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Онлайн-исследования в России: тенденции и перспективы
The key trends and publications in social sciences are reviewed in the article in order to reveal and present in a systematic way the changes in dominant research topics and methods of Internet research taking place since early 2000s as well as concomitant modifications in types of available data and in our understanding of the nature of Internet communications. Particular attention is paid to leading methodological innovations and to possibilities and challenges which arise as a leading function of online research shifts from an auxiliary “mirror” of conventional lab experiments, surveys and participant observations in a prevalent mode of research of global effects of micro-interactions and source of new “social phenomenology” for many research fields, including large scale experiments on the topics of social influence and cultural diffusion, political mobilization, etc.
The article discusses the prospects of qualitative sociological research online, with the main focus on the survey, observation and analysis of documents. Online conditions require not only the use of innovative technological developments for contact in the field, but has earned the recognition and adaptation of offline methods in the interaction between researchers and respondents.The author believes that there is no single solution for or against online qualitative methods. Rather, it is a methodologically diversified access, which combines Internet-based approaches, depending on the current task with the help of proven methods in online research.
The possibility to use respondent-driven sampling to conduct online surveys was introduced to the scientific community seven years ago. Since that time, foreign and domestic researchers conducted a series of experiments with WebRDS. However, their results have been ambiguous. On the one hand, some authors see WebRDS as a convenient and promising research technology. On the other hand, some researchers bring attention to the difficulty of applying respondent-driven sampling and unsuccessful attempts to obtain unbiased estimates of the target population parameters. The contradiction is further escalated by broader discussion of the possibility to follow basic RDS assumptions. Therefore, this paper analyzes implementation of respondent-driven sampling in online research and summarizes results of recent methodological discussion on the development of RDS. In conclusion, the paper provides recommendations on conducting methodological studies of WebRDS
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.
The proceedings of the 11th International Conference on Service-Oriented Computing (ICSOC 2013), held in Berlin, Germany, December 2–5, 2013, contain high-quality research papers that represent the latest results, ideas, and positions in the field of service-oriented computing. Since the first meeting more than ten years ago, ICSOC has grown to become the premier international forum for academics, industry researchers, and practitioners to share, report, and discuss their ground-breaking work. ICSOC 2013 continued along this tradition, in particular focusing on emerging trends at the intersection between service-oriented, cloud computing, and big data.
Operational processes leave trails in the information systems supporting them. Such event data are the starting point for process mining – an emerging scientific discipline relating modeled and observed behavior. The relevance of process mining is increasing as more and more event data become available. The increasing volume of such data (“Big Data”) provides both opportunities and challenges for process mining. In this paper we focus on two particular types of process mining: process discovery (learning a process model from example behavior recorded in an event log) and conformance checking (diagnosing and quantifying discrepancies between observed behavior and modeled behavior). These tasks become challenging when there are hundreds or even thousands of different activities and millions of cases. Typically, process mining algorithms are linear in the number of cases and exponential in the number of different activities. This paper proposes a very general divide-and-conquer approach that decomposes the event log based on a partitioning of activities. Unlike existing approaches, this paper does not assume a particular process representation (e.g., Petri nets or BPMN) and allows for various decomposition strategies (e.g., SESE- or passage-based decomposition). Moreover, the generic divide-and-conquer approach reveals the core requirements for decomposing process discovery and conformance checking problems.
Full texts of third international conference on data analytics are presented.
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.
Several approaches to the concept of fatherhood present in Western sociological tradition are analyzed and compared: biological determinism, social constructivism and biosocial theory. The problematics of fatherhood and men’s parental practices is marginalized in modern Russian social research devoted to family and this fact makes the traditional inequality in family relations, when the father’s role is considered secondary compared to that of mother, even stronger. However, in Western critical men’s studies several stages can be outlined: the development of “sex roles” paradigm (biological determinism), the emergence of the hegemonic masculinity concept, inter-disciplinary stage (biosocial theory). According to the approach of biological determinism, the role of a father is that of the patriarch, he continues the family line and serves as a model for his ascendants. Social constructivism looks into man’s functions in the family from the point of view of masculine pressure and establishing hegemony over a woman and children. Biosocial theory aims to unite the biological determinacy of fatherhood with social, cultural and personal context. It is shown that these approaches are directly connected with the level of the society development, marriage and family perceptions, the level of egality of gender order.
This article is talking about state management and cultural policy, their nature and content in term of the new tendency - development of postindustrial society. It mentioned here, that at the moment cultural policy is the base of regional political activity and that regions can get strong competitive advantage if they are able to implement cultural policy successfully. All these trends can produce elements of new economic development.