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Creating an innovation environment is shown in the context of interaction of economic agents in the creation and consumption of innovative value-based infrastructure approach. The problem of the complexity of collecting heterogeneous data on the formation and distribution of innovative value in the conditions of the dynamic nature of the research object and the environment is formulated. An information model providing a subject-independent representation of data on innovation value flows is proposed and allows to automate the processes of data collection and analysis with the minimization of time costs. The article was prepared in the course of carrying out research work within the framework of the project part of the state task in the field of scientific activity in accordance with the assignment 26.2758.2017 / 4.6 for 2017-2019. on the topic “System for analyzing the formation and distribution of the value of innovative products based on the infrastructure concept”.

In our increasingly globalised world, the study of impediments to international trade is of interest to the field of international economics. This paper focuses on the particular problem of speedy and accurate processing of customs declarations. We present a novel use of graph based spreading activation algorithm for the automated processing of customer declarations for commercial goods, based on supervised learning. This method allows us to build recommender systems for use by customs officers, traders, carriers and insurers. We examine the particular use case of the recommendation to assign or not assign an armed escort to a shipping vehicle in cases of elevated risk of theft. In contrast to the usual risk based approach, this algorithm is trained solely on shipment data rather than on traditional risk indicators. This is useful as the recommendation to customs officials can be explained in terms of the make-up of a shipment and can be verified in real-time. The feasibility of the approach was tested by application to 2500 custom records collected during a continuous period of one month at eight border checkpoints between Russian Federation and two EU countries. The algorithm achieved 100 % accuracy under experimental conditions.