OLAP: OLAP: OLAP can help analysts, managers from a variety of perspectives from the original data out of the transformation, can really for the user to understand and truly reflect the data dimensional characteristics of the information, fast, consistent, interactive access to the data, so as to obtain a more in-depth understanding of the data. OLAP provides users with powerful statistical analysis and report processing capabilities. Online Analytical Processing (OLAP) concept was first proposed by the father of relational databases, E.F. Codd in 1993. At that time, Codd that online transaction processing (OLTP) has been unable to meet the needs of end-users to analyze the database query, SQL for large databases to carry out a simple query can not meet the needs of user analysis. The user's decision analysis requires a large number of calculations on the relational database to get the results, and the results of the query do not meet the needs of decision makers. Therefore, Codd proposed the concept of multidimensional databases and multidimensional analysis, or OLAP.Codd proposed 12 criteria for OLAP to describe OLAP systems: Criterion 1 The OLAP model must provide a multidimensional conceptual view Criterion 2 Transparency criterion Criterion 3 Access capacity speculation Criterion 4 Stable reporting capacity Criterion 5 Client/server architecture Criterion 6 Dimensional equivalence criterion Criterion 7 Dynamic Guideline 7 Dynamic Sparse Matrix Processing Guideline 8 Multi-User Support Capability Guideline 9 Unconstrained Cross-Dimensional Operations Guideline 10 Intuitive Data Manipulation Guideline 11 Flexible Report Generation Guideline 12 Unconstrained Dimensional and Aggregation Hierarchies The basic multidimensional analytical operations of OLAP are drill up and drill down, slice and dice, and pivot. pivot), etc. Drill up is to change the level of dimensionality and transform the granularity of the analysis. It includes drill up and drill down. A roll up is a generalization from low-level detail data to high-level summary data in a given dimension; a drill down, on the other hand, is a deeper look from summary data to detail data. Slicing and dicing are concerned with the distribution of the metric data over the remaining dimensions after selecting values in one of the dimensions. If there are only two remaining dimensions, it is slicing, otherwise it is chunking. Rotation is a transformation of the orientation of dimensions, i.e., rearranging the placement of dimensions in a table (e.g., swapping rows and columns).