On-line analytical processing (OLAP) massive multidimensional data dimension storage method

A multi-dimensional data, massive technology, applied in the fields of electrical digital data processing, special data processing applications, instruments, etc., can solve problems such as the inability to adapt to the query and analysis of massive multi-dimensional data, the space occupied by preprocessing data, and the lack of indexing mechanisms. The effect of avoiding brute force scanning, fast calculation and positioning, and saving I/O overhead

Inactive Publication Date: 2013-03-20
SOUTHEAST UNIV
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Problems solved by technology

However, row-oriented ROLAP often needs to scan the entire row of data, which affects the overall query efficiency; MOLAP stores pre-aggregated data in multi-dimensional arrays, which can quickly respond to OLAP aggregation calculations, but its update cost is high and the space occupied by pre-processed data Exponentially grows with increasing dimensionality
The previous OLAP storage technology has been unable to adapt to t

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  • On-line analytical processing (OLAP) massive multidimensional data dimension storage method
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  • On-line analytical processing (OLAP) massive multidimensional data dimension storage method

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Embodiment Construction

[0021] Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

[0022] In this embodiment, the form of source data is shown in Attached Table 1, including TID column, dimension hierarchy attribute column and measure column. TID indicates the position where the attribute value of the dimension level appears in the original data base table, quantity is the measurement column, and the dimension level attribute column is between TID and quantity.

[0023] 1) Split by dimension

[0024] In view of the characteristic that OLAP analysis usually performs aggregation ...

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Abstract

The invention discloses an on-line analytical processing (OLAP) massive multidimensional data dimension storage method. Firstly, OLAP multidimensional data are divided according to dimensions, dimension hierarchical encoding is built, a high definition (HD) File dimension storage file structure is designed, only relevant dimension corresponding data needs to be accessed for aggregation calculation, and therefore retrieval of unrelated data is avoided; secondly, a B+ tree index based on the dimension hierarchical encoding is built for rapid positioning of the dimension storage data, and therefore input (I)/output (O) overhead is saved; and at last, a high-efficiency parallel query algorithm is designed, and OLAP query efficiency is further improved. Therefore, the OLAP massive multidimensional data dimension storage method which is high in efficiency, easy to use and scalable is provided for massive data analysis application for scientific experimental statistics, environmental meteorology, bioinformatics computing and the like.

Description

technical field [0001] The invention relates to a dimension storage method for OLAP massive multidimensional data, which is suitable for fast analysis of multidimensional massive data in an OLAP system, and especially capable of distinguishing the hierarchical characteristics of dimensions in OLAP analysis. Background technique [0002] First the abbreviations and terms used in the present invention are explained: [0003] OLAP: Online Analytical Processing, online analytical processing; [0004] ROLAP: Relational OLAP, relational OLAP; [0005] MOLAP: Multidimensional OLAP, multidimensional OLAP; [0006] Hadoop: a distributed system infrastructure; [0007] Hive: A data warehouse tool based on Hadoop; [0008] Pig: a dataflow language and runtime for retrieving very large datasets; [0009] HDFS: Hadoop Distributed File System, Hadoop Distributed File System; [0010] HDFile: HDFS Dimension File, distributed dimension storage file; [0011] MapReduce: a parallel pro...

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Application Information

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IPC IPC(8): G06F17/30
Inventor 宋爱波何战国罗军舟
Owner SOUTHEAST UNIV
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