Survey on Big Data Analytics in Public Sector of Russian Federation
Everyone is talking about big data, and how it will transform government. However, looking past the excitement, questions abound. How to use big data to make intelligent decisions? Perhaps most importantly, what value will it really deliver to the government and the citizenry it serves to? By reviewing the literature and summarizing insights from a series of business reports and interviews of public sector and top companies Chief Information Officers (CIOs), we offer a survey for both practitioners and researchers interested in understanding big data in the public sector of Russian Federation. Remarkable changes are taking place in IT industry of Russian Federation at present: new strategies of Federal Government, sanctions and import substitution tendency. The paper makes the estimate of internal and external factors, which effect on big data development in public sector of Russian Federation and makes comparative analysis of Russian and world practices of the study area.
> Georgia. Georgia's $16 bln economy saw strong annual growth in 2010-12 of around 6-7%, but in 2013 growth slowed to 3.2%, which is still good but not enough for an economy with a GDP per capita of around $3,600. Indeed, over the year, Georgia - which depends heavily on capital inflows - failed to utilize its competitive advantage of lower unit labor costs than in other countries in the region, such as Turkey and Bulgaria. > Turkey. The Turkish economy performed well in 1H14 as industrial output rose 3.8% y-o-y (down from 5.3% y-o-y in 5m14). GDP climbed 4.3% y-o-y in 1Q14, and we estimate 2Q14 to show GDP growth just below 4.0%. We expect 3.7% for 2014 as a whole, which is a bit stronger than we expected early in the year. > Bulgaria. Similar to some other smaller economies in the region, Bulgaria benefited from a recovery in the Eurozone that was characterized by ECB President Mario Draghi on August 7 as "moderate and uneven." Bulgarian GDP picked up to around 1.4% y-o-y in 1H14 (1.2% in 1Q14 and 1.6% in 2Q14). Given that Bulgaria's currency is pegged to the euro, the country was unable to extract benefits from this recovery to the same extent as some other countries, such as Turkey, Hungary or Romania, whose monetary policy and exchange rates are more independent. In 2H14, Bulgaria will face additional pressure from potentially slower growth in the EU as policy makers in the West and Russia continue experiments with sanctions.
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.
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.
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.