METHOD OF ADAPTIVE BLOCK FORMATION AND INDEXING OF UNSTRUCTURED DATA IN DECENTRALIZED STORAGE SYSTEMS
The paper addresses the problem of processing and indexing unstructured textual data in decentralized storage systems. An analysis of existing approaches based on static block formation parameters is conducted, revealing their limitations related to the lack of consideration of dynamic characteristics of distributed environments, which leads to increased network overhead and reduced search efficiency.
An adaptive method for block formation and data indexing is proposed, taking into account input data rate, network load, and the number of active nodes. The system architecture is described, including modules for data preprocessing, aggregation, indexing, and distributed storage based on distributed hash tables. A mathematical model and an algorithm are developed to provide dynamic control of block formation parameters.
A theoretical analysis of the proposed algorithm is performed, demonstrating the influence of key system parameters on block formation. A comparison with a fixed block formation method is presented, showing the advantages of the adaptive approach in terms of reduced network costs, improved scalability, and robustness under varying load conditions.
The proposed method can be applied in the design of distributed storage systems, decentralized search platforms, and stream processing systems. The results provide a foundation for further research on adaptive and hybrid methods for processing unstructured data in decentralized environments.
Voskoboinikov I.S., Gvozdevsky I.N., Bulgakov V.D. Method of Adaptive Block Formation and Indexing of Unstructured Data in Decentralized Storage Systems // Research result. Information technologies. – T.11, №2, 2026. – P. 121-134. DOI: 10.18413/2518-1092-2026-11-2-0-10
While nobody left any comments to this publication.
You can be first.