Recent data (April 2010) provided by ANCOM show a 12.4%increase in the number of fixed broadband connections at the end of 2009 versus the same period of 2008, from 2, 5 to 2, 8 million connections.
These preliminary data indicate a penetration rate of broadband fixed connections at 13.1%of the population and 34.2%of households at the end of last year.
A. Government increased capacity to take decisions based on resources offered by Information Society Facilitate obtaining data,
and processing data, information and updated documents B. Streamline relations with public institutions Increased access to electronic public services Strengthening public confidence in electronic services Ensure protection of personal data Increased performance of public services
and health through electronic public services D. International Context Improve information security Reassessment of host status for data Increased competitiveness
INE and Datacomex. 1 Data available in December 2012. This table presents the available data for each indicator in the latest update of each source.
Population and territory The Autonomous Community of the Balearic islands is located in the Mediterranean sea along the eastern coast of the Iberian peninsula.
October 2012 data The unemployment rate in Balearic islands in 2011 (21,96%)is well above the European average (9, 70%)and the OECD countries (8, 20),
Evidently, this raises fundamental new questions about management levels, responsibilities, control rights, data access, markets and market roles, as well as regulation.
competitive energy markets, efficient use of energy, integration of fluctuating renewables, consumer protection and data safety, industry development, research.
to avoid the need of regular visual data collection by utility personnel, to gain a better short-term control over electricity losses (including theft prevention),
to define or restructure responsibilities for data handling, customer contact or local congestion management, easily lead to considerable shifts in technological, commercial and political power between the players involved.
competitive energy markets, efficient use of energy, integration of fluctuating renewables15, consumer protection and data safety, 16 industry development (mainly ICT, electrical equipment, appliances
2) ensuring data protection for consumers;(3) establishing a regulatory framework to provide incentives for Smart Grid deployment;(
and a stronger representa--tion of the telecom industry) has been appointed by DG Energy. 25 The updated mandate puts a stronger emphasis on regulatory issues as well as on communication and data handling.
This is a huge growth opportunity for the corresponding industries offering hardware, software, data handling and communication.
http://www. tdeurope. eu/data/TD%20europe%20position%20paper%20on%20infrastructures%20and%20smar t%20grids%20010212. pdf 48 This is also the case in member
Who will have access to real-time consumption data of the customers? Will there be distribution--level markets for optimally managing capacities,
A centralised approach would require much more efforts for data gathering, data handling, data security and discussions on data privacy.
and have access to detailed consumer data. Moreover, in a perspective where consumption and production of energy cannot be separated clearly anymore since increasing numbers of (industrial,
Bundesverband Neuer Energieanbieter, Berlin. http://www. neue--energieanbieter. de/data/uploads/20111010 bne positionspapier smart grids. pdf Brandstätt, C.,Brunekreeft, G.,Fridrichsen, N.,2012.
Research methodology APPROACH DATA METHOD Quantitative Statistical data from Spanish National Statistics Institute (INE) on number of establishments, GDP, employment and Input-Output regional
) Qualitative Qualitative data: all the regional RIS3 in Spain available from the Spanish Ministry of Finance and Public Administration (MINHAP)( http://www. dgfc. sgpg. meh. es/sitios/dgfc/)and each regional
the tuning of priority choices included, indicators to follow-up and improve the strategy, a data set of policies (some
1 Background information on research and innovation in Aragon Palma de Mallorca RIS 3 meeting, February 2013 Key socio economic data Aragón is proud of
from data facilitated by IDEPA. After that general introduction, we will discuss position in the last Regional Innovation Scoreboard,
According to the latest population data, for the advancement of the Municipal Register to January 1, 2011, Asturias has 1,
according to the latest data provided by the National Statistics Institute in Central Business Directory. This is a decrease from the previous year of 1. 3
With a noticeable drop in spending on innovation over the past two years with available data, 2009 and 2010.
In view of the above data the main features of the Asturian economy and more specifically of its industrial sector would be the following:
With due caution with the data presented in Expert Assessment of RIS3 strategy for the region of Asturias, Spain Miquel Barceló 6 this report,
See more details in annex 5. 1. For every institution visited some basic data, functions and available figures are provided.
Movilidad, semantica, inteligencia ambiental y Open Data. Sede W3c. 80 personas Presupuesto 2011: 4 M€ Pieza clave de una futura estrategia del sector TIC.
The strategy has been iniciated by IDEPA with the preparation of some data based on enquiries and information already available,
By indirect data like companies participating in Neotec programs (CDTI loans to based technology), EIBT certification by ANCES (National Association of CEEI's) or by information provided by the University,
Economic data The (Gross domestic product) GDP of Cantabria was 13.289,89 millions of € in 2011. The GDP per capita in Cantabria was 22. 407€ in 2011.
Economic data GDP distribution Services 61%Industry and energy 17%Construction: 12%Agriculture: 3%A high percentage of GDP in services, is a symptom of developed region.
Economic data Industry GDP distribution 34.72%Metal processing. 12.51%Food, beverages and tobacco. 10.37%Chemistry. 8. 01%Machinery and 6, 2%34,72%6
%Car parts. 12,51%10,37%8, 01%7, 61%6, 5%Economic data Services GDP distribution 26,87%Business services. 13,76%Tourism
%13%10%8%8%Economic data The main export destinations: France. Germany. Italy. U k. Portugal.
there is a significant increase in the R&d activity of the enterprises as compared with the data of one decade ago, in
%Figure 5. Main data of‘HAMHIT'Companies in Castilla y León. Source:
RESEARCH AND INNOVATION STRATEGY FOR SMART SPECIALISATION (RIS3) OF CASTILLA Y LEON 2014-2020 EXECUTIVE SUMMARY The Research and Innovation S t r a t e g y
%somewhat above than the national average(-0. 4%)and below the European average (2. 2%).According to the data published by EUROSTAT,
Based on objective data and including solid supervision and evaluation systems. 1. 2 METHODOLOGY The elaboration of the Castilla y León RIS3 has followed the six-step methodology published by the European commission's Smart Specialisation Platform4,
The latest available data assigns economic returns for Castilla y León participation in national R&d programmes compared with the national total of 3. 5%in 2012.
Concerning Internet access technology, data for the first quarter of 2013 in Castilla y León are very positive,
Additional noteworthy data is the positive evolution of technology by the youngest part of the population, especially in the 10 to 15-year-old age range.
where usage data and ICT availability continue to be low with minor annual economic growth. In 2012, specifically, only 68%of companies with fewer than 10 employees had compared computers with 71.6%at the national level.
Usage data from companies using the Castilla y León Online Government is better than the usage of these services by citizens:
cloud computing, and new pay-peruse models, Open Data; new models for collaboration with other companies.
9 MONITORING AND EVALUATION 1 Latest data available from 2010.2 National Statistics Institute. 3 Scopus Database, Elsevier. 4 Application of historical data queried from the Spanish
and Tourism. 7 Data corresponding to the first six months of 2013.8 At least once a week during the last three months. 9 Data corresponding to 2013.
Open Data, demand for contents, more usable technologies closer to citizens, etc. Growing possibilities for use of ICT in the public sector (energy saving, education, health, social care.
and gather information, through open data gathering techniques, crowdsourcing and so on. The potential importance of digital dialogue, including for Smart Specialisation for Regional Innovation:
This book presents the relevant data and policy information from 40 economies around the world,
In addition to presenting the data, the report also explores the policy imperatives in three major yet insufficiently recognised action areas that are new to much of the policy world.
providing up-to-date data and highlighting current policy issues of greatest concern. It draws in particular on the expertise and analysis of the OECD's Working Party on SMES and Entrepreneurship and the Directing Committee of the Local Economic and Employment Development Programme.
Notes on the country data...128 Chapter 3. Knowledge Flows...131 Introduction...132 How knowledge affects entrepreneurship...
Presents a set of country-level data on SMES, entrepreneurship and innovation performance, and a review of major policies and new policy developments in the field.
The data also show substantial EXECUTIVE SUMMARY SMES, ENTREPRENEURSHIP AND INNOVATION OECD 2010 17 shares of total activity accounted for by each of the sub-categories of micro,
The data suggest that SMES innovate less than large firms across a range of categories including product innovation, process innovation, non-technological innovation, new-tomarket product innovations and collaboration in innovation activities.
and data is not commonly available for non-technological innovation as a proportion of firm employment or turnover.
Chapter 2 provides data on SME innovation performance and constraints across 40 economies and examines the major and new policies that have been introduced.
Definitions Supporting Frameworks for Data Collection, OECD Statistics Working papers, 2008/1, OECD Publishing, Paris, doi: 10.1787/243164686763.1.
New Evidence from Micro Data, Ch. 1, pp. 15-82, University of chicago Press, Chicago. Baumol, W. 2002), The Free-Market Innovation Machine:
Each Country Note is accompanied by structural data on the SME sector and selected indicators showing SME innovation performance, perceived barriers to innovative activities
When available, data are presented also for accession countries (Chile, Estonia, Israel, Russia and Slovenia) and enhanced engagement countries (Brazil, China, India, Indonesia and South africa.
Box 2. 1. Basic methodological references Data presented in the chapter come from three main sources:
Structural indicators of the enterprise population Data are drawn from the OECD dataset Business Statistics by Size Class,
1. Data only reflect enterprises with 3 or more persons engaged. 2. As%of all firms within size class. 3. 2002-04.4.
1. For manufacturing, data only reflect enterprises with 4 or more persons engaged. 2. As%of SMES with new product sales. 3. Index scale of 0-6 from least to most restrictive.
1. For manufacturing, data only reflect enterprises with 5 or more persons engaged. 2. As%of all firms within size class. 3. As%of total turnover. 4. Index scale of 0-6 from least to most restrictive.
combined, they provided legal recognition to transactions carried out by electronic data interchange. Several other actions, including the establishment of Cyber Laws, the setting up of the Cyber Regulations Appellate Tribunal,
which include data relating to the Golan heights, East Jerusalem and Israeli settlements in the West bank. 1. Czech republic, Hungary, Korea, Mexico, Poland, Slovak Republic, Turkey. 2. Austria, Belgium, Finland
AN OVERVIEW BY COUNTRY SMES, E 128 NTREPRENEURSHIP AND INNOVATION OECD 2010 ANNEX 2. A1 Notes on the Country Data The structural data on businesses presented in the chapter
Most data presented refer to the nonfinancial business economy, i e. ISIC Rev. 3/NACE Sections C to I and K and is subdivided into Industry (Sections C, D,
The following text gives details on the completeness of the data for each country. Australia:
Given the limits of official data sources for local-level analysis, we turn to firm-level information from the commercial ORBIS database on the location, nature and performance of local innovation clusters.
Selected clusters are compared then using data on business demography and business performance. These findings represent some of the first outputs of an OECD project to map clusters
structural and selection biases in the original data source. 3 3. KNOWLEDGE FLOWS SMES, ENTREPRENEURSHIP AND INNOVATION OECD 2010 137 European union The first two maps show the agglomeration of HTM
and United kingdom. Given problems of comparability between the NUTS classification and the location information included in the original data source, countries such as Denmark, Luxembourg, The netherlands,
which might also infer a bias in the original data sources. Secondly, KISA firms often tend to Figure 3. 2. Distribution of KISA firms in the European union (Quantiles based on LQS) Source:
Figure 3. 5 points out the location of KISA firms in the United states using the same data source,
In the case of the US clusters, given data source constraints for this country, the composite indicator is limited to the first three variables in the list.
Despite the increasing importance of university-industry knowledge transfers and of public schemes that try to bolster such transfers, it is difficult to produce reliable and comparable data on this phenomenon.
In addition, data collection is regular in some countries but sporadic in others. 3. KNOWLEDGE FLOWS SMES,
Data show that KTOS have a much longer tradition in the United states than in Europe.
Finally, data on university spin-offs in the two different contexts diverge much more slightly, with nearly 2 spin-offs per KTO a year in Europe and nearly 3 in the United states. University-industry knowledge transfers are also of increasing importance in Asia;
Data, however, show that knowledge transfer is still incipient in China. Universities have a great number of patents (126 per KTO),
Evidence from European Patent Data, European Economic Review, Vol. 47, pp. 687-710. Cantner U.,M. Goethner and A. Meder (2007), Prior Knowledge and Entrepreneurial Innovative Success, Jena Economic Research Paper, No. 2007-052.
is a source of business micro data. The database includes around 40 million companies, has a geographical coverage of up to 200 countries,
unless national limitations reduce the coverage of administrative data sources. Given national data source constraints
there is plenty of information at the firm level about sector, legal status, ownership and an array of financial and economic variables.
The value of the ORBIS database for territorial analysis rests on the possibility to rearrange firm-level data according to detailed company location.
Potential biases of territorial data calculated from commercial databases The key territorial information included in a commercial database with firm-level data is the company's complete postal address.
Two different sources of location bias can be associated with the use of this information to produce territorial data.
As already outlined in the previous section, the attribution of all company activities to a single location leads to incoherent territorial data in the case of multi-plant companies.
other characteristics of commercial databases can indirectly alter the consistency of territorial data calculated from this sort of source.
which firms under a certain size are included not in the original data sets or by additional restrictions on revealing information on small areas imposed by the database provider.
Structural biases may be induced by a number of factors such as restrictions in the database and poor data quality consistency across industries, regions,
E 160 NTREPRENEURSHIP AND INNOVATION OECD 2010 The extent to which statistical biases affect indicators Territorial data in the form of absolute values are affected by coverage restrictions, structural bias and selection bias.
This is because simple aggregations of micro data by relevant territorial units completely mirror the characteristics of input data.
While the consistency of static territorial indicators is altered by an uneven spatial distribution of the sample of micro data as compared to the target population,
since commercial databases are upgraded sometimes in terms of coverage and data quality. This database upgrading may induce a structural break that can alter the spatial distribution of companies by increasing
Spatial) econometric modelling may also represent a possible way to deal with different types of territorial bias in the data.
This discussion balances the unquestionable informative relevance of commercial databases to carry out nonstandard territorial analysis with an insight into the methodological problems that may affect the consistency of these territorial data.
Moran'S i and LISA differ in data employed and analytical scope. Moran'S i is a global measure of spatial autocorrelation.
and the overall pattern in the data is summarised in a single statistic. In contrast, LISA calculates a local version of Moran'S i for each areal unit in the data.
In particular, LISA shows statistically significant groupings of neighbouring areas with high and low values around each region in the study area.
data confirm the existence of a skills and training problem holding back innovation. Across OECD countries, employees of SMES participate in formal training activities to only half the extent that staff in large firms do (OECD, 2010b, forthcoming.
Across the EU-15 countries, data from the Eurostat Continuing vocational training Survey show that employees in enterprises with less than 50 employees receive significantly less in-company training than employees in larger firms.
The Social Entrepreneurship Monitor is a special report of the Global Entrepreneurship Monitor (GEM) UK project to estimate the percentage of social entrepreneurs in UK society using population survey data.
recent UK data released by the Third Sector in July 2009 (www. cabinetoffice. gov. uk/media/231495/factoids. pdf) refer to an estimated average (2005-07) of 61
8 in July 2009) out of 515 organisations that applied (data elaborated for OECD by the Korea Labor Institute and the Research Institute of Social Enterprise.
In the United states, the Johns Hopkins Nonprofit Economic Data Project (NED) is generating information on the dynamics of the nonprofit sector by analysing diverse datasets on nonprofit organisations,
including data on nonprofit finances, employment and wages, and volunteering. The website of the project (www. ccss. jhu. edu/index. php?
In addition, country notes present statistics and policy data on SMES, entrepreneurship and innovation for 40 economies, including OECD countries, Brazil, China, Estonia, Indonesia, Israel, the Russian Federation
Open Data movements and innovative/transparent forms of governance go hand in hand (http://data. gov. uk) with these new forms of coproduction.
The Open Data movement lobbies government institutions, international organizations and the private sector to make private and public databases available to application developers.
Data is an important resource and output of these social media innovations. Opening up government data silos to developers and communities is
therefore potentially one way to support this growing social-digital economy. Yet to be of any use,
the new superfluity of data needs to be structured, analyzed and interpreted (Wilson et al. 2013). This is an increasingly pressing challenge,
Data Is the Solution! What was the Question Again? Public Money & Management 33 (3): 163 166. doi:
collecting and correlating social network data (e g. degree, density, etc.)for innovation success Providing a framework for timely communication and distribution of experiences, contextual information,
and displaying data and is used as the front-end tool for the application in conjunction with Oracle 9i database as the backend.
Through data-driven tables of contents, Oracle Forms provides users with an easy interface to the information that is required.
The Entity Relationship diagram (ERD) in Figure 6 represents the data modeling of I3. The figure represents the organization of data into entities and the relationships between the entities in I3.
Although the actual system includes both user modules and administrator modules only the administrator modules that focus on the management of a social network based digital city is discussed in this study.
as the administrator is responsible for updating the database to ensure that the data in the system is updated and true at all times,
and manage all meta-data and other pre-conditional data required for the system. These include metadata pertaining to user, company, resource or request management like Education level, Occupation list, Ethnic group, Expertise areas, NAICS code, Nonprofit type, Occupation type
, Session type, Milestone, Counties, zipcodes, regions, contact method etc. The subsequent sections will discuss how the ERD for the various management tasks has been designed
) User registration and profiles are created through an initial registration process and data (e g.,, occupation, education information, college teaching, highest education level, privacy level, etc.
The survey statement entity manages the who knows who'data collated from a survey deployed during the registration process
Similar data is collected and managed for all resource types. Each resource is tagged to its owner (a registered user)
all data entry fields are enabled with smart tips to provide information (e g.,, type, length, restrictions,
or meanings of data) or helpful tips when a user moves mouse over a field,
Smart tips are enabled for all data fields Figure 12: Resource Management Smart tips enables. Figure 13:
In I3 several graphical representations are provided with many reports to give the administrator a different direction for analyzing data.
but analyzing the data and building a knowledge base would help build a stronger digital community.
Who Knows Who Data Presents the user's social connectedness in the network and can help identify important nodes in the network Social network Analysis ACKNOWLEDGMENTS This project is funded by National Science Foundation Award#0332378, Partnerships for Innovation Program, Dr. John hurt Program Director
His research interests are in the fields of data/text mining, business process simulation, software agent applications,
This demographic data implies a shift in healthcare spending and policy reform the two main areas that will impacted he hugely by this trend.
Smart technology will create an opportunity for our growing cities to operate far more efficiently than they do today using interconnected sensors and data analytics;
and business) o There is an opportunity for technology convergence (such as digital intelligence, internet of everything and data analytics in buildings, homes, grids, water networks, hospitals, cities, factories and transport
and enriched with secondary research across a host of both external and internal data sources,
data-driven approach to market quantification and forecasting through triangulated data inputs to derive its initial quantification of the global market potential represented by Social Innovation.
Social Innovation to answer Society's Challenges 2014 Frost & Sullivan 23 www. frost. com This Whitepaper has been developed by Frost & Sullivan in conjunction with Hitachi, Ltd.
This information, data and reports are needed more promptly and must be produced more speedily. This should include progress updates on projects from implementing bodies as well as quantitative data;
-The levels of bureaucracy and administrative burden on beneficiaries within the RDP need to be reduced further
there is one EU data commissioner but the legislation in countries is still different. When we go to Germany,
Kenneth Cukier is data editor at The Economist and co-author with Viktor Mayer-Schönberger of Big data:
and these huge amounts of data can teach us things that are extremely interesting, in fact things we would never have been able to find out with smaller amounts.
That's done by placing different algorithms onto these large amounts of data. Let me give you an example.
and compared them against historical influenza data from the Centers for Disease Control and Prevention.
After running half a billion calculations against their data Google identified 45 terms that strongly coincided with CDC's data on flu outbreaks.
The Google trends method has been criticised, because its been wrong in some instances. However that is not the whole story.
The autopilot system on their airplanes collects data. Some of the data it collects has improved actually the accuracy of German weather forecasting by 7%
which is a considerable improvement. Lufthansa now sells that data to a meteorological company, which is a great example of how big data can be commodified.
So big data can be sold? Absolutely. In fact big data is a potential gold mine. There are a few forward-thinking companies who have realised they can sell the data they collect as they go about their everyday work.
It will be a revenue generator. In the future I expect to see companies employing data or chief information officers,
who will be responsible for this. It's not just companies. In the future, each of us will be able to sell our data.
People will upload data to online data exchanges, neutral platforms which can bring the data to the marketplace for a fair price.
And there will be a market for this data as people realise the enormous potential of big data.
Will there be an impact on how people work? There will be a significant impact. This will be a revolution in the workplace.
Both white colour and blue collar jobs will be replaced by big data, but that destruction will also create jobs.
But in in a world where data shape decisions more and more, what purpose will remain for people,
Essentially, this means that if you want to use someone's data, you have to tell them what you are collecting and why.
it is impossible to know what purpose the data will be used for. Small data is like a waltz.
There's a clear tempo with known steps. Big data is like a mosh pit or jazz-improv.
for that data to be used and reused and reused without knowing what the specific purpose is.
The data mentioned is catalytic and shows us that this is the direction we need to move in,
and provides data, and other evidence that demonstrates how a business creates and delivers value to customers.
Data, Tools and Research, held at the U s. Department of commerce, Washington, D c.,25-26 may 1999. It draws upon joint work with Edward Steinmueller,
Gordon (1998a) presents more finely disaggregated data on labor productivity, which reveals the pervasiveness of the slowdown. 9 pronounced between the period 1948-66 and 1966-89.
and improved access to marketing data are indeed enabling faster, less costly product innovation, manufacturing process redesign,
By combining this with data from Bailey and Gordon (1988) on the rising number of products stocked by the average U s. grocery supermarket,
the spread of partially networked personal computers supported the development of new database and data entry tasks, new analytical and reporting tasks,
The primary bridge between these application environments was the widespread use of the IBM 3270, the DEC VT-100 and other standards for"intelligent"data display terminals, the basis for interactive data
and related data logging devices were to be found in the hands of maintenance, restaurant, and factory workers.
These more"task specialized"devices have become sufficiently ubiquitous to provide the infrastructure for task-oriented data acquisition and display systems,
and maintenance of critical company data resources must be resolved, and these often are compelling enough to force redesign of the organizational structure, Thirdly,
Applications and their maintenance can be controlled by the technical support team who would previously have been responsible for the company's centralized data resources.
and the Data Constraint, American Economic Review, Mar. 1994,84, pp. 1-23. Griliches, Zvi, Comments on Measurement Issues in Relating IT Expenditures to Productivity Growth, Economics of Innovation and New Technology, 3 (3-4), pp. 317-21,1995:
Firm-Level Evidence from Government and Private Data Sources, 1977-1993, Canadian Journal of Economics, 1998.
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