A New Method for Acquiring Relevant Data Partitioning by Optimization Techniques

Over the past several decades there is an exceptionally large improvement in the computer technology which leads to an uncountable number of data and information emerging in and all over the world. Due to this tremendous and huge dump of data as well as web data most popular search engines are experiencing a lot of irrelevant retrieval of data. The major aspire of this proposed Improved Weis to identify an accurate data search and also to generate data that comes from anywhere. Furthermore, the data itself may be too large to store on a single machine such that the computers are inter connected with each other by the massive internet storage technologies. This approach mainly focuses on design of search engines and its infrastructure grave. Improved Micro partitioning is a modularized approach of cloud computing mainly framed to overcome the pitfalls in the traditional search engine and also in manipulation of large information stored in a single computer. The Map Reduce Task Scheduling algorithm which has been used in the cloud helps in overcoming the challenges of conventional methodologies. The map reduce protocol model is a simple model that makes the data to save in different locations by partitioning the data technique. Additionally in order to avoid the uneven distribution of data the data sampling technique is used. Henceforth, the Search engine in cloud produces low-latency and the data materialization will increase the efficiency in its optimized search and thus outperforms the traditional approaches.