Evaluation of Parallel Indexing Scheme for Big Data

This paper evaluates the parallel indexing scheme proposed for efficient processing of big data. In the proposed scheme, three kinds of computing nodes are introduced. These are reception-nodes, representative-nodes, and normal-nodes. A reception-node receives data for insertion. A representative-node receives queries. Normal-nodes retrieve data from indexes. Three kinds of indexes are also introduced. These are a whole-index, a partial-index, and a reception-index. In a partial-index, data are stored. In a whole-index, partial-indexes are stored as its data. In a reception-index, additional data are stored. The reception-index is moved to a normal-node, and becomes a partial-index. The proposed scheme is also a data distribution scheme for shortening the insertion time. It is experimentally clarified that data can be inserted into nodes with little time overhead. It is also clarified that retrieval time decreases according to the number of normal-nodes. It is shown that the overlap distribution scheme is considered to be better than the area expansion and the proximity ones.