The Issue of De-Anonymization of the Personality in Today's "Media" Society

В статье рассматриваются причины и методы деанонимизации личных данных граждан, объясняются позиции сторонников и противников данного явления. Мы попытаемся ответить на вопрос: благо или зло борьба с анонимностью, а также рассмотрим, как она протекает в наши дни и с какими трудностями приходится сталкиваться. Помимо этого, присутствуют размышления о человеческом поведении в деанонимизированном пространстве.In the article we observe the causes and methods of civil citizens data’s De-anonymization from the allies and opponents of the theory. We try to debunk the theory and find out whether its impact is negative or positive, the process of De-anonymization and which difficulties you can face. Despite of that, we also apply the idea of people’s behavior in such society under De-anonymization

Towards certain fixes with editing rules and master data

A variety of integrity constraints have been studied for data cleaning. While these constraints can detect the presence of errors, they fall short of guiding us to correct the errors. Indeed, data repairing based on these constraints may notfind certain fixes that are absolutely correct, and worse, may introduce new errors when repairing the data. We propose a method for finding certain fixes, based on master data, a notion of certain regions, and a class of editing rules. A certain region is a set of attributes that are assured correct by the users. Given a certain region and master data, editing rules tell us what attributes to fix and how to update them. We show how the method can be used in data monitoring and enrichment. We develop techniques for reasoning about editing rules, to decide whether they lead to a unique fix and whether they are able to fix all the attributes in a tuple, relative to master data and a certain region. We also provide an algorithm to identify minimal certain regions, such that a certain fix is warranted by editing rules and master data as long as one of the regions is correct. We experimentally verify the effectiveness and scalability of the algorithm

Leveraging Big Data Analytics to Improve Quality of Care in Healthcare Organizations:A Configurational Perspective

Big data analytics (BDA) is beneficial for organizations, yet implementing BDA to leverage profitability is fundamental challenge confronting practitioners. Although prior research has explored the impact that BDA has on business growth, there is a lack of research that explains the full complexity of BDA implementations. Examination of how and under what conditions BDA achieves organizational performance from a holistic perspective is absent from the existing literature. Extending the theoretical perspective from the traditional views (e.g. resource-based theory) to configuration theory, the authors have developed a conceptual model of BDA success that aims to investigate how BDA capabilities interact with complementary organizational resources and organizational capabilities in multiple configuration solutions leading to higher quality of care in healthcare organizations. To test this model, the authors use fuzzy-set qualitative comparative analysis to analyse multi-source data acquired from a survey and databases maintained by the Centres for Medicare & Medicaid Services. The findings suggest that BDA, when given alone, is not sufficient in achieving the outcome, but is a synergy effect in which BDA capabilities and analytical personnel's skills together with organizational resources and capabilities as supportive role can improve average excess readmission rates and patient satisfaction in healthcare organizations

Social Rating System of the People’s Republic of China in the Cultural Aspect of the State and Society Relations

The article discusses the elements of China’s political culture and their influence on the construction of a social rating system in the PRC and the attitude of the Chinese people towards it.В настоящей статье рассматриваются элементы политической культуры Китая, их влияние на построение системы социального рейтинга в КНР и отношение к ней китайского народа

Explaining Query Answer Completeness and Correctness with Partition Patterns

International audienceInformation incompleteness is a major data quality issue which is amplified by the increasing amount of data collected from unreliable sources. Assessing the completeness of data is crucial for determining the quality of the data itself, but also for verifying the validity of query answers over incomplete data. In this article, we tackle the issue of efficiently describing and inferring knowledge about data completeness w.r.t. to a complete reference data set and study the use of a partition pattern algebra for summarizing the completeness and validity of query answers. We describe an implementation and experiments with a real-world dataset to validate the effectiveness and the efficiency of our approach