Analysing Computational Complexity For Prediction Function In Health Record Dataset

Nowadays, XML database growth plays a vital role in many real time applications. XML database contains a collection of XML dataset. More analytical functions are applied to XML database by using Xquery. In real world, huge businesses are exchanging the data as XML data model. In general, space and time parameters are considered for Xquery processing in the database. In existing, the analytical operation is analyzed in eXist-DB and BaseX databases with the execution time of ORBDA dataset. In existing system, the prediction analysis operation is not supposed in the dataset. In this paper, Xquery is processed by using Riak database. Riak database produces better execution time than eXist-DB and BaseX. This research has analyzed the prediction operation for ORBDA dataset using machine learning approach. This paper uses various regression techniques to analyze the prediction operation. Machine learning approaches produce better accuracy in prediction. The query processing time is reduced than the existing approach. This research uses ORBDA dataset in demonstration.