The concept of online analysis processing (OLAP) was earliered by the parent E.codd of the relational database. CoDD, in 1993, he also proposed 12 guidelines on OLAP. OLAP has caused a large response, OLAP is distinguished from the online transaction processing (OLTP).
Today's data processing can be roughly divided into two major classes: online transaction processing OLTP (On-line Transaction Processing), online analysis processes OLAP (On-line Analytical Processing). OLTP is the main application of traditional relational databases, primarily basic, daily transaction, such as bank transactions. OLAP is the main application of data warehouse systems, support complex analysis operations, focusing on decision support, and provides intuitive and easy query results. The following table lists the comparisons between OLTP and OLAP.
OLTP OLAP User Operator, Low-level Management Decision Person, Senior Manage Function Daily Operation Decision Decision DB Design Download Top Theme Data Current, Latest Detail, Two-dimensional discrete historical, aggregated, multi-dimensional integration , Unified access / write dozens of records read millions of records work unit simple transaction complex query users number thousands of hundreds of DB size 100MB-GB 100GB-TB OLAP is to manage analysts, management People or executives can quickly, consistent, interactively access information from a multi-angle, thereby obtaining a class of software technologies for more in-depth understanding of data. The goal of OLAP is to meet decision support or meet specific queries and report needs in a multidimensional environment, and its technical core is the concept of "dimension".
"Dimension" is the perspective of people to observe the objective world and is a high-level type division. "Dimension" generally includes hierarchical relationship, which is sometimes quite complicated. By defining a number of important attributes of an entity as multiple dimensions, users can compare different dimensions. Therefore, OLAP can also be said to be a collection of multi-dimensional data analysis tools.
OLAP's basic multi-dimensional analysis operates with Roll Up and DRILL DOWN, slice (SLICE), Dice, and rotation (Pivot), Drill Across, Drill Through et al.
· Drilling is the level of changing the dimension, the particle size of the transformation. It includes a roll UP and drill down (DRILL DOWN). Roll Up is summarized on a certain dimension to summarize the low-level summary data, or reduce dimensions; while Drill Down is reversed from the summary data to detail or increase new dimension. · Slices and cutting blocks After a partial dimension, care is concerned about the distribution of metric in the remaining dimension. If there is only two remaining dimensions, it is slice; if there are three, it is cut. · Rotation is the direction of transform dimension, that is, rearrange the dimension in the table (such as ranks interchange). OLAP has a variety of implementation methods that can be divided into ROLAP, MOLAP, and HOLAP depending on storage data.
ROLAP represents an OLAP implementation based on a relational database. The relationship database is the core, and the representation and storage of multi-dimensional data is performed in a relational structure. ROLAP divides the multidimensional structure of the multi-dimensional database into two types: a class is the fact table, used to store data and Qi Key key; the other is a dimension, that is, at least one table is used to store the level of the dimension, Description information such as a member class. The dimension tables and facts are associated with the primary keyword and the foreign keyword, forming "Star Mode". For hierarchical complex dimensions, in order to avoid redundant data, multiple tables can be used to describe that this star pattern is called "snowflake mode".
MOLAP represents an OLAP implementation based on multi-dimensional data organizations (Multidimensional OLAP). The multi-dimensional data organization is the core, that is, MOLAP uses the multi-dimensional array stored data. The multi-dimensional data will form a "cube" structure in the storage, "Rotate" "Cube" in MOLAP, "cut", "Slices", "Slice" is the main technique for generating a multidimensional data report. HOLAP represents an OLAP implementation based on a hybrid data organization (Hybrid OLAP). If the low layer is a relational, the high layer is a multi-dimensional matrix type. This approach has better flexibility.
There are other ways to implement OLAP, such as providing a dedicated SQL Server, providing special support for SQL queries for some storage modes (such as stars, snow-type).
OLAP Tools are online data access and analysis of specific issues. It analyzes, queries and reports in a multi-dimensional manner. Dimension is a specific angle of people to observe the data. For example, a company is usually observing the sales of products from different perspectives of time, regional and products in consideration of product sales. The time, region and product here are dimension. The multi-dimensional arguments made of these dimensional different combinations and the measured metrics of the investigated metrics are the foundation of OLAP analysis, formulated as (dimension 1, dimension 2, ..., dimension N, metrics), such as (region, time) , Product, sales). Multidimensional analysis refers to the analysis of various analytics, slice, cutting (DRIL-DOWN and ROLL-UP), rotation (PIVOT), and other analytical operations, such as slice, cut-down and roll-up, rotation (Pivot). User users can observe data from multiple angles, multi-sidewalk, thereby in-depth understanding of information included in the data.
According to the organization of syndrome data, common OLAP mainly has two types of multi-domain-based MOLAP and ROLAP based on relational databases. MOLAP is organized and stored in a multi-dimensional manner, and ROLAP utilizes existing relational database techniques to simulate multi-dimensional data. In data warehouse applications, OLAP applications are generally front-end tools for data warehouse applications, while OLAP tools can also be used with data mining tools, statistical analysis tools, enhance decision analysis.
