ISSN 1995-0802, Lobachevskii Journal of Mathematics, 2020, Vol. 41, No. 12, pp. 2535–2541. c  Pleiades Publishing, Ltd., 2020. Additive Criteria to Evaluate Relevance of Innovative Objects in Data Warehouse V. K. Ivanov 1* , B. V. Palyukh 1** , and A. N. Sotnikov 2*** (Submitted by A. M. Elizarov) 1 Tver State Technical University, 22, Quay A. Nikitin, Tver, 170026, Russia 2 Joint Supercomputer Centre of RAS, 32a, Leninskiy Av., Moscow, 119991, Russia Received May 19, 2020; revised May 30, 2020; accepted June 06, 2020 Abstract—The article discusses some aspects of warehousing object descriptions having significant innovation potential. The procedure for selecting such descriptions consists of two consecutive phases. The first phase involves generating effective search queries with a special genetic algorithm (GAP). In the second phase, the model developed determines the index of innovativeness of an object archetype. Meanwhile the values of additive selection criteria are calculated. In the former case, the criterion is a fitness function of GAP. In the latter case, the criterion is the index of innovativeness. The purpose of the article is to justify the additive criterion applicability for calculating the value of the GAP fitness function. The article describes general conditions of applying additive evaluation criteria and shows how these conditions are met for the GAP fitness function. The analysis of the partial criteria gives grounds to assert their additive independence and, therefore, the correct use of additive n-dimensional utility function. Some additional reasons for applying additive criterion are also given. In general, the article proposes a unified approach to generating global assessment criteria and the relevance of unified formal structure is shown. The models presented in the article are used to develop adequate computational algorithms for the developed data warehouse support system. DOI: 10.1134/S199508022012015X Keywords and phrases: data warehouse, innovation, genetic algorithm, additive criterion, utility function, additive independence, partial criterion, search query 1. INTRODUCTION In the framework of the project “Data storage based on search agent intellectualization and an evolu- tionary model for selecting target information” (https://www.rfbr.ru/rffi/ru/project_search/o_2071601) a technology for describing warehouses for objects with significant innovative potential is proposed. The technology involves a two-phase procedure for selecting object or document descriptions to be stored in a warehouse and then index them. The first phase involves generating a stable and efficient query population in a search engine with a special genetic algorithm (hereinafter referred to as GAP) in order to obtain highly relevant results. This evolutionary process results in accumulating object descriptions as candidates to be stored in the data warehouse and forming a linguistic model that describes the object archetype. In the second phase, the developed model is used to determine the index of innovativeness of the object archetype. In both phases, the values of additive selection criteria are calculated. In the first phase, this criterion is a GAP fitness function whose maximand depends on several factors discussed below. The purpose of the article is to substantiate additive criterion applicability at the stage of selecting object descriptions to be stored and forming an object archetype. The article describes general conditions * E-mail: mtivk@tstu.tver.ru ** E-mail: pboris@tstu.tver.ru *** E-mail: asotnikov@jscc.ru 2535