy loss.

Schaffers, Hans, Nicos Komninos, Marc Pallot, Brigitte Trousse, Michael Nilsson, and Alvaro Oliveira. “Smart Cities and the Future Internet: Towards Cooperation Frameworks for Open Innovation.” In The Future Internet, edited by John Domingue, Alex Galis, Anastasius Gavras, Theodore Zahariadis, Dave Lambert, Frances Cleary, Petros Daras, et al., 431–446. Lecture Notes in Computer Science 6656. Springer Berlin Heidelberg, 2011. http://bit.ly/16ytKoT.

  • This paper “explores ‘smart cities’ as environments of open and user-driven innovation for experimenting and validating Future Internet-enabled services.”
  • The authors examine several smart city projects to illustrate the central role of users in defining smart services and the importance of participation. They argue that, “Two different layers of collaboration can be distinguished. The first layer is collaboration within the innovation process. The second layer concerns collaboration at the territorial level, driven by urban and regional development policies aiming at strengthening the urban innovation systems through creating effective conditions for sustainable innovation.”

Suciu, G., A. Vulpe, S. Halunga, O. Fratu, G. Todoran, and V. Suciu. “Smart Cities Built on Resilient Cloud Computing and Secure Internet of Things.” In 2013 19th International Conference on Control Systems and Computer Science (CSCS), 513–518, 2013. http://bit.ly/16wfNgv.

  • This paper proposes “a new platform for using cloud computing capacities for provision and support of ubiquitous connectivity and real-time applications and services for smart cities’ needs.”
  • The authors present a “framework for data procured from highly distributed, heterogeneous, decentralized, real and virtual devices (sensors, actuators, smart devices) that can be automatically managed, analyzed and controlled by distributed cloud-based services.”

Townsend, Anthony. Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia. W. W. Norton & Company, 2013.

  • In this book, Townsend illustrates how “cities worldwide are deploying technology to address both the timeless challenges of government and the mounting problems posed by human settlements of previously unimaginable size and complexity.”
  • He also considers “the motivations, aspirations, and shortcomings” of the many stakeholders involved in the development of smart cities, and poses a new civics to guide these efforts.
  • He argues that smart cities are not made smart by various, soon-to-be-obsolete technologies built into its infrastructure, but how citizens use these ever-changing technologies to be “human-centered, inclusive and resilient.”

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We Need a Citizen Maker Movement

Curated on Posted on by Stefaan Verhulst

Lorelei Kelly at the Huffington Post: “It was hard to miss the giant mechanical giraffe grazing on the White House lawn last week. For the first time ever, the President organized a Maker Faire–inviting entrepreneurs and inventors from across the USA to celebrate American ingenuity in the service of economic progress.
The maker movement is a California original. Think R2D2 serving margaritas to a jester with an LED news scroll. The #nationofmakers Twitter feed has dozens of examples of collaborative production, of making, sharing and learning.
But since this was the White House, I still had to ask myself, what would the maker movement be if the economy was not the starting point? What if it was about civics? What if makers decided to create a modern, hands-on democracy?
What is democracy anyway but a never ending remix of new prototypes? Last week’s White House Maker Faire heralded a new economic bonanza. This revolution’s poster child is 3-D printing– decentralized fabrication that is customized to meet local needs. On the government front, new design rules for democracy are already happening in communities, where civics and technology have generated a front line of maker cities.
But the distance between California’s tech capacity and DC does seem 3000 miles wide. The NSA’s over collection/surveillance problem and Healthcare.gov’s doomed rollout are part of the same system-wide capacity deficit. How do we close the gap between California’s revolution and our institutions?

  • In California, disruption is a business plan. In DC, it’s a national security threat.
  • In California, hackers are artists. In DC, they are often viewed as criminals.
  • In California, “cyber” is a dystopian science fiction word. In DC, cyber security is in a dozen oversight plans for Congress.
  • in California, individuals are encouraged to “fail forward.” In DC, risk-aversion is bipartisan.

Scaling big problems with local solutions is a maker specialty. Government policymaking needs this kind of help.
Here’s the issue our nation is facing: The inability of the non-military side of our public institutions to process complex problems. Today, this competence and especially the capacity to solve technical challenges often exist only in the private sector. If something is urgent and can’t be monetized, it becomes a national security problem. Which increasingly means that critical decision making that should be in the civilian remit instead migrates to the military. Look at our foreign policy. Good government is a counter terrorism strategy in Afghanistan. Decades of civilian inaction on climate change means that now Miami is referred to as a battle space in policy conversations.
This rhetoric reflects an understandable but unacceptable disconnect for any democracy.
To make matters more confusing, much of the technology in civics (like list building petitions) is suited for elections, not for governing. It is often antagonistic. The result? policy making looks like campaigning. We need some civic tinkering to generate governing technology that comes with relationships. Specifically, this means technology that includes many voices, but has identifiable channels for expertise that can sort complexity and that is not compromised by financial self-interest.
Today, sorting and filtering information is a huge challenge for participation systems around the world. Information now ranks up there with money and people as a lever of power. On the people front, the loud and often destructive individuals are showing up effectively. On the money front, our public institutions are at risk of becoming purely pay to play (wonks call this “transactional”).
Makers, ask yourselves, how can we turn big data into a political constituency for using real evidence–one that can compete with all the negative noise and money in the system? For starters, technologists out West must stop treating government like it’s a bad signal that can be automated out of existence. We are at a moment where our society requires an engineering mindset to develop modern, tech-savvy rules for democracy. We need civic makers….”

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