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Academic considerations on the topic of political marketing very often boil down to discussing the political history of the 20th and 21st centuries, while failing to account for the significant changes in the range of instruments used to implement election strategies. However, the academic discussion about political marketing as an applied sub-discipline should take into account some mechanisms for accomplishing specified tasks. Therefore, the purpose of this paper is to fill this gap and try to examine the influence of the tools based on big data on the broad picture of what we name marketing activities in the field of politics. We would like to present readers with our hypothesis that the profound changes in this area that were particularly noticeable in the election and referendum campaigns in 2016 may provide a premise to identify a new paradigm in the discussion on the use of marketing strategies in political communication. It would be based on the Internet combining the function of a communication channel and a source of data about voters. This information is next used for marketing purposes. It should be emphasized that this mechanism that has been used in the field of commercial activities for almost a decade, in the field of politics produces completely new and potentially dangerous consequences for citizens ; Rozważania naukowe dotyczące marketingu politycznego bardzo często sprowadzają się do dyskusji na temat politycznej historii XX i XXI wieku z pominięciem istotnych zmian w zakresie narzędzi wykorzystywanych do realizacji strategii wyborczej. Tymczasem analiza naukowa dotycząca marketingu politycznego, jako subdyscypliny stosowanej, uwzględniać winna mechanizmy realizacji określonych zadań. Celem niniejszego artykułu jest więc uzupełnienie tej "luki" i próba zastanowienia się nad wpływem narzędzi opartych o Big data na szerszy obraz tego, co nazywamy zabiegami marketingowymi w obszarze polityki. Pragniemy też poddać pod osąd czytelnika hipotezę, iż daleko idące zmiany w tym obszarze, w szczególności widoczne w kampaniach wyborczych i referendalnych w 2016 r., mogą stanowić przesłankę wyodrębnienia nowego paradygmatu w dyskusji na temat wykorzystania strategii marketingowych w komunikowaniu politycznym. Jest on wynikiem połączenia funkcji kanału komunikacji (internet) z jednoczesnym wykorzystaniem go jako źródła zdobywania danych o wyborcach. Te są zaś wykorzystywane w celach marketingowych. Podkreślić należy, iż mechanizm ten, od niemal dekady stosowany w obszarze działań komercyjnych, na gruncie polityki rodzi zupełnie nowe, potencjalnie niebezpieczne dla obywatela konsekwencje.

Big Data for Qualitative Research covers everything small data researchers need to know about big data, from the potentials of big data analytics to its methodological and ethical challenges. The data that we generate in everyday life is now digitally mediated, stored, and analyzed by web sites, companies, institutions, and governments. Big data is large volume, rapidly generated, digitally encoded information that is often related to other networked data, and can provide valuable evidence for study of phenomena. This book explores the potentials of qualitative methods and analysis for big data, including text mining, sentiment analysis, information and data visualization, netnography, follow-the-thing methods, mobile research methods, multimodal analysis, and rhythmanalysis. It debates new concerns about ethics, privacy, and dataveillance for big data qualitative researchers. This book is essential reading for those who do qualitative and mixed methods research, and are curious, excited, or even skeptical about big data and what it means for future research. Now is the time for researchers to understand, debate, and envisage the new possibilities and challenges of the rapidly developing and dynamic field of big data from the vantage point of the qualitative researcher.

The government is liable to help every citizen for employability, minimum facilitation of livelihood and almost all countries have shown their concern for. There are several ways of helping the poor and weaker section of society as well as all resident of a community/area or whole population and one of them subsidy. All subsidy schemes have two objectives, to help the common man and to upgrade their economic and living status. These have several success and failure stories and need up-gradation at every actionable day to protect the frauds, minimize the financial burden to government and maximize the impact of schemes. The main objective of the present study is to assess the issues associated with government subsidies and proposing the solution from Big data, most accepted source of information in current information era. The study review the source of information which may be usable for the subsidy mechanism and scaled it through data driven technique which will be more transparent, democratic and recommend the more responsible governance system after assigning the stepwise monitoring and verification from the data.

Abstract The delivery of psychiatric care is changing with a new emphasis on integrated care, preventative measures, population health, and the biological basis of disease. Fundamental to this transformation are big data and advances in the ability to analyze these data. The impact of big data on the routine treatment of bipolar disorder today and in the near future is discussed, with examples that relate to health policy, the discovery of new associations, and the study of rare events. The primary sources of big data today are electronic medical records (EMR), claims, and registry data from providers and payers. In the near future, data created by patients from active monitoring, passive monitoring of Internet and smartphone activities, and from sensors may be integrated with the EMR. Diverse data sources from outside of medicine, such as government financial data, will be linked for research. Over the long term, genetic and imaging data will be integrated with the EMR, and there will be more emphasis on predictive models. Many technical challenges remain when analyzing big data that relates to size, heterogeneity, complexity, and unstructured text data in the EMR. Human judgement and subject matter expertise are critical parts of big data analysis, and the active participation of psychiatrists is needed throughout the analytical process.

One way to study the politics of big data is the inspection of their assemblages. By opening up the "black boxes" of data assemblages, it is possible to reconstruct the choices, compromises, conflicts and agree-ments that contributed to the construction of a given datum. Leaning on in-depth interviews and focus groups with experts and specialists who work within three European data centers, we unveil the interde-pendence between social and technical aspects and between a series of internal and external actors, which all contribute to the data assemblage. Results show that communities of experts, technologies, stakeholders and end-users are entwined components that interact amongst them in a contingent and complex web of negotiations and constraints and frame what is possible, desirable and expected by data.

The Rotation Forest classifier is a successful ensemble method for a wide variety of data mining applications. However, the way in which Rotation Forest transforms the feature space through PCA, although powerful, penalizes training and prediction times, making it unfeasible for Big Data. In this paper, a MapReduce Rotation Forest and its implementation under the Spark framework are presented. The proposed MapReduce Rotation Forest behaves in the same way as the standard Rotation Forest, training the base classifiers on a rotated space, but using a functional implementation of the rotation that enables its execution in Big Data frameworks. Experimental results are obtained using different cloud-based cluster configurations. Bayesian tests are used to validate the method against two ensembles for Big Data: Random Forest and PCARDE classifiers. Our proposal incorporates the parallelization of both the PCA calculation and the tree training, providing a scalable solution that retains the performance of the original Rotation Forest and achieves a competitive execution time (in average, at training, more than 3 times faster than other PCA-based alternatives). In addition, extensive experimentation shows that by setting some parameters of the classifier (i.e., bootstrap sample size, number of trees, and number of rotations), the execution time is reduced with no significant loss of performance using a small ensemble. ; This work was supported through project TIN2015-67534-P (MINECO/FEDER, UE) of the Ministerio de Economía y Competitividad of the Spanish Government, projects BU085P17 and BU055P20 (JCyL/FEDER, UE) of the Junta de Castilla y León, Spain (both projects co-financed through European Union FEDER funds), and by the Consejería de Educación of the Junta de Castilla y León, Spain and the European Social Fund through a pre-doctoral grant (EDU/1100/2017). The project leading to these results has received also funding from "la Caixa" Foundation, Spain , under agreement LCF/PR/PR18/51130007. This material is based upon work supported by Google Cloud.

Big Data ist eines der spannendsten Mysterien der Gegenwart. Jeder redet davon, keiner weiß, wie es genau geht, alle geben vor, es zu tun - so hat es der Ökonom Dan Ariely mal formuliert. Dieser Kurzbericht gibt einen Überblick darüber, inwiefern Big-Data-Analysen in Wirtschaftswissenschaft und Politikberatung bereits eingesetzt werden und welche Nutzungspotenziale es gibt.

Das digitale Datenvolumen der Welt verdoppelt sich alle zwei Jahre. Es gibt um ein Vielfaches mehr Internet-fähige Geräte als Menschen auf der Erde. Milliarden vernetzte Sensoren, stetig fließende Datenströme und selbstlernende Algorithmen - wir leben im Zeitalter von Big Data. Internetkonzerne, Nachrichtendienste, viele Wissenschafts- und Wirtschaftsbereiche nutzen seit Jahren die Potenziale zur Analyse großer Datenmengen. Mit ihrer Einführung in Marketingstrategien, in die politische Willensbildung und in die Lernprozesse selbst (Learning Analytics) werden Big Data Analysen lebensweltlich erfahrbar und zur gesellschaftlich übergreifenden Bildungsherausforderung. Welche Aspekte machen Big Data zum Thema für die kritische Medienbildung? Inwieweit können Daten und abstrakte Algorithmen medienpädagogisch bearbeitet werden? Lässt sich souveränes Handeln in Datenwelten durch digitale Selbstverteidigung und digitale Ethik stützen? Der Band 3 der Schriftenreihe zur digitalen Gesellschaft des Landes Nordrhein-Westfalen bündelt Reflexionen, Positionen und Ansätze zum Umgang mit der gesellschaftlichen Revolution Big Data aus Perspektiven der Medienbildung und der medienpädagogischen Forschung – mit Beiträgen von Sandra Aßmann und Valentin Dander (Universität zu Köln), Niels Brüggen (JFF - Institut für Medienpädagogik in Forschung und Praxis), Harald Gapski (Grimme-Institut), Petra Grimm (Institut für Digitale Ethik / HdM) und Birgit Kimmel (LMK / EU-Initiative klicksafe), Christopher Koska (Hochschule für Philosophie München), Gerda Sieben (jfc Medienzentrum), Stefan Selke (Hochschule Furtwangen) und Isabel Zorn (TH Köln). (Herausgeber)

".Big data refers to things one can do at a large scale that cannot be done at a smaller one, to extract new insights or create new forms of value, in ways that change markets, organizations, the relationship between citizens and governments, and more."1 Today, technology is more a part of our lives than ever before. With more and more people all over the world gravitating towards social media and using sites such as Twitter and Facebook, more of our private lives is available to others than ever before. In his article "Big Data and Privacy" Tom Price explores the controversial role big data plays in our society today. While Price presents many of the benefits this data offers, he also notes its controversial nature. The main issue that big data collection and usage creates is that of consumer privacy. As Edward Snowden discussed in an interview with TV host John Oliver and throughout the documentary CitizenFour, organizations such as the NSA have access to unlimited amounts of data on the American public. Thus, while new technology has allowed for numerous advancements, it has also opened the doors for a debate on issues of privacy infringement. The world of big data has led society to a difficult crossroads, where people desire convenience and better, faster technology but simultaneously want to maintain their privacy. Although a huge issue, it seems many people do not recognize it as such, due to the fact that people are simply not aware of the existence, much less the use, of vast amounts of information, especially since big data is a relatively new concept. Additionally, it is often downplayed or presented in a very skewed manner. The government and other organizations that use this data for either surveillance or business practices do not want consumers to know how their private information may in fact be used. Thus when the risk of privacy infringement involves the government, it is usually justified for reasons of defense. Similarly, when companies are accused of misusing people's data, they often argue that its for the good of the consumer and allows them to better serve their customers.

The digital domain, technological innovations, and Big Data analytics increasingly shape the lives of millions of individuals, groups, organisation, and societies. This cyber age calls for effective governance to protect the basic interests and needs of individuals and groups. Simultaneously, the very nature of governance is changing. Increasingly policy-making moves away from top-down governance by the state towards more horizontal modes of governance. This paper argues that network in the term network governance needs to be taken seriously in order to reconceptualize power and responsibily in networked societies. Therefore, we combine the literature on governance with social network theory to conceptualise governance in the Cyber Age. It will be argued that dominant modes of governance are inadequate to govern the Cyber domain. Therefore, a novel networked mode of governance is proposed based on the regulation of new power relationships between Cyber actors and civil society.

This special issue offers a critical dialogue around the myriad political dimensions of Big Data. We begin by recognising that the technological objects of Big Data are unprecedented in the speed, scope and scale of their computation and knowledge production. This critical dialogue is grounded in an equal recognition of continuities around Big Data's social, cultural, and political economic dimensions.

Businesses, governments, and the research community can all derive value from the massive amounts of digital data they collect. Analyzing big data application projects by governments offers guidance for follower countries for their own future big data initiatives. Decision making in government usually takes much longer and is conducted through consultation and mutual consent of a large number of diverse actors, including officials, interest groups, and ordinary citizens. Governments deal not only with general issues of big data integration from multiple sources and in different formats and cost but also with some special challenges. The biggest is collecting data, governments have difficulty, as the data not only comes from multiple channels but from different sources. Most governments operating or planning big data projects need to take a step by step approach for setting the right goals and realistic expectations. Success depends on their ability to integrate and analyze information, develop supporting systems, and support decision making through analytics. Pravin Dattu Gangad | Pranay Pravin Gaikwad "Use of Big Data in Government Sector" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: https://www.ijtsrd.com/papers/ijtsrd15676.pdf

Wo wir sind, was wir tun und mit wem wir kommunizieren, wird heute routinemässig von zahllosen Geräten erfasst. Dies ist keine gezielte Überwachung, sondern eine inhärente Eigenschaft digitaler Technologie. Zudem können Daten auf immer einfachere Weise gespeichert und mit immer komplexeren Verfahren ausgewertet werden. Die Studie «Ethische Herausforderungen für Unternehmen im Umgang mit Big Data» hat die ethischen Herausforderungen von Big-Data-Anwendungen untersucht und Handlungsempfehlungen für Unternehmen und für Politik formuliert.

The delivery of psychiatric care is changing with a new emphasis on integrated care, preventative measures, population health, and the biological basis of disease. Fundamental to this transformation are big data and advances in the ability to analyze these data. The impact of big data on the routine treatment of bipolar disorder today and in the near future is discussed, with examples that relate to health policy, the discovery of new associations, and the study of rare events. The primary sources of big data today are electronic medical records (EMR), claims, and registry data from providers and payers. In the near future, data created by patients from active monitoring, passive monitoring of Internet and smartphone activities, and from sensors may be integrated with the EMR. Diverse data sources from outside of medicine, such as government financial data, will be linked for research. Over the long term, genetic and imaging data will be integrated with the EMR, and there will be more emphasis on predictive models. Many technical challenges remain when analyzing big data that relates to size, heterogeneity, complexity, and unstructured text data in the EMR. Human judgement and subject matter expertise are critical parts of big data analysis, and the active participation of psychiatrists is needed throughout the analytical process.

The editorial sets the scene for this thematic issue on big data applications in governance and policy. It highlights the lack of engagement in the current literature with the application of big data at the cross-section of governance of data and its utilization in the policy process and draws out aspects related to its definition and future research agenda. The contributions highlight several aspects related to big data in different contexts, such as local and national government as well as a variety of policy areas. They converge on the idea that big data applications cannot overcome existing political and structural limitations that exist in government. This leads to a future research agenda that looks at the disconnect between data production and usage as well as identifying policy issues that are more or less suitable for data analytics.