Performance Enhancement for Geospatial Time-Series Data Prediction using Hash-Naive Bayes (HNB) Classification
Abstract: Due to the increasing demand for cloud services and the threat of privacy invasion, the user is suggested to encrypt the data before it is outsourced to the remote server. The safe storage and efficient retrieval of d-dimensional data on an untrusted server has therefore crucial importance. The paper proposed a new data distribution model which offers spatial order-preservation for d-dimensional data. In our Research Hybrid HASH based nave bayes classification system can be calculated using symmetric keys. We have similarly involved a specification of possessions of an distributed hash arrangement when using it to convinced use and matched our suggestion beside it. We have presented a security investigation of our method beside with recognised and enhance of the system using MATLAB 2014Ra.
Keywords: Distributed hash, Big data, geo spatial, nave HASH table, data mining
How to Cite?: Gyati Mittal, Mohini Mittal, "Performance Enhancement for Geospatial Time-Series Data Prediction using Hash-Naive Bayes (HNB) Classification", Volume 7 Issue 9, September 2018, International Journal of Science and Research (IJSR), Pages: 118-121, https://www.ijsr.net/getabstract.php?paperid=ART20191034, DOI: https://dx.doi.org/10.21275/ART20191034