Большие данные в измерениях аудитории цифровых медиа: новые возможности для исследований и необходимость создания единого измерителя
The aim of the study was to determine the media experts’ attitude towards the single measurement system creation and data alliance formation, and towards the development of the common measurement methods for digital media audience analysis using the Big Data technologies. As part of the study, 10 semi-structured expert interviews were conducted with specialists who works with audience data or engaged in the data collection. In particular, the sample included representatives of such companies as Mail.ru, Russia Today, Transparency International-R and others.
This work helped to reveal the contradiction between the interest in standardizing audience currencies for data exchange and a number of serious external factors affecting the impossibility of exchange implementation at this stage of market development. One of the main barriers to creating a single measurement system and sharing information is the financial disinterest of major players and concerns about competitive advantage. A separate vector for discussion is the issue of ethics regarding the collection of user data. Expectations from the Big Data technologies are controversial: some experts believe that the introduction of new measurement methods will greatly simplify the company’s operations, while others state that these technologies are overestimated and will not significantly affect audience measurement methods.
This book provides a comprehensive analysis of the ways in which new media technologies have shaped language and communication in contemporary Russia. It traces the development of the Russian-language internet (Runet) from late-Soviet cybernetics to the advent of Twitter and explores the evolution of web-based communication practices, showing how they have both shaped and been shaped by social, political, linguistic and literary realities. Throughout the volume, leading Runet scholars draw attention to features and trends that are characteristic of global new media, as well as those that are more specific to Russian media culture.
Pattern structures, an extension of FCA to data with complex descriptions, propose an alternative to conceptual scaling (binarization) by giving direct way to knowledge discovery in complex data such as logical formulas, graphs, strings, tuples of numerical intervals, etc. Whereas the approach to classification with pattern structures based on preceding generation of classifiers can lead to double exponent complexity, the combination of lazy evaluation with projection approximations of initial data, randomization and parallelization, results in reduction of algorithmic complexity to low degree polynomial, and thus is feasible for big data.
This is a compilation of all presentations given at the 14th Central Asia Media Confirence, organized by the Representativ’s office, which brought together international and local experts from five Central Asia participating States of the OBSE. This publication is designed to serve as a record of the events of that conference and is intended for journalists, government and regulatory officials and students.
The practical relevance of process mining is increasing as more and more event data become available. Process mining techniques aim to discover, monitor and improve real processes by extracting knowledge from event logs. The two most prominent process mining tasks are: (i) process discovery: learning a process model from example behavior recorded in an event log, and (ii) conformance checking: diagnosing and quantifying discrepancies between observed behavior and modeled behavior. The increasing volume of event data provides both opportunities and challenges for process mining. Existing process mining techniques have problems dealing with large event logs referring to many different activities. Therefore, we propose a generic approach to decompose process mining problems. The decomposition approach is generic and can be combined with different existing process discovery and conformance checking techniques. It is possible to split computationally challenging process mining problems into many smaller problems that can be analyzed easily and whose results can be combined into solutions for the original problems.
In 2015-2016 the Department of Communication, Media and Design of the National Research University “Higher School of Economics” in collaboration with non-profit organization ROCIT conducted research aimed to construct the Index of Digital Literacy in Russian Regions. This research was the priority and remain unmatched for the momentIn 2015-2016 the Department of Communication, Media and Design of the National Research University “Higher School of Economics” in collaboration with non-profit organization ROCIT conducted research aimed to construct the Index of Digital Literacy in Russian Regions. This research was the priority and remain unmatched for the moment
Companies are increasingly paying close attention to the IP portfolio, which is a key competitive advantage, so patents and patent applications, as well as analysis and identification of future trends, become one of the important and strategic components of a business strategy. We argue that the problems of identifying and predicting trends or entities, as well as the search for technical features, can be solved with the help of easily accessible Big Data technologies, machine learning and predictive analytics, thereby offering an effective plan for development and progress. The purpose of this study is twofold, the first is an identification of technological trends, the second is an identification of application areas and/or that are most promising in terms of technology development and investment. The research was based on methods of clustering, processing of large text files and search queries in patent databases. The suggested approach is considered on the basis of experimental data in the field of moving connected UAVs and passive acoustic ecology control.
The article is dedicated to the analysis of Big Data perspective in jurisprudence. It is proved that Big Data have to be used as the explanatory and predictable tool. The author describes issues concerning Big Data application in legal research. The problems are technical (data access, technical imperfections, data verification) and informative (interpretation of data and correlations). It is concluded that there is the necessity to enhance Big Data investigations taking into account the abovementioned limits.
The article is devoted to the problem of communicative features of the constructive structure of the font identity in the city branding sphere. This problem is considered in the framework of the nonlinearity of visual communication based on typology, comparative and structural analysis of the font identity of the world's cities. The article analyzes the brand identity of the city of Murmansk (2015) with the use of qualitative research methods: an expert interview with the designer of Murmansk identity.
This paper explores, mainly from a legal perspective, the extent to which the Russian regulations of traditional TV and online audiovisual media policies have been consistent with the Council of Europe (hereinafter CoE) standards. The study compares between the CoE and Russian approaches to specific aspects of audiovisual regulation including licensing, media ownership, public service media, digitalization, and national production. The paper first studies the CoE perspective through examining its conventional provisions related to audiovisual media, the case law of the European Court of Human Rights as well as the CoE non-binding documents. The paper then considers Russian national legislation governing audiovisual media and the Russian general jurisdiction courts’ practice on broadcast licensing. The paper suggests that the Russian audiovisual regulations are insufficiently compatible with the CoE standards and more in line with the Soviet regulatory traditions.
Systems Thinking in Museums explores systems thinking and the practical implication of it using real-life museum examples to illuminate various entry points and stages of implementation and their challenges and opportunities. Its premise is that museums can be better off when they operate as open, dynamic, and learning systems as a whole as opposed to closed, stagnant, and status quo systems that are compartmentalized and hierarchical. This book also suggests ways to incorporate systems thinking based on reflective questions and steps with hopes to encourage museum professionals to employ systems thinking in their own museum. Few books explore theory in practice in meaningful and applicable ways; this book offers to unravel complex theories as applied in everyday practice through examples from national and international museums.