Building Quotient Cube with MapReduce in Hadoop

In order to solve the problem that how to improve the efficiency of query and calculation in massive data, a method of building quotient cubes in Hadoop plateform which combined the advantage of the quotient cube and MapReduce model is proposed in this paper. At first, all cubes will be established and their aggregate value will be calculated in the Mapping stage. All the key/value pair formed in Mapping stage will be passed to Reducing stage. Equivalence partitioning will be carried out In this stage, and the minimum aggregation cube of each equivalence partitioning will be the key with its aggregate value. According to the minimum aggregation cubes, we can get the quotient cubes. In order to improve the speed of parallel computing and reduce network traffic, equivalence class division will be executed locally after the Map stage, it is named as combiner stage. In this paper, MapReduce model is used to improve the efficiency of building quotient cube because of its ability of parallel computing in a large amount of data. In addition, the experiment proved that, under certain circumstances, increasing the number of Mapper/Reducer task can reduce the building time effectively, and improve the construction efficiency.

Summarizing Datacubes

Datacubes are especially useful for answering efficiently queries on data warehouses. Nevertheless the amount of generated aggregated data is huge with respect to the initial data which is itself very large. Recent research work has addressed the issue of summarizing Datacubes in order to reduce their size. In this chapter, we present three different approaches. They propose structures which make it possible to reduce the size of the data cube representation. The two former, the closed cube and the quotient cube, are said semantic and discard the redundancies captured within data cubes. The size of the underlying representations is especially reduced but the counterpart is an additional response time when answering the OLAP queries. The latter approach is rather syntactic since it enforces an optimization at the logical level. It is called Partition Cube and based on the concept of partition. We also give an algorithm to compute it. We propose a Relational Partition Cube, a novel R-Olap cubing solution for managing Partition Cubes using the relational technology. An analytical evaluation shows that the storage space of Partition Cubes is smaller than Datacubes. In order to confirm analytical comparison, experiments are performed in order to compare our approach with Datacubes and with two of the best reduction methods, the Quotient Cube and the Closed Cube.