Optimization of metadata volume in modern information systems: methods, tools and algorithmic approaches

Abstract

B a c k g r o u n d. The study of metadata volume optimization aims to achieve a balance between the sufficiency of description and system efficiency. Excessive metadata can overload systems, and insufficient metadata can complicate data access. At the same time, the growing volume of data complicates work with metadata, since its creation, storage and processing require significant resources. Metadata volume optimization has become an important task for organizations seeking to achieve effective information management. Challenges associated with metadata volume: redundancy, insufficiency, duplication, data dynamics, non-compliance with standards.

M e t h o d s. The paper considers the theoretical foundations, practical methods, tools (Collibra, Apache Atlas, Talend Metadata Manager, AI algorithms) and the benefits of metadata volume optimization (cost reduction, improved productivity, improved data quality, flexibility and scalability, improved analytics).

R e s u l t s. The paper proposes a comprehensive metadata optimization strategy adapted for the IT environment. It is shown that the use of a systematic approach, including analysis, standardization, automation and integration of the latest technologies, allows to significantly reduce costs and improve data management. An algorithm for optimizing the volume of metadata is presented, which can be adapted for various application areas, such as databases, content management systems or big data. The proposed algorithm takes into account the assessment of metadata usefulness using metric normalization to unify evaluation scales and determine the usefulness of each metadata element; metadata selection (filtering, clustering); metadata compression; automatic optimization using machine learning models and dynamic tuning; verification and adaptation. The algorithm can be expanded or modified depending on the specifics of the task.

C o n c l u s i o n s. The proposed algorithm takes into account: metadata usefulness assessment using metric normalization to unify evaluation scales and determine the usefulness of each metadata element; metadata selection (filtering, clustering); metadata compression; automatic optimization using machine learning models and dynamic tuning; testing and adaptation. The algorithm can be expanded or modified depending on the specifics of the task.