2018 IEEE 20th Conference on Business Informatics (CBI)
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 present paper considers the questions of modeling and analysis of dynamical features of social networks taking into account evolutionary changes. A two level approach to monitoring of social network dynamics and evolution is described, including the functionality of both levels. Possible practical interpretations and correlations with the customer-centric concept are mentioned.
Any marketing activity requires ways to assess its positive or negative impact. Such a measurement is not always easy to implement, especially when studying the behavior of consumers in offline space, for example, in a retail shop. In this paper, we present a case study of the behavior of consumers in the shop from the FMCG area. The procedure for collecting and analyzing data is described, and proposals for increasing the Cross Penetration Index from one store zone to another are formed and tested. Special user behavior data collection system called "Locastor" was used to collect MAC-addresses of smartphones of the visitors for further analysis.