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This book, the third one of three volumes, focuses on data and the actions around data, like storage and processing. The angle shifts over the volumes from a business-driven approach in "Disruption and DNA" to a strong technical focus in "Data Storage, Processing and Analysis", leaving "Digitalization and Machine Learning Applications" with the business and technical aspects in-between. In the last volume of the series, "Data Storage, Processing and Analysis", the shifts in the way we deal with data are addressed.

The article focuses on the analysis of big data phenomenon that by the expansion of information technologyhas become a challenge for sociology and social statistics. The history of «big data» term origins is presented,the factors of appearance and development of this phenomenon are determined. It is noted that in thesociological perspective big data have not only transformed the methods of obtaining primary sociologicalinformation, but also changed the very logic of the study. The author considers that with a help of big datasociology will be able to return to its calling – the creation of a large theory of society, which, in turn, theopportunity to analyze and interpret big data depends on. Emphasis is placed on Cathy O'Neil's work «Bigdata. Weapons of Math Destruction…», which concludes that big data construct new forms of inequality in acontemporary world. It is stressed that through the focus of this idea the American researcher analyzes theimpact of big data on various spheres of public life: on the educational system, emphasizing the role ofuniversity rankings (which definition is not always transparent) in commercialization of higher education, itsturning into big business; on the law enforcement system, in particular in a country such as the United States,noting that the mathematical models developed for the country's police have discrimination grounds for poorand «colored» citizens; on the system of employment, credit system, etc. At the same time, she writes thatbecause of big data privacy is disappearing in people's lives, they are increasingly adapting to models of massbehavior, being under the influence of consumer and political (what is especially alarming) marketing. Theauthor of the publication notes that Cathy O'Neil, unfortunately, gives no answer to the question of how it ispossible to counteract the manipulative effects of big data. She relies heavily on ethical regulators andrecommends to data specialists create models with mandatory forward linkages. ; Публикация посвящена анализу феномена больших данных (BIG DATA), которые благодаря экспансииинформационных технологий стали вызовом для социологии и социальной статистики. Представленаистория происхождения термина «BIG DATA», определены факторы появления и развитиясоответствующего феномена. Отмечено, что в социологической перспективе большие данные не толькотрансформировали методы получения первичной социологической информации, но и изменили саму логикуисследования. По мнению автора публикации, благодаря большим данным социология сможет вернуться ксвоему призванию – созданию большой теории общества, от которой, в свою очередь, зависит возможностьанализа и интерпретации больших данных. Акцентировано внимание на работе Кейт О'Нил «BIG DATA.Оружие математического уничтожения . », главная идея которой заключается в том, что большие данныепорождают новые формы неравенства в современном мире. Подчеркивается, что с точки зрения этой идеиамериканская исследовательница анализирует влияние больших данных на различные сферы общественнойжизни: на образовательную систему, акцентируя роль университетских рейтингов (определение которыхдалеко не всегда является прозрачным) в коммерциализации высшего образования, его превращении в крупныйбизнес; на правоохранительную систему, в частности такой страны, как США, отмечая, чтоматематические модели, которые создаются для полиции этой страны, имеют дискриминационные признакиотносительно бедных и «цветных» граждан; на систему приема на работу, предоставления кредита и томуподобное. При этом она пишет о том, что из-за больших данных из жизни людей исчезает приватность, онивсе больше подстраиваются к моделям массового поведения, находясь под влиянием потребительского и (чтовызывает особую тревогу) политического маркетинга. Автор публикации отмечает, что у Кейт О'Нил, ксожалению, нет ответа на вопрос, как противодействовать манипулятивному воздействию больших данных.Она преимущественно полагается на этические регуляторы и рекомендует специалистам по даннымсоздавать модели с обязательными обратными связями. ; Публікацію присвячено аналізу феномену великих даних (BIG DATA), які завдяки експансії інформаційних технологій стали викликом для соціології та соціальної статистики. Представлено історію походження терміну «BIG DATA», визначено чинники появи та розвитку відповідного феномену. Зазначено, що в соціологічній перспективі великі дані не тільки трансформували методи отримання первинної соціологічної інформації, а й змінили саму логіку дослідження. На думку автора публікації, завдяки великим даним соціологія зможе повернутися до свого покликання – створення великої теорії суспільства, від якої, у свою чергу, залежить можливість аналізу та інтерпретації великих даних. Акцентовано увагу на роботі Кейт О'Ніл «BIG DATA. Зброя математичного знищення…», головна ідея якої полягає у тому, що великі дані породжують нові форми нерівності в сучасному світі. Підкреслено, що під кутом зору цієї ідеї американська дослідниця аналізує вплив великих даних на різні сфери суспільного життя: на освітню систему, акцентуючи роль університетських рейтингів (визначення яких далеко не завжди є прозорим) у комерціоналізації вищої освіти, її перетворенні на великий бізнес; на правоохоронну систему, зокрема такої країни, як США, зазначаючи, що математичні моделі, що створюються для поліції цієї країни, мають дискримінаційні ознаки стосовно бідних та «кольорових» громадян; на систему прийому на роботу, надання кредиту тощо. При цьому вона пише про те, що через великі дані із життя людей зникає приватність, вони все більше підлаштовуються до моделей масової поведінки, знаходячись під впливом споживчого та (що викликає особливу тривогу) політичного маркетингу. Автор публікації констатує, що у Кейт О'Ніл, на жаль, немає відповіді на питання, як протидіяти маніпулятивному впливу великих даних. Вона переважно покладається на етичні регулятори та рекомендує спеціалістам з даних створювати моделі з обов'язковими зворотними зв'язками.

In recent years, social media analysis is arousing great interest in various scientific fields, such as sociology, political science, linguistics, and computer science. Large amounts of data gathered from social media are widely analyzed for extracting useful information concerning people's behaviors and interactions. In particular, they can be exploited to analyze the collective sentiment of people, understand the behavior of user groups during global events, monitor public opinion close to important events, identify the main topics in a public discussion, or detect the most frequent routes followed by social media users. As an example of the countless works in the state-of-the-art on social media analysis, this paper presents three significant applications in the field of opinion and pattern mining from social media data: (i) an automatic application for discovering user mobility patterns, (ii) a novel application for estimating the political polarization of public opinion, and (iii) an application for discovering interesting social media discussion topics through a hashtag recommendation system. Such applications clearly highlight the abundance and wealth of useful information in many application contexts of human life that can be extracted from social media posts.

The term Big Data has been recently used to define big, highly varied, complex data sets, which are created and updated at a high speed and require faster processing, namely, a reduced time to filter and analyse relevant data. These data is also increasingly becoming Open Data (data that can be freely distributed) made public by the government, agencies, private enterprises and among others. There are at least two issues that can obstruct the availability and use of Open Big Datasets: Firstly, the gathering and geoprocessing of these datasets are very computationally intensive; hence, it is necessary to integrate high-performance solutions, preferably internet based, to achieve the goals. Secondly, the problems of heterogeneity and inconsistency in geospatial data are well known and affect the data integration process, but is particularly problematic for Big Geo Data. Therefore, Big Geo Data integration will be one of the most challenging issues to solve. With these applications, we demonstrate that is possible to provide processed Big Geo Data to common users, using open geospatial standards and technologies. NoSQL databases like MongoDB and frameworks like RASDAMAN could offer different functionalities that facilitate working with larger volumes and more heterogeneous geospatial data sources.

The term Big Data has been recently used to define big, highly varied, complex data sets, which are created and updated at a high speed and require faster processing, namely, a reduced time to filter and analyse relevant data. These data is also increasingly becoming Open Data (data that can be freely distributed) made public by the government, agencies, private enterprises and among others. There are at least two issues that can obstruct the availability and use of Open Big Datasets: Firstly, the gathering and geoprocessing of these datasets are very computationally intensive; hence, it is necessary to integrate high-performance solutions, preferably internet based, to achieve the goals. Secondly, the problems of heterogeneity and inconsistency in geospatial data are well known and affect the data integration process, but is particularly problematic for Big Geo Data. Therefore, Big Geo Data integration will be one of the most challenging issues to solve. With these applications, we demonstrate that is possible to provide processed Big Geo Data to common users, using open geospatial standards and technologies. NoSQL databases like MongoDB and frameworks like RASDAMAN could offer different functionalities that facilitate working with larger volumes and more heterogeneous geospatial data sources.

The term Big Data has been recently used to define big, highly varied, complex data sets, which are created and updated at a high speed and require faster processing, namely, a reduced time to filter and analyse relevant data. These data is also increasingly becoming Open Data (data that can be freely distributed) made public by the government, agencies, private enterprises and among others. There are at least two issues that can obstruct the availability and use of Open Big Datasets: Firstly, the gathering and geoprocessing of these datasets are very computationally intensive; hence, it is necessary to integrate high-performance solutions, preferably internet based, to achieve the goals. Secondly, the problems of heterogeneity and inconsistency in geospatial data are well known and affect the data integration process, but is particularly problematic for Big Geo Data. Therefore, Big Geo Data integration will be one of the most challenging issues to solve. With these applications, we demonstrate that is possible to provide processed Big Geo Data to common users, using open geospatial standards and technologies. NoSQL databases like MongoDB and frameworks like RASDAMAN could offer different functionalities that facilitate working with larger volumes and more heterogeneous geospatial data sources.

This book discusses the latest developments in the field of open data. The opening of data by public organizations has the potential to improve the public sector, inspire business innovation, and establish transparency. With this potential comes unique challenges; these developments impact the operation of governments as well as their relationship with private sector enterprises and society. Changes at the technical, organizational, managerial, and political level are taking place, which, in turn, impact policy-making and traditional institutional structures. This book contributes to the systematic analysis and publication of cutting-edge methods, tools, and approaches for more efficient data sharing policies, practices, and further research. Topics discussed include an introduction to open data, the open data landscape, the open data life cycle, open data policies, organizational issues, interoperability, infrastructure, business models, open data portal evaluation, and research directions, best practices, and guidelines. Written to address different perspectives, this book will be of equal interest to students and researchers, ICT industry staff, practitioners, policy makers and public servants