Query Optimization in a Blockchain-Based Land Registry Management System
Bibliographic record
Abstract
Digitization of land records is not sufficient for preventing fraud cases, time delay, and brokers' involvement. Distributed Ledger Technology (DLT) is used for making this digitized record more secure and process it in a decentralized way, and reduces paperwork in selling and buying of land. Blockchain technology has come to the fore in recent years and is the center of technical discussion, with intuitive applications driven by its network architecture. It has been firmly established as one of the most important emerging technologies. This article aims to implement a land registry mechanism using blockchain technology and optimize searching of land records in blockchain. Interplanetary File System (IPFS) provides an infrastructure that offers a precise portrayal of all the members' roles. The application interacts with the blockchain network, which is built using IPFS. This paper will help in providing a secure and decentralized system for the land registry process. The proposed consensus algorithm MRRCM achieves less time required to generated a block on an average by 3.06% round-robin and 96.48% PoW approach. The proposed modified hash table search approach requires less time to search the blockchain's land record block than the extensive liner searches and hash table search approach. A search for a land record in the blockchain reduces the search time on an average by 59.5% compared to the traditional extensive liner search approach and by 18.68% as compared with the hash table search approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".