Map Ranking, Map-Reduce and Application in Big Data Analysis
AbstractThe map method works with a certain algorithm and the inputs which send to a value list as a parameter. All values in the list converted to the intermediate result list. We sort all of the data then obtain the map list. The proposed and developed map structure was tested with quicksort approach. The sorting process depends on the byte situation of the each data. Small data can do it easily side by side. Thus, small data do not need to applying of the reduce process. Sample selection havbe to will be easier. The goal is to give an intermediate operationtothe map-reduce structure. The more accurate is to get a ranking. In large data analysis, the data mapping sequence and reduction works with a certain algorithm structure, introducing and sending inputs as a parameter to a value list. An intermediate result list is created by converting all the values in the list, which are included in the entered system. In the structure developed after the mapping (Map) process, the mapping list is divided and obtained. The order depends on the byte value that is generated by each data. In case of large volume data, the data will be used without using a single line operation. In short, the data may be side-by-side, so there is no need to apply the reduction to each data. Therefore, the process of selecting the sample will be easier.