Взаимодействие технологий Internet of things и Big data
This research provides the strategy of using two modern directions such as Big Data and Internet of Things and their various opportunities. There are the overview and analysis of tools which helps to work in this area: cloud services for data’s storage and its monitoring. The new using option, linking technologies Big Data and Internet of Things, is represented in this work.
In this work it was explained why the energy balancing in a static wireless sensor networks with autonomous energy sources is actual nowadays. We presented mathematical model of static wireless sensor network which considers external influence. We presented method of energy balancing in a static wireless sensor networks with autonomous energy sources and experimental results which show increasing work efficiency of static WSN with autonomous energy sources by increasing nodes lifetime and decreasing nodes energy consumption.
This book constitutes the proceedings of the 9th International Workshop on Enterprise and Organizational Modeling and Simulation, EOMAS 2013, held in conjunction with CAiSE 2013 in Valencia, Spain, in June 2013.
Tools and methods for modeling and simulation are widely used in enterprise engineering, organizational studies, and business process management. In monitoring and evaluating business processes and the interactions of actors in a realistic environment, modeling and simulation have proven to be both powerful, efficient, and economic, especially if complemented by animation and gaming elements.
The ten contributions in this volume were carefully reviewed and selected from 22 submissions. They explore the above topics, address the underlying challenges, find and improve solutions, and show the application of modeling and simulation in the domains of enterprises, their organizations and underlying business processes.
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 book constitutes the refereed proceedings of the 5th International Workshop on Multiple Access Communications, MACOM 2012, held in Maynooth, Ireland, in November 2012. The 13 full papers and 5 demo and poster papers presented were carefully reviewed and selected from various submissions. The papers are organized in topical sections on network coding, handling interference and localization techniques at PHY/MAC layers, wireless access networks, and medium access control.
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.