| Big data (1193) |  |
| Big data analytics (29) | |
| Hadoop (24) | |
| Massive data (5) | |
| Big data (1193) | |
| Big data analytics (29) | |
| Hadoop (24) | |
| Massive data (5) | |
mobile, cloud and big data DIG 12. The topics of Digiworld 2013 were connected objects, video as a service, digital malls and digital money, smart city and digital living, future Internet and games.
Big data offers unified access to information. It allows the large-scale dissemination, analysis and use of data for the benefit of consumers and citizens.
Social networks, especially Facebook, are another contributor to big data. All these data are stored in data centers that must be powered and cooled.
Another ethic of publishing on Facebook may considerably decrease the need for big data. Google are said to use 50%less energy than the typical data center.
Bull offer includes big data, the cloud, green IT and digital simulation. The latter is very useful for creating a greener innovation it allows us to simulate the potential impact before transformation
The involvement of Bull in health care focuses on patient management and quick diagnosis. The latest technological advances, such as highperformance computing (HPC), big data, M2m, cloud, security and mobility
Smart city is an intensive user of ICT, intelligent technology, big data, connected objects and others.
Big data will be used to give organizations a competitive advantage in terms of marketing and customer relationships; lawyers will be displaced by e-discovery software that can rapidly determine which electronic documents are relevant to court cases;
the valuation of big data. These challenges can be seen as seven critical pillars to initiate in France the process of long-term prosperity and employment.
15 are concerned with exploring big data, 13 others address the personalized medicine and 10 are concerned with energy storing.
energy storage, recycling of rare metals, exploration of sea resources, vegetable proteins and plant chemistry, personalized medicine, silver economy and longevity and valorization of Big data.
This Big data generated over the past 30 years is the innovation treasure of Europe, but should be empowered by an intelligent engine allowing the efficient finding of the relevant information
two involving crossborder and cross-sector collaboration, two on user-centered design, one on big data,
The other cases described in the Open Innovation Yearbook 2014 are about innovation networks such as Oulu Innovation Alliance, Big data exploration, smart urban lighting and innovative services for lawyers.
Big data is collected from devices; we will be able to create our own apps and smart analytics to use them
big data versus world knowledge base. It is time to switch from quick business, having more and to show what we have to an awareness about the beauty
Existing big data technology can make information available on a real-time basis and at the same time enable prediction of future events,
Big data and Analytics Information-filled events are generated by a wide variety devices and systems: computers, mobile phones, vehicles, industrial equipment, sensors, security systems, building automation systems,
including complex event processing, pattern analysis and detection, big data processing, predictive analytics and automated decisioning.
The pilot generated in total big data containing 4, 500 driving hours and 250,000 km road vehicle traffic data (Ha ndel et al.
Out of the blue, Big data has become a topic in board-level discussions. The abundance of data will change many jobs across all industries.
, cyber physical systems, big data analysis, business intelligence approaches, or process mining provide more and more results in real-time.
including actuaries and statisticians for the purposes of developments in Big data and occupations in engineering, financial services,
New ones are related to green industry and sustainability, health industry, physics and big data analysis. As for the sectoral opportunities,
for Big data 53 3. 4 Winning Abroad 55 3. 5 Integrated Licensing Application Service 56 3. 6 Local Enterprise Offices 57 3
and our recognised success in Big data and data analytics; Grid integration of renewables, with associated‘smart grid'components.
Manufacturing Step Change, National Health Innovation Hub, Competitive Ecosystem for Big data, Winning Abroad, Integrated Licensing Application Service, Local Enterprise Offices, Trading Online,
DJEI, D/Health, EI, Joint Agency Project Team, Oversight Group) 2015 ACTION PLAN FOR JOBS 53 3. 3 Competitive Ecosystem for Big data
The overall ambition of the Disruptive Reform is to build on existing enterprise strengths to make Ireland a leading country in Europe in the area of Big data and Data Analytics.
On behalf of the Task force on Big data, DJEI commissioned a review of Ireland's progress towards achieving this goal
However in the face of strong European and international competition in this area the Task force has identified a number of new actions that will harness Big data for employment growth.
and develop a specific Big data agenda clarifying its leadership goals; 2. Building on our research strengths consolidate Ireland's leadership position in Big data/Data Analytics within Horizon 2020
and continue to promote engagement by enterprise in Ireland; 3. Continue to implement the recommendations of the EGFSN's report Assessing the demand for Big data and Analytics Skills;
4. Develop a coherent ecosystem to bridge the gap between R&d and innovation and take-up;
and focus of the Big data Task force will be renewed to provide effective overarching coordination and monitoring to ensure that the strategic goals are achieved.
The Big data market is in an emerging phase of development and in order to achieve the benefits of data-driven innovation,
and will progress a range of actions in 2015 (as set out below) in this regard. 54 2015 Actions Big data 86 Renew the mission
Task force on Big data, DJEI) 88 Monitor progress annually, based on the KPIS, and produce a report updating/revising the main actions.
Task force on Big data, DJEI) 89 Oversee the implementation of the actions arising from the IDC review
Task force on Big data, DJEI) 90 The Task force on Big data will review the opportunities for Ireland arising from the Internet of things
Task force on Big data, DJEI, IDA) 91 Establish interdepartmental committee on data protection issues and related structures.
In 2015 the Taskforce on Big data will assess the most appropriate policy response to this new and emerging opportunity
and job creation. 2015 Actions Internet of things 352 The Task force on Big data will review the opportunities for Ireland arising from the Internet of things
Task force on Big data, DJEI, IDA) 11.5 Innovative/Advanced Manufacturing By 2020 manufacturing will be different from
and Big data in manufacturing: better use if data is derived from process and product analytics. In addition, the existing Principle Investigators in NIBRT are developing a research plan to get started in ADC manufacturing in collaboration with other centres such as SSPC (Synthesis
Innovation Hub 2. 6-Intellectual Property in Enterprise 3. 3-Competitive Ecosystem for Big data 10-RD&I 11-New Sources of Growth 7. 2
crowdsourcing and crowdfunding, big data visualisation and analytics, P2p production and consumption, edemocracy and eparticiaption. Crowdsourcing refers to a platform for on-line distributed problems and a network of coordinated human‘problem solvers'.
such as the use of so-called‘big data'2 to collect and analyse data of what social needs are being experienced by which people in different places at different times.
and making policy recommendations based on the cumulative work of WP8. 1 http://digitalsocial. eu/2‘Big data'refers to the vast amount of data that can be collected from the internet,
How to guard against decisions being taken about peoples'lives based purely on big data, data analytics and closed algorithms?
www. otmenta. ru 31 www. lipsva. com 32 www. gdecasino. org/ru 39 How to balance data privacy, protection and misuse with openness, transparency and the benefits of big data?
A large amount of‘big data'is located geo, i e. it includes geo-coordinates which locate the data to specific points in space or specific geographic areas.
to huge‘big data'initiatives with an enormous variety of potential user groups and very significant impacts.
These two cases show the range from, respectively, small, niche and focused groups and interests, to huge‘big data'initiatives with an enormous variety of potential user groups and very significant impacts.
essential for knowledge co-creation & ongoing dialogue-Data and knowledge creation and manipulation vary hugely from small to big data, depending on case-Civil, voluntary finance & operation-Flat, informal, open, bottom-up,
How to address the big data challenges, knowledge generation and use How to ensure access to relevant data for the use of social enterprises
How to balance data privacy, protection and misuse with openness, transparency and the benefits of big data.
Big data and healthcare-Health communication and health information technology (IT) are central to health care, public health, and the way our society views health.
the apps only indirectly create network effects by providing big data of interest across all users.
'Other developments such as augmented reality (combining real world and digital information), Big data, and service robotics will expose consumers to a whole variety of new digital services in their daily lives.
big data, mobile and cloud solutions) to improve business operations, invent new business models, sharpen business intelligence, and engage with customers and stakeholders.
big data and cloud computing. Innovations and new business models are emerging in the fields of industry, agriculture, energy, health, traffic and education in particular.
For example, the catchphrase big data refers to the ever increasing volume of digital information that can be used by organisations to make predictions about people's everyday habits
The digital economy and digital workplace I I. THE DIGITAL ECONOMY AND DIGITAL WORKPLACE 13 Ever greater volumes of data (so-called big data) are being interconnected to build smart data,
zzthe establishment and expansion of research and technology programmes with high transferability to industry, for example, the areas of autonomic technology, 3d, big data, cloud computing and microelectronics;
zzthe initiation of new business models and innovative services by fostering the development and distribution of big data and cloud applications that offer greater security and data privacy;
IT SECURITY research, microelectronics and service research. zzwe are increasing innovation support for the area of big data to exploit its inherent potential for business (e g.
Two centres of excellence for big data are to be established in Berlin and Dresden. zzthe Federal government is boosting high-performance computing as a basis for scientific excellence and added value in business.
or types of data processing such as big data, profiling, web tracking or cloud computing to protect privacy. 32 VI.
crowdsourcing and crowdfunding, big data visualisation and analytics, P2p production and consumption, edemocracy and eparticiaption. Crowdsourcing refers to a platform for on-line distributed problems and a network of coordinated human‘problem solvers'.
Yet on the level of services, the emerging cloud model of some services (proprietary social networks, big data providers, implementations of the Internet of things
while the value of big data is associated often only with efficiency and profitability, big data can also be used for social good,
to improve public services and stimulate inclusive innovation. 1. 3 DIGITAL SOCIAL INNOVATION IN THE CONTEXT OF FUTURE INTERNET IN EUROPE The world wide web became successful
Big data can also be used for social good, to improve public services and stimulate inclusive innovation. 18 Growing a Digital Social Innovation Ecosystem for Europe European SMES,
social media, crowdsourcing, crowdfunding, big data, machine learning, 3d printing, online learning and e-petitions. The main technological trends in DSI 0100 200 300 400 Arduino Smart Citizen Kit Fairphone Safecast OPEN NETWORKS Tor Confine Guifi. net Smart
adapted from Sestini, F (Digital) Innovation Venture capital Big data and cloud computing COMPETITION, ECONOMIC ENTERESTS Innovation and innovation policy are not new to the European union.
A EU Big data strategy is becoming a priority for the competitiveness of European industries. In this framework the EC is promising to launch a multi-million euro Public Private Partnership on big data with industry.
The focus is driven business, with little attention to societal challenges or to the inclusion of civil society and bottom-up approaches.
Big data and cloud companies but also States have a lot of control over an individual's online identity.
and 3. New governance modalities for big data (main question around collective ownership of data, data portability and data as knowledge commons:
the question is how to ensure user control over personal information in an ocean of commercially valuable big data.
Defining sensible governance modalities for big data will require substantial collaboration between the public and private sectors, based on a multi-stakeholder model,
in order to define the minimum level of sensible regulation allowing fair competition in the emerging areas of big data.
yy Embracing creative disruption from technology (the pervasive use of social media, mobility, big data, cloud computing packaged in new digital government offerings;
by defining sensible governance modalities for big data thorugh a large collaboration between public and private actors;
the open source community, the developers'community, the innovation labs community, the open/big data community, the smart citizen/civic society community,
Furthermore, a EU Big data strategy is becoming a priority for the competitiveness of European industries,
In this framework the EC is promising to launch a launch a multi-million euro Public Private Partnership on big data with industry towards the end of this year.
In this report, the open/big data community refers to the set of governments, usually at the local level,
labs themselves Networks Networked Formal enabling/servicing structures Lack of interconnection between different types of labs Cost of being a network member Difficulty to involve the community Open/big data (Local governments Competition organizers
The open/big data community It has already been stated that the open/big data community includes a set of governments, usually at the local level,
The open/big data community's enablers connect (local governments with those who are potential users and who will boost innovation.
Open data evangelists are also enablers within the open/big data community. There are organisations that encourage the use of open data.
or Jay Nath (San francisco's Mayor Chief Innovation Officer) are only a few examples. of the open/big data community is top down, that is,
This does not mean the open/big data community does not have references. There are outstanding good practices
a lot has been written on open/big data failures. Huijboom & Van den Broek (2012) identified several barriers for open/big data initiatives to progress.
After reviewing open data strategies in several European countries, they describe a closed government culture, privacy legislation, limited quality of data, lack of standardisation (due to individual decisions), security threats,
and reputation Open/big data Organization of competitions Support for networking Knowledge sharing and dissemination New services Generation of economic value Transparency Political incentives (reputation) Technical support Monetary incentives
the open/big data community's instruments are very similar to the so-called enablers in section X. In particular,
Incentives for the open/big data community should take into account the instruments'flaws and the needs of the community in terms of motivations In this respect,
and pseudonymisation. 3. The main questions in a data-driven society emerge around new governance modalities for Big data, collective ownership of data, data portability,
The question is how to assure user control over personal information in an ocean of commercially valuable Big data.
Defining sensible governance modalities for big data will requires a large collaboration between public and private actors. 56 4. Identity Management is becoming a very important issue in the digital economy
to connect industrialized big data with collective awareness, while taking into account privacy concerns. The objective would be to harness technology for making the fabric of society as a whole wiser, a genuine product of a more inclusive collective intelligence.
The emerging cloud model,(proprietary social networks, big data providers, the Internet of things implementation), are currently following a different model that allows us convenience but at the expense of security, privacy and openness:
and digital services adopted by DSI activities such as social networking, social media, crowdsourcing, crowdfunding, big data, machine learning, 3d printing, online learning,
approaches (e g. supply-side approach to Big data & Big brother). Unlike traditional innovation actions, DSI and Collective Awareness Platforms are motivated by the vision of building an open and grassroots civic innovation Ecosystem in Europe to unleash the potential of collective intelligence.
which has been restricted in recent years by the sheer abundance of‘big data.''Volunteers are presented with a series of image or‘slides'.
(i e. opening up data analysis to the public) to process big data sets quicker, while simultaneously advancing scientific research.
to connect industrialized big data with collective awareness, while taking into account privacy concerns. The objective would be to harness technology for making the fabric of society as a whole wiser, a genuine product of a more inclusive collective intelligence.
The emerging cloud model,(proprietary social networks, big data providers, the Internet of things implementation), are currently following a different model that allows us convenience but at the expense of security, privacy and openness:
and digital services adopted by DSI activities such as social networking, social media, crowdsourcing, crowdfunding, big data, machine learning, 3d printing, online learning,
approaches (e g. supply-side approach to Big data & Big brother). Unlike traditional innovation actions, DSI and Collective Awareness Platforms are motivated by the vision of building an open and grassroots civic innovation Ecosystem in Europe to unleash the potential of collective intelligence.
which has been restricted in recent years by the sheer abundance of‘big data.''Volunteers are presented with a series of image or‘slides'.
(i e. opening up data analysis to the public) to process big data sets quicker, while simultaneously advancing scientific research.
Lerman, J.,Big data and Its Exclusions, Stanford Law Review Online, 66,55, 2013. Levin,"The rise of Asia's universities,"2010. http://www. hepi. ac. uk/483-1780/Seventh-HEPI-Annual-Lecture. html Lewis, Maureen,
and A. Hung Byers, Big data: The next frontier for innovation, competition, and productivity, Mckinsey Global Institute, 2011.
and Jules Polonetsky,"Privacy in the Age of Big data: A Time for Big Decisions,"Stanford Law Review Online, Vol. 64,2012, p. 63.
The examples of what other countries (such as Singapore) are doing in new areas such as Big data
social and big data are already central to business thinking, and the next set of digital technologies, trends, opportunities and threats is creating yet another competitive frontier.
Gfk turns big data into smart data, enabling its clients to improve their competitive edge and enrich consumers'experiences and choices.
Big data to monitor risks and identify opportunities Another big trend that is further maturing in 2014 is the application of big data analytics and visualization to the domain of online payments.
E-commerce leaders such as Amazon have been applying big data for years now with the objective of building sophisticated profiles of their consumers for Conversion Rate Optimization (CRO.
the cloud and big data are transforming the way companies and their customers interact. At the same time these technologies are releasing a wave of IT-led innovation,
respectively The americas CIO and global CMO at SAP, believe their functions have changed so fundamentally with the advent of digital technologies such as big data, mobile,
but the real spending is being redirected into platforms, around analytics, big data, mobility and the cloud or whichever area has the highest benefit for your company,
reality and an early government big data initiative to build a digital 117 surveillance system today called PRISM.
Educators could incorporate these principles and techniques into their curricula through the fusion of augmented reality, big data and social media.
Yet on the level of services, the emerging cloud model of some services (proprietary social networks, big data providers, implementations of the Internet of things
while the value of big data is associated often only with efficiency and profitability, big data can also be used for social good,
to improve public services and stimulate inclusive innovation. 1. 3 DIGITAL SOCIAL INNOVATION IN THE CONTEXT OF FUTURE INTERNET IN EUROPE The world wide web became successful
Big data can also be used for social good, to improve public services and stimulate inclusive innovation. 18 Growing a Digital Social Innovation Ecosystem for Europe European SMES,
social media, crowdsourcing, crowdfunding, big data, machine learning, 3d printing, online learning and e-petitions. The main technological trends in DSI 0100 200 300 400 Arduino Smart Citizen Kit Fairphone Safecast OPEN NETWORKS Tor Confine Guifi. net Smart
adapted from Sestini, F (Digital) Innovation Venture capital Big data and cloud computing COMPETITION, ECONOMIC ENTERESTS Innovation and innovation policy are not new to the European union.
A EU Big data strategy is becoming a priority for the competitiveness of European industries. In this framework the EC is promising to launch a multi-million euro Public Private Partnership on big data with industry.
The focus is driven business, with little attention to societal challenges or to the inclusion of civil society and bottom-up approaches.
Big data and cloud companies but also States have a lot of control over an individual's online identity.
and allow for harnessing big data to improve healthcare. Clinical decision systems assist healthcare providers with decision making task.
including cloud computing, Internet of things, data analytics and big data, IT-powered robotics, intelligent agents, mobile commerce, improved self-serve kiosks, 3d printing, location awareness, and machine learning.
%Last but not least, policy should support the application of various research methods (e g. teacherled research, control groups, experimental research, longitudinal studies, social networks analysis, learning analytics, big data research, etc.
Supporting the application of various research methods (e g. teacher-led research, control groups, experimental research, longitudinal studies, social networks analysis, learning analytics, big data research, etc.
Supporting the application of various research methods (e g. teacher-led research, control groups, experimental research, longitudinal studies, social networks analysis, learning analytics, big data research, etc.
, experimental research, longitudinal studies, social networks analysis, learning analytics, big data research, etc. to the study of complex'ecosystems'of ICTELI..
ITU is joining the international statistical community in looking into ways of using new and emerging data sources such as those associated with big data to better provide timely and relevant evidence for policy-making.
as well as being a major source of big data in their own right. Big data from mobile operators, for example, are real-time and low-cost
and have one of the greatest development potentials in view of the widespread use and availability of mobile networks and services.
This report provides the reader with a comprehensive and critical overview of the role of big data from the telecommunication sector,
152 Chapter 5. The role of big data for ICT monitoring and for development...173 5. 1 Introduction...
173 5. 2 Big data sources, trends and analytics...175 5. 3 Telecommunication data and their potential for big data analytics...
181 5. 4 Big data from mobile telecommunications for development and for better monitoring...185 5. 5 Challenges and the way forward...
156 5. 1 The five Vs of big data...176 5. 2 An overview of telecom network data...
182 5. 3 Customer profiling using telecom big data...184 xii List of boxes 1. 1 Final review of the WSIS targets:
158 5. 1 How big data saves energy Vestas Wind Systems improves turbine performance...177 5. 2 How Twitter helps understand key post-2015 development concerns...
191 5. 9 Using mobile big data and mobile networks for implementing surveys...193 List of tables 1. 1 Rural population covered by a mobile-cellular signal, 2012.4 1. 2 Total Internet domain registrations by world region, 2003,2008
163 4. 15 ICT Price Basket and sub-baskets, 2013.166 5. 1 Sources of big data...
and the role of big data for ICT monitoring. 1. 2 The voice market In line with developments in recent years,
New data sources could include big data (mostly provided by private-sector companies) which could help improve the timeliness and completeness of data,
The topic of big data is gaining momentum in the statistical community. Chief statisticians gathering at the UNSC meetings in 2013 recognized that big data constitute a source of information that cannot be ignored by official statisticians
and that official statisticians must organize and take urgent action to exploit the possibilities and harness the challenges effectively (UNSC, 2014). 30 In view of declining responses to national household and business surveys in a number of countries,
big data could provide important sources of more timely and relevant information, thus complementing official statistics on the economy, society and environment.
thus potentially becoming big data sources as well. At the UNSC meeting in 2014, the commission reiterated its call for the global statistical community to take action,
and supported the proposal to create a global working group on the use of big data for official statistics.
31 To make an inventory of ongoing activities and concrete examples regarding the use of big data for official statistics at regional,
access to data and legislation related to big data To address the issue of obtaining access at no cost to big data from the private sector for official statistical purposes,
as well as the issue of access to transborder data or access to data on transboundary phenomena To develop guidelines to classify the various types of big data sources
and approaches To develop methodological guidelines related to big data, including guidelines for all the legal aspects To formulate an adequate communication strategy for data providers
and users on the issue of use of big data for official statistics To reach out to other communities, especially those more experienced in IT issues or in the use of open data platforms.
The UN Global Working group on Big data for Official Statistics was launched formally in June 2014, under the auspices of the UN Statistics Division.
provide strategic vision, direction and coordination of a global programme on big data for official statistics;
promote practical use of sources of big data for official statistics; provide solutions for methodological, legal and privacy issues;
foster communication and advocacy of the use of big data for policy applications; and build public trust in the use of private-sector big data for official statistics.
ICTS are part of the debate on the data revolution, big data and, more broadly, emerging data issues in the post-2015 development debate.
First, the ICT sector in itself represents a new source of data, provided by, for example, Internet and telecommunication companies.
In view of the link between big data and ICTS, work is under way in ITU with a view to contributing to the debate
and identifying new ways and means of exploiting the potential of big data. The focus is primarily on the telecommunication/ICT sector as a source of big data,
including players such as operators and service providers, in the fixed, mobile and Internet sectors. Delegates attending the eleventh World Telecommunication/ICT Indicators Symposium (WTIS) in Mexico city in December 2014 recommended that ITU should further examine the challenges and opportunities of big data,
in particular data coming from ICT companies; that regulatory authorities should explore the development of guidelines on how big data could be produced,
exploited and stored; and that national statistical offices, in cooperation with other relevant agencies, should look into the opportunities for big data and address current challenges in terms of big data quality,
Chapter 1. Recent information society developments 30 veracity and privacy within the framework of the fundamental principles of official statistics. 33 The big data approach taken by ITU so far focuses on the following areas and questions:
Standardization: 34 Which standards are required to facilitate interoperability and allow technology integration in the big data value chain?
Which definitions, taxonomies, secure architectures and technology roadmaps need to be developed for big data analytics and technology infrastructures?
What is the relationship between cloud computing and big data in view of security frameworks? Which techniques are needed for data anonymization for aggregated datasets such as mobile-phone records?
How is exploited big data in different industries; what are the specific challenges faced; and how can these challenges be addressed through international standards?
and should big data be regulated? How does big data impact on the regulation of privacy, copyright and intellectual property rights (IPR), transparency and digital security issues?
Box 1. 4: What is a data revolution? The report of the High-level Panel of Eminent Persons on the Post-2015 Development Agenda to the UN Secretary-general,
and satisfy the demand for data needs in new areas, such as big data, geospatial information and geographical information systems Open data policies should be envisaged to ensure accountability
What is the link between big data and open data (crowdsourcing, cloud computing, etc.)?is there need a to regulate data management and service providers?
How can market dominance in the area of big data be prevented and the rights of the data owners protected?
How can big data complement existing ICT statistics to better monitor information-society developments? Which type of data from ICT companies are most useful and for which purposes?
Which new ICT indicators could be produced from big data sources? What are the key issues that need to be addressed,
and by whom, in terms of collecting and disseminating big data in telecommunications? What is the role of national statistical offices
and how can big data complement official ICT data? How can big data from telecommunications inform not only ICT but broader development policy in real time,
leading to prompt and more effective action? Chapter 5 of this report addresses some of these questions
http://www. itu. int/en/ITU-D/Statistics/Pages/events/wtis2013/default. aspx. 34 For further information on the work on big data carried out by the ITU Telecommunication
see http://www. itu. int/en/ITU-T/techwatch/Pages/big data-standards. aspx. 35 A background document on big data that was prepared for GSR-14 is available at http
whereas dispersion in mobile-cellular prices is of 60 per cent around the mean. 173 Measuring the Information Society Report 2014 Chapter 5. The role of big data for ICT monitoring
the emergence of big data holds great promise, and there is an opportunity to explore their use in order to complement the existing,
There is no unique definition of the relatively new phenomenon known as big data. At the most basic level it is understood as being data sets whose volume,
The emergence of big data is linked closely to advances in ICTS. In today's hyper-connected digital world, people and things leave digital footprints in many different forms
Big data have great potential to help produce new and insightful information, and there is a growing debate on how businesses,
governments and citizens can maximize the benefits of big data. Although it was the private sector that first used big data to enhance efficiency and increase revenues
the practice has expanded to the global statistical community. The United nations Statistical commission (UNSC) and national statistical organizations (NSOS) are looking into ways of using big data sources to complement official statistics
and better meet their objectives for providing timely and accurate evidence for policy-making. 1 So far,
there is limited evidence as to the value added by big data in the context of monitoring of the information society,
and Chapter 5. The role of big data for ICT monitoring and for development 174 there is a need to explore its potential as a new data source.
and that big data have the potential to help realize those efforts. In addition to the data produced
and potential of big data when it comes to providing new insights for broader social and economic development.
Big data are already being leveraged to understand socioeconomic well-being forecast unemployment and analyse societal ties. Big data from the ICT industry play a particularly important role
because they are the only stream of big data with global socioeconomic coverage. In particular, mobile telephone access is quasiubiquitous,
and ITU estimates that by the end of 2014 the number of global mobile subscriptions will be approaching 7 billion.
and where big data hold great promise for development. However, while there are a growing number of research collaborations and promising proofof-concept studies,
To this end, this chapter will contribute to the debate on big data for development highlight advances, point to some best practices
including in regard to the production and sharing of big data for development. The chapter will first (in Section 5. 2) describe some of the current big data trends and definitions,
highlight the technological developments that have facilitated the emergence of big data, and identify the main sources
and uses of big data, including the use of big data for development and ICT monitoring. Section 5. 3 will examine the range and type of data that telecommunication companies,
in particular mobile-cellular operators, produce, and how those data are 175 Measuring the Information Society Report 2014 currently being used to track ICT developments
which telecom big data may be used to complement official ICT statistics and assist in the provision of new evidence for a host of policy domains,
while Section 5. 5 discusses the challenges of leveraging big data for ICT monitoring and broader development, including in terms of standardization and privacy.
and fully exploiting telecom big data for monitoring and for social and economic development, in particular with regard to the different stakeholders involved in the area of big data from the ICT industry. 5. 2 Big data sources,
trends and analytics With the origins of the term big data being shared between academic circles, industry and the media,
the term itself is amorphous, with no single definition (Ward and Barker, 2013). At the most basic level of understanding, it usually refers to large and complex datasets, Table 5. 1:
Sources of big data Sources Some examples Administrative data Electronic medical records Insurance records Tax records Commercial transactions Bank transactions (inter-bank as well as personal) Credit card
Indeed, one of the key trends fostering the emergence of big data is the massive datafication
big data are generated in digital form from a number of sources. They include administrative records (for example, bank or electronic medical records), commercial transactions between two entities (such as online purchases or credit card transactions), sensors and tracking devices (for example
and social media content)( Table 5. 1). Big data is not just about the volume of the data.
describes big data characteristics such as velocity and variety, in addition to volume (Laney, 2001). Velocity refers to the speed at
while the Chapter 5. The role of big data for ICT monitoring and for development 176 term variety encompasses the fact that data can exist as different media (text,
A fifth V value is included by some to acknowledge the potentially high socioeconomic value that may be generated by big data (Jones,
2012)( Figure 5. 1). Included within the scope of big data is the category of transaction-generated data (TGD),
The value of this subset of big data is that it is connected directly to human behaviour
The five Vs of big data Source: ITU. at the forefront of extracting value from this data deluge.
the public sector is turning towards big data to improve its service delivery and increase operational efficiency.
In addition, there are uses for big data in broader development and monitoring, and there is an increasing focus on big data's role in producing timely (even real-time) information,
as well as new insights that can be used to drive social and economic well-being. Big data uses by the private and public sectors Marketing professionals,
whose constant aim is to understand their customers, are now increasingly shifting from conventional methods, such as surveys, to the extraction of customer preferences from the analysis of big data.
Walmart, the world's biggest retailer, has been one of the largest and earliest users of big data.
In 2004, it discovered that the snack food known as Pop Tarts was purchased heavily by United states citizens preparing for serious weather events such as hurricanes.
including large amounts of unstructured data Level of quality, accuracy and uncertainty of data and data sources VALUE Potential of big data for socioeconomic development VELOCITY Speed at
large-scale automated correlation analysis and predictive analytics are two of the key techniques that have helped unleash the value of big data.
Nor is the private sector's use of big data techniques restricted solely to market research. Companies and whole industries (healthcare, energy and utilities, transport, etc.
How big data saves energy Vestas Wind Systems improves turbine performance Vestas, a global energy company dedicated to wind energy,
with installations in over 70 countries, has used big data platforms to improve the modelling of wind energy production
By using big data techniques based on a large set of factors and an extended set of structured and unstructured data
Big data have enabled the creation of a new information environment and allowed the company to manage
Telecom operators also use big data techniques to understand and control churn, optimize their management of customer relations
Encouraged by the potential of big data to produce new insights and slimmer budgets governments (at all levels) are now looking to exploit big data
and increase the application of data analytics to a range of activities, including monitoring and improvement of tax compliance and revenues, crime detection and prediction,
and generate themselves, Chapter 5. The role of big data for ICT monitoring and for development 178 complement their official statistics by leveraging data from new sources, including crowd-sourced data generated by the public.
Big data for development and ICT monitoring One of the richest sources of big data is captured the data by the use of ICTS.
Big data from the ICT services industry are already helping to produce large-scale development insights of relevance to public policy.
It should be noted that in some countries and regions the use of big data including big data from the ICT industry,
is subject to national regulation. In the EU, for example, a number of directives require data producers to obtain users'consent before gathering any of their personal data. 5 One of the best-known examples of leveraging the online population's digital breadcrumbs for development purposes is Google Flu Trends (GFT.
GFT was held up as an outstanding example of big data in action and of the great potential of big data for broader development and monitoring (Mayer-Schönberger and Cukier, 2013;
The Internet has also been a rich source of big data beyond the realm of user search terms.
a UN initiative to use big data for sustainable development and humanitarian action, has been mining Twitter data from Indonesia (where Twitter usage is high) 9 to understand food price crises.
UN Global Pulse and the Millennium Campaign are using big data and visual analytics to identify the most pressing development topics that people around the world are concerned about
the ICT sector is itself using the Internet as a source of big data for monitoring purposes.
and oceans Phone and Internet access Equality between men and women Chapter 5. The role of big data for ICT monitoring and for development 180 Mobile data Despite the rapid growth in Internet access,
non-Internet-related mobilenetwork big data seems to have the widest socioeconomic coverage in the near term,
Mobile network big data have been utilized to great effect in the area of transportation, helping to measure and model people's movements (even in real time) and understand traffic flows (Wu et al.,
) It is evident from the examples given that big data from the ICT sector, and especially those available to telecommunication operators, have wide applicability for informing multiple public policy domains.
Less use has thus far been made of telecommunication big data with a view to understanding its potential for producing additional information and statistics on the information society.
are an important source of data and for the purpose of this chapter, all forms of telecommunication big data (either volume,
Not surprisingly, the big data for development initiatives (outlined in Section 2. 2) have drawn mainly on mobile network big data rather than on those from fixed-telephone operators or ISPS.
which in turn can provide some insight as to that Chapter 5. The role of big data for ICT monitoring and for development 182figure 5. 2:
The telecom industry's use of big data Telecommunication companies are actively seeking to intensify their use of big data analytics
For operators, big data open up opportunities for better understanding of their customers, which in turn leads to improved sales and marketing opportunities.
big data can help optimize network operations and create new revenue streams and business lines, for example when selling data.
Chapter 5. The role of big data for ICT monitoring and for development 184 Customer profiles include details about customers'mobility patterns, social networks and consumption preferences.
Big data, on the other hand, can help to enhance that classification by enabling analysis of the levels of consumption of different services,
Big data techniques can help operators understand churn better by enabling them to model the likelihood of customers leaving the network
Customer profiling using telecom big data Source: ITU. CUSTOMER INTERESTS SOCIOECONOMIC CLASS LEVEL OF INFLUENCE OF CUSTOMERS LIKELIHOOD OF CHURN MOBILITY PROFILE 185 Measuring the Information Society Report 2014 in competitor networks.
In the Republic of korea, for example, SK Planet, a subsidiary of SK TELECOM, uses big data to help its parent company to cut churn
One example is based the US big data startup Cignifi, 19 which obtains data from mobile operators
which the detailed mobility profiles available to operators are leveraged. 5. 4 Big data from mobile telecommunications for development and for better monitoring In 2013, the United nations High-level Panel of Eminent Persons on the Post-2015
In March 2014, the forty-fifth session of UNSC, the highest decision-making body for international statistical activities, presented a report on big data and modernization of Chapter 5. The role of big data for ICT
and proposed the creation of a big data working group at the global level (UNSC, 2013). 20 Current uses of big data to complement official statistics are still exploratory,
but there is a growing interest in this topic, as evidenced by the numerous initiatives being pursued by the United nations, as well as by others,
There are many big data sources that can be used to monitor and assess development results. In a world where mobile telephony is increasingly ubiquitous,
it is not surprising that mobile telecommunication big data have unique potential as a new data source,
This section will present some of the existing (and growing) evidence for the role of mobile big data in achieving development goals in various policy areas,
In addition to their use for development, telecommunication big data have potential as a source to enable monitoring of the information society,
however, ITU is exploring the potential of big data to complement its existing, and often limited, set of ICT statistics.
This section presents a first attempt to help identify some of the areas in which mobile telecommunication big data could complement existing ICT indicators to provide a more complete
Mobile phone big data for development Mobile data offer a view of an individual's behaviour in a low-cost, high-resolution, realtime manner.
Big data for disaster management and syndromic surveillance21 Mobility data collected immediately after a disaster can in many cases help emergency responders to locate affected populations
Chapter 5. The role of big data for ICT monitoring and for development 188 Big data for better transportation planning A data-centric approach to transportation management is already a reality in many developed economies.
This map was produced on the basis of mobile network data to show the potential of big data in tracking population movements.
Leveraging mobile network data for transportation and urban planning in Sri lanka Very similar findings between the results of an official household survey assessing mobility patterns (right-hand map) with the results of a big data analysis using mobile-phone
data (left-hand map) underscore the merits of big data. The image on the left, based on mobile-phone data, depicts the relative population density in Colombo city
Mobile big data (left) versus official survey data (right) Both passive and active positioning data are used to analyse traffic conditions, particularly in urban areas with higher base-station density.
Chapter 5. The role of big data for ICT monitoring and for development 190 Big data for socioeconomic analysis Data from mobile operators can provide insights in the areas of economic development and socioeconomic status
Big data techniques can therefore complement official statistics in the intervals between official surveys, which are usually relatively expensive
In many cases, insights derived from big data sources may help to fill in the gaps, rather than replace official surveys.
It should also be noted that mobile network big data are one of the few big data sources (and often the only one) in developing economies that contain behavioural information on low-income population groups Frias-Martinez et al.
A compelling example of how mobile big data can be used for the unbanked is Cignifi, a big data startup that uses the mobile phone records of poor people to assess their creditworthiness
when they apply for a loan (Box 5. 8). Big data for understanding societal structures Social-network studies relying on self-reporting relational data typically involve both a limited number of people
and limited number of time points (usually one). As a result, social-network analysis has generally been confined to the examination of small population groups through a small number of snapshots of interaction patterns.
a big data startup, has developed an analytic platform to provide credit and marketing scores for consumers, based on their mobile-phone data.
Chapter 5. The role of big data for ICT monitoring and for development 192 have been used to study the geographic dispersion
Big data to monitor the information society There is a case to be made for analysing data captured by telecommunication operators in the interests of improving the current range of indicators used for monitoring the information society.
Against this background, mobile networks and mobile big data could be used to identify alternative, less costly and faster ways of carrying out representative surveys (Box 5. 9). Given the shortcomings of existing administrative data from operators and survey data collected by NSOS,
it is particularly interesting to assess some of the ways in which big data can be used to overcome the shortcomings of existing key ICT indicators
Big data could help in obtaining more granular information in several areas, and big data techniques could be applied to existing data to produce new insights.
In particular, operators'big data could produce information in the following areas: Individual subscriber characteristics: Additional categorization across both time and space are possible for subscription indicators,
and big data could provide additional information on gender, socioeconomic status and user location. Information on gender or age, for example, could be derived from customer registration information (notwithstanding a number of challenges and privacy issues,
as discussed later in Section 5. 5). The socioeconomic status of the person linked to a subscription could be derived from big data techniques applied to users'consumption information,
as well as other data contained in customer registration information. In addition, the analysis of customers'mobility patterns will often allow for an understanding of important locations (work
Using mobile big data and mobile networks for implementing surveys An important measurement for assessing the development of the information society is the extent to
Chapter 5. The role of big data for ICT monitoring and for development 194 create new information.
By applying big data techniques to survey data and administrative data from operators, new insights could be derived, in particular, in respect of the following:
Big data techniques could help extrapolate the actual number of unique mobile subscribers or users, rather than just subscriptions,
big data methods only complement existing surveys rather than replacing them completely (see Section 5. 5 for a further discussion of this).
In sum, relatively simple big data techniques can help analyse and provide complementary information on existing ICT data,
Such techniques will often include combining data from surveys with big data to build new correlation and predictive analytic techniques.
and for other big data for development projects, big data analysis cannot replace survey data, which is needed to build
and test correlations and to validate big data results. While the opportunities discussed above present what is analytically possible,
and it is important for big data producers and users to collaborate closely in that regard. This includes raising awareness about the importance
and the establishment of public-private partnerships to exploit fully the potential of big data for development.
Traditional curation approaches do not scale with big data and require automation, especially since 85 per cent of big data are estimated to be unstructured (Techamerica Foundation, 2012.
Dealing with large heterogeneous data sets calls for algorithms that can understand the data shape
To be able to use telecom big data for development and monitoring and to guarantee its continuity,
Chapter 5. The role of big data for ICT monitoring and for development 196 Accessing and storing data,
and data philanthropy Big data for development is still in its nascent stages and, as such, comes with its share of challenges, not least
Until holders of big data become more comfortable about their release it is going to be difficult for third-party research entities to gain access.
Researchers (mainly from developed countries, with some exceptions such as LIRNEASIA) have succeeded recently in obtaining mobile network big data,
and standard-bearers when it comes to telecom network big data. Some have argued that NSOS are placed well to ensure that best practices are followed in the collection and representation of big data,
and to provide a stamp of trust for potential third-party data seekers. Telecom operators, for their part
it should be noted that the emergence of big data is linked closely to advances in the ICT sphere,
and consent practised by most companies to inform users of what data are Chapter 5. The role of big data for ICT monitoring
however, that in a big data world the inform and consent approach is woefully inadequate and impractical,
Secondly, in the big data paradigm, the greatest potential often lies in secondary uses, which may well manifest long after the data was collected originally.
and hinder efforts to exploit big data for the greater social good. Encryption, virtual private networks (VPNS), firewalls, threat monitoring
but it will be some time before a consensus is achieved on the most appropriate method (s). In response to the growing trend to unlock socioeconomic value from the rising tide of big data,
Given the complexity of the questions related to privacy and data protection in a big data world, the danger is that these questions may take too long to resolve
and further delay the potential use of big data for broader development. Hence, a balanced risk-based approach may be required in the context of
i e. the use of telecom big data for monitoring and development. This does still require the confluence of appropriate stakeholders.
research into the use of big data for development can be sandboxed, with appropriate privacy protections imposed on researchers,
In the big data paradigm, it is easy to overlook that concept, given the expectation that when dealing with vast volumes of (often unstructured) data from a multitude of sources,
and the question is important in establishing the generalizability of the big data findings. Data provenance and data cleaning Understanding data provenance involves tracing the pathways taken by data from the originating source through all the processes that may have mutated,
replicated or combined the data that feed into the big data analyses. This is no simple feat.
Related to the question of data provenance is the issue of understanding the underlying Chapter 5. The role of big data for ICT monitoring
Hence, the big in big data does not automatically mean that issues such as measurement bias and methodology,
These are fundamental issues not just for small data but also for big data (Boyd and Crawford, 2012.
Telecom network big data, which mostly fall under this category, may be less susceptible to self-censorship and persona development,
In a way, big data analyses of behavioural data are subject to a form of the Heisenberg uncertainty principle, whereby as soon as the basic process of an analysis is known,
and understanding the real-world context therefore remains important when considering big data analyses for monitoring purposes.
when it comes to the generalizability of telecomdata analyses based on big data. For example, prior research had established a power-law distribution between the frequency of airtime recharges
Causation versus correlation It is easy to confuse correlation with causation in the big data paradigm
Big data draws many of its techniques from machine learning, which is primarily about correlation and predictions. 40 Big data are by their very nature observational
and can measure only correlation and not causality. Supporters of big data have predicted the end of theory and hypothesis-testing, with correlation trumping causality as the most relevant method (Anderson, 2008;
Mayer-Schönberger and Cukier, 2013. However, such predictions may be premature. The behavioural economist Sendhil Mullainathan notes that inductive science
(i e. the algorithmic mining of big data sources) will not drown out traditional deductive science (i e. hypothesis testing), even in a big data paradigm.
Among the three Vs in the traditional big data definition, volume and variety produce countervailing forces.
More volume makes big data induction techniques easier and more effective, while more variety makes them harder and less effective.
(i e. deductive science) Chapter 5. The role of big data for ICT monitoring and for development 202 rather than merely predicting it (Mullainathan,
) Causal modelling is possible in a big data paradigm by conducting experiments. Telecom network operators themselves use such techniques when rolling out new services or, for that matter, for pricing purposes.
2012a), big data are most useful as a basis for encouraging timely investigation, rather than as a replacement for existing measures of disease activity.
thus continue to be important to building the big data models and for periodic benchmarking so that the models can be tuned fine to reflect ground realities.
Transparency and replicability The issues with GFT also illustrate transparency and replicability problems with big data research.
and extracting value from big data calls for a combination of specialized skills in the areas of data mining, statistics and domain expertise,
when it comes to working with large volumes of big data calling for computer science and decision-analysis skills that are emphasized not in traditional statistical courses (Mcafee and Brynjolfsson,
and identified intensive training and capacity development of their staff as a prerequisite to being able to exploit new big data sources (UNSC, 2013).
) This suggests that organizations wishing to leverage big data for development will face competition from the private sector
which stand to benefit the most from the use of telecommunication big data to complement official statistics,
The way forward Current research suggests that new big data sources have great potential to complement official statistics and produce insightful information to foster development.
Future efforts to mainstream and derive full benefit from the use of big data will have to overcome a number of barriers.
Very limited information is available on opportunities for using big data to complement official ICT statistics.
Although this report highlights some of the big data sources and techniques that could be used, further research is needed to understand
and confirm the usefulness of big data sources for monitoring the information society. As with other official statistics, it is paramount for big data producers
and big data users to collaborate and to initiate a dialogue to identify opportunities and understand needs and constraints.
Since many of the big data sources lie within the private sector close cooperation between NSOS, on the one hand,
and telecommunication operators and Internet companies, including search engines and social networks, on the other, is necessary
and researchers to understand how to leverage big data for different purposes. Such engagement will also broaden their understanding of the limitations and assist them in the development of new methodologies
They are placed well to develop a Chapter 5. The role of big data for ICT monitoring and for development 204 privacy framework, in consultation with other stakeholders.
Combining big data sources has great potential to increase added value and produce new insights. There is scope for exploring established models for such pooling for example, the sharing by banks of some of their customer data with credit bureaux.
Governments Governments have different opportunities and different roles to play in the exploitation of big data for monitoring and development.
They can use big data to identify areas where rapid intervention may be necessary to track progress
More and more governments are recognizing the importance of big data and have set up communities of practice and working groups to study their use
and take a lead in setting big data standards. To this end, national regulatory authorities (NRAS) and NSOS, in consultation with other national stakeholders, are placed best to lead the corresponding discussions
and big data clearing houses that promote analytical best practices in relation to the use of big data for complementing official statistics and for development.
in order to handle big data, while at the same time investing in the necessary computational infrastructure. As the main regulatory interface to the telecom sector, NRAS are placed well to co-champion the national discussion on how telecommunication big data may be leveraged for social good.
Regulators have a role to play in facilitating the introduction of legislation that addresses privacy concerns while encouraging data sharing in a secure manner.
While the use of big data can help better decision-making through probabilistic predictions, this information should not be used against citizens.
which government agencies and others can utilize big data predictions. Fostering big data competition and openness: Regulators could foster big data competition in increasingly concentrated big data markets,
including by ensuring that data holders allow others to access their data under fair and reasonable terms. 205 Measuring the Information Society Report 2014 International stakeholders International stakeholders including UN AGENCIES and initiatives (such as
ITU and UN Global Pulse), the Partnership on Measuring ICT for Development, ICT industry associations and producers of big data (Google, Facebook, etc.)
have an important role globally. More work is needed to understand fully the potential of big data
and examine the challenges and opportunities related to big data in the ICT sector. To this end, the key international stakeholders have to work together to facilitate the global discussion on the use of big data.
UN Global Pulse, as one of the main UN initiatives exploring the use of big data,
can do much to inform and motivate the discussion on global best practices and the use of big data for development.
Where using big data for monitoring the information society is concerned new partnerships, including public-private partnerships between data providers
and the ICT statistical community, including ITU, could be formed to explore new opportunities and address challenges, including in the area of international data comparability and standards.
As one of the main international bodies working on issues related to the telecommunication sector, ITU could leverage its position to facilitate global discussion on the use of telecom big data for monitoring the information society.
Together, ITU and UN Global Pulse could facilitate the work that needs to be done by NRAS and NSOS, through awareness raising and engagement on privacy frameworks
ITU could help reduce the transaction costs associated with obtaining telecommunication big data, for example by facilitating the standards-setting process.
and leveraging telecommunication big data for social good. Academia, research institutes and development practitioners The research into how telecom data may be used to aid broader development is being done mainly by academia, public and private research institutes and, to a lesser degree,
This makes them important stakeholders in defining the state of the art with respect to leveraging big data for development. They, more than others, have been the first to engage with telecommunication operators with a view to using their data for development.
Their collective experiences will be valuable as big data for development becomes mainstreamed. 207 Measuring the Information Society Report 2014 Chapter 5 Annex The mobile-telecommunication data that operators possess can be classified into different types,
as outlined in Section 5. 3. Chapter 5. The role of big data for ICT monitoring
Chapter 5. The role of big data for ICT monitoring and for development 210 1 The report of the UN Secretary-general's High-level Panel of Eminent Persons on the post-2015 Development Agenda
including big data. In addition, the European Statistical System Committee (ESSC) in 2013 adopted the Scheveningen Memorandum on Big data and Official Statistics,
which acknowledges that Big data represents new opportunities and challenges for Official Statistics, and which encourages the European Statistical System
and its partners to effectively examine the potential of Big data sources, see: http://epp. eurostat. ec. europa. eu/portal/page/portal/pgp ess/0 docs/estat/SCHEVENINGEN MEMORANDUM%20final%20version. pdf. 2 This term was discussed first
in 1991, although the term then used was generated transaction information (Mcmanus, 1990) 3 For more information, see http://www. cityofboston. gov/doit/apps/streetbump. asp. 4 See http://www. donotpay. treas. gov/About. htm. 5 In Europe,
http://policyreview. info/articles/analysis/big data-big-responsibilities. 6 See https://www. google. org/denguetrends/.
of the sample regulation on the Indian Department of Telecommunications website (http://dot. gov. in/sites/default/files/Unified%20licence 0. pdf). Chapter 5. The role of big data for ICT monitoring and for development
Bollier, D. 2010), The Promise and Peril of Big data. WASHINGTON DC. Bouckaert, J.,Van dijk, T. and Verboven, F. 2010), Access regulation, competition, and broadband penetration:
Boyd, D. and Crawford, K. 2012), Critical questions for big data. Information, Communication & Society, 15 (5), 662 679. doi:
http://www. cck. go. ke/resc/downloads/Sector statistics report q2 201314. pdf. David, T. 2013), Big data from Cheap Phones.
Harford, T. 2014), Big data: are we making a big mistake? Financial times. pp. 7 11. Retrieved 28 march 2014 from:
The real-world use of big data. How innovative enterprises extract value from uncertain data. Retrieved from:
http://public. dhe. ibm. com/common/ssi/ecm/en/gbe03519usen/GBE03519USEN. PDF. IBM (2013), The Four V's of Big data.
http://www. ibmbigdatahub. com/sites/default/files/infographic file/4-Vs-of-big data. jpg. ICT Qatar (2014), Qatar's ICT landscape 2014 households and individuals.
http://www. itu. int/en/ITU-D/Statistics/Documents/publications/mis2012/MIS2012 WITHOUT ANNEX 4. pdf. ITU (2013), Big data:
Report 2014 ITU GSR discussion paper (2014), Big data-Opportunity or Threat. Retrieved from: http://www. itu. int/en/ITU-D/Conferences/GSR/Documents/GSR2014/Discussion%20papers%20and%20presentations%20-%20gsr14/Session3 gsr14-Discussionpaper-Bigdata
. pdf. Jones, S. 2012), Why Big data is the fourth factor of production, Financial times. Retrieved 27 december 2012 from:
Gartner, 6. Lazer, D.,Kennedy, R.,King, G. and Vespignani, A. 2014), Big data. The parable of Google Flu:
traps in big data analysis. Science (New york, N y.),343 (6176), 1203 5. doi: 10.1126/science. 1248506.
Lokanathan, S.,Silva, N. de, Kreindler, G.,Miyauchi, Y. and Dhananjaya, D. 2014), Using mobile network big data for informing transportation and urban planning in Colombo.
Manyika, J.,Chui, M.,Brown, B.,Bughin, J.,Dobbs, R.,Roxburgh, C. and Byers, A. H. 2011), Big data:
Mayer-Schönberger, V. and Cukier, K. 2013), Big data: A revolution that will transform how we live,
Mcafee, A. and Brynjolfsson, E. 2012), Big data: the management revolution. Harvard Business Review, 90 (10), 60 6, 68,128.
What big data means for social science. Headcon'13 Part I. Retrieved from: http://edge. org/panel/headcon-13-part-i. Naef, E.,Muelbert, P.,Raza, S.,Frederick, R.,Kendall, J. and Gupta, N. 2014
2012), Demystifying Big data: A Practical Guide To Transforming The Business of Government. Retrieved from: http://www. techamerica. org/Docs/filemanager. cfm?
f=techamerica-bigdatareport-final. pdf. The White house (2014), Big data: Seizing Opportunities, Preserving values. Executive Office of the President.
http://unohrlls. org/custom-content/uploads/2013/10/State-of-the-LDCS-2013. pdf. UNSC (2013), Report of the Secretary general on Big data and modernization of statistical systems.
UNSC (2014), Big data and modernization of statistical systems, December 2013. Available at: http://unstats. un. org/unsd/statcom/doc14/2014-11-Bigdata-E. pdf. Van damme, E. 2002), The European UMTS-auctions, European Economic
10.1016/S0014-2921 (01) 00219-7. Varian, H r. 2013a), Beyond Big data. NABE Annual Meeting. San francisco, CA. Varian, H r. 2013b), Big data:
New Tricks for Econometrics. Wallsten, S. 2001), An econometric analysis of telecom competition, privatization, and regulation in Africa and Latin america.
A Survey of Big data Definitions, 2. Databases. Retrieved from: http://arxiv. org/abs/1309.5821. Waverman, L. and Koutroumpis, P. 2011), Benchmarking telecoms regulation:
Rewards and Risks of Big data (B. Bilbao-Osorio, S. Dutta and B. Lanvin, Eds..References 220 Wesolowski, A. and Eagle, N. 2010), Parameterizing the Dynamics of Slums.
< Back - Next >
Overtext Web Module V3.0 Alpha
Copyright Semantic-Knowledge, 1994-2011