Big Data leads to new international data processing policies
Response to Peter Schaar (Chairman of the European Academy For Freedom of Information and Data Protection, former German Data Commissioner) about the incompatibility of the Internet and Big Data with Data protection. It declares that technological development has overtaken the policy-making process and applications according to web 3.0 are likely to be far more effective at piecing together personal data than even traditional search engines.
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.
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 volume contains a set of dedicated scientific contributions to the 11th International Conference on Perspectives in Business Informatics Research. The peer-reviewed and tentatively selected papers cover a broad scope of modern research in Business Informatics, and include new results in such domains as: Knowledge Management and Semantic Web, Business and information systems development, Business, people and systems interoperability and Business intelligence.
In 2012 the conference is hosted by National Research University Higher School of Economics (NRU HSE) in Nizhny Novgorod. Our university is Russia’s leader in the field of scientific research conducted at the junction of Management, Economics and Governance of IT. In particular, NRU HSE is the originator and the promoter of Business Informatics in Russia. Therefore NRU HSE pays particular attention to sustainable international cooperation and leverages scientific research in that area.
We strongly believe that materials presented will contribute to further advances in Business Informatics and will foster intensive scientific cooperation between researchers.
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.
This compendium comprises transcript of the workshop on ‘Human Rights on the Internet: legal frames and technological implications’ organized by the Higher School of Economics on the 7th Internet Governance Forum (Baku, Azerbaijan, 6–9 November, 2012) and relevant articles on legal and technological issues of Internet Governance in sphere of human rights, prepared by the group of legal and technical scholars of information studies of the Higher School of Economics. This compendium is devoted to the forthcoming 8th Meeting of the Internet Governance Forum on Bali, Indonesia, 22–25 October 2013.
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.