Blockchain Retrieval Model Based on Elastic Bloom Filter
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 11817 LNCS, Page: 527-538
2019
- 3Citations
- 14Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Blockchain as emerging technology is revolutionizing several industries, especially the education industry, which has high requirements for the authenticity of data. The proposed blockchain technology realizes decentralization and time-sequence chain storage of data blocks, ensuring that the stored data blocks are not tamperable and unforgeable, and satisfy the high trust of data authenticity. However, current League Chains (such as Hyperledger Fabric) generally have problems such as low throughput and lack of indexing technology, which leads to inefficient data retrieval problems. To this end, this paper proposes a new elastic Bloom filter model that combines smart contracts. This model provides an adaptive adjustment method for Bloom filters, it can effectively reduce the false positive probability under the condition of low memory consumption and improve the efficiency of data retrieval. The experimental results based on Hyperledger Fabric show that compared with the standard Bloom filter model, the proposed model guarantees a lower false positive probability and verifies its high efficiency under data retrieval.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85075601027&origin=inward; http://dx.doi.org/10.1007/978-3-030-30952-7_53; https://link.springer.com/10.1007/978-3-030-30952-7_53; https://dx.doi.org/10.1007/978-3-030-30952-7_53; https://link.springer.com/chapter/10.1007/978-3-030-30952-7_53
Springer Science and Business Media LLC
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