Big data query optimization by using Locality Sensitive Bloom Filter
Mayank Bhushan, Monica Singh, Sumit Kumar Yadav · International Conference on Computing for Sustainable Global Development · 2015
For faster access of data or in network bloom filter plays an important part in searching technique. It process data in short amount of time and frequently with probabilistic analysis. Bloom Filter also decreases the cost of analyzing data. Various applications are using this technology for accessing and processing the data. Thus by implementing Bloom's Filter over big data will result into efficient query accessing in big data. In this paper, an approach to implement Locality Sensitive Bloom Filter (LSBF) technique in big data is proposed. To remove the drawbacks of simple hashing technique, the LSBF must be implemented to store data in the bloom filter which will help to search the most approximate result by using the Locality Sensitive Hashing approach.