Method of combined indexing for effective search in multi-attribute catalogs of technical components

Abstract

Optimizing search in multidimensional catalogs of radio-electronic components (sensors, microcontrollers, communication modules) is a crucial task for computer-aided design systems, electronic component logistics management, and intelligent technical support systems. The complexity arises due to the high dimensionality of the parameter space (operating frequencies, power consumption, temperature ranges, etc.), data heterogeneity, and the high frequency of complex queries combining numerical constraints with categorical filters. Classic indexing algorithms from relational database management systems are inefficient for this specific domain, which slows down the performance of real-time information systems. Modern indexing technologies for searching multi-attribute catalogs of technical components are moving away from the paradigm of universal one-dimensional structures in favor of specialized and hybrid approaches tailored to the nature of technical data. The focus has shifted from fast single-key search to efficient pruning of multidimensional parameter space. To achieve this, spatial indexes are actively used, interpreting each component as an object in an N-dimensional space, where each technical parameter is a separate axis. This allows a single query to the index to find all entries that fall within a specified multidimensional hyperrectangle. Simultaneously, technologies that treat search as an information retrieval problem are evolving. Categorical attributes, such as interface type or manufacturer, are indexed using inverted indexes or compressed bitmap indexes, which provide ultra-fast execution of AND/OR operations over large sets. A separate direction involves the use of vector representations (embeddings) of technical characteristics, obtained using machine learning models, followed by indexing using specialized structures for nearest neighbor search. This enables semantic search by technical description or finding analogous components. A key issue in indexing for search within multi-attribute catalogs of technical components is the selection and combination of data structures that effectively prune the search space across all relevant dimensions simultaneously, minimizing the retrieval of irrelevant data in the early stages of query execution. Therefore, the development of a new method that systematically combines the strengths of modern approaches within a unified adaptive architecture is a relevant scientific and technical problem. Its solution will significantly reduce the execution time of complex queries in key information systems for the fields of radio electronics, telecommunications, and automated engineering, addressing the challenges of production digitalization and intelligent data processing. Objective. Development of a combined indexing method for efficient execution of complex search queries in multi-dimensional catalogs of technical components. Method. A combined indexing method is proposed, which combines R-tree for multidimensional filtering of numerical parameters and inverted indexes for categorical features. The method for improving search efficiency is based on an adaptive query planner that dynamically chooses the optimal execution strategy (Index-First, Parallel-Merge or Full-Scan) based on an assessment of the selectivity of the conditions. Results. The problem was formulated, and a combined indexing method was developed for multidimensional catalogs of radioelectronic components. In the course of the study, a formalized mathematical search model was created that takes into account the selectivity of numerical and categorical conditions, and algorithms for automatic distribution of attributes between index types and dynamic selection of the query execution strategy were developed. A hybrid index architecture was proposed that combines an R-tree for indexing numerical parameters and inverted indexes for categorical features, as well as mathematical models for assessing selectivity and optimizing the query execution plan. Experimental studies on a synthetic data set confirmed the effectiveness of the developed method, demonstrating a reduction in the execution time of complex queries compared to basic indexing based on B-trees. Conclusions. The work develops a combined indexing method for efficient execution of complex multi-attribute queries in radioelectronic component catalogs. The method is based on a formalized mathematical search model, which allowed building a hybrid index architecture. This architecture integrates an R-tree for filtering numerical parameters, inverted indexes for categorical features, and an adaptive planner that dynamically selects the optimal query execution strategy (Index-First, Parallel-Merge, Full Scan) based on an assessment of the selectivity of conditions. An algorithm for the automatic distribution of attributes between index types is developed. Experimental modeling confirmed the effectiveness of the method, showing a 35–55% reduction in the execution time of complex queries compared to traditional B-tree-based indexing, especially for queries typical of engineering component selection. The results obtained prove the feasibility of using combined indexing to increase the productivity of information systems working with multidimensional technical catalogs.