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Multi-dimensional data cube increment aggregation and query optimization method

A multi-dimensional data and query optimization technology, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve problems such as low efficiency, high system load, and huge data volume in data warehouses, and achieve high efficiency and small system load Effect

Active Publication Date: 2012-02-22
ZHEJIANG HONGCHENG COMP SYST
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

For data warehouses that are incrementally updated at regular intervals, the traditional aggregation algorithm needs to aggregate all data. However, the data volume of the data warehouse is huge. This method is inefficient and the system load is high, which is unbearable for users.

Method used

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  • Multi-dimensional data cube increment aggregation and query optimization method
  • Multi-dimensional data cube increment aggregation and query optimization method
  • Multi-dimensional data cube increment aggregation and query optimization method

Examples

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Embodiment 1

[0018] Embodiment 1: a kind of multi-dimensional data cube incremental aggregation and query optimization method, in this method, focus is on selecting algorithm to aggregate incremental data and summarize multiple results hit by OLAP query; The following describes each process in turn:

[0019] 1. Get incremental data:

[0020] Incremental data can be acquired through triggers, timestamps, full table comparisons, and log comparisons. Since this method needs to obtain incremental data periodically, the timestamp method is selected. Timestamp is a change data capture method based on snapshot comparison. A timestamp field is added to the source table. When the system updates and modifies table data, the value of the timestamp field is also modified. When extracting data, determine which data to extract by comparing the system time and the value of the timestamp field. The timestamp of some databases supports automatic update, that is, when the data in other fields of the tabl...

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Abstract

The invention relates to the field of OLAP (On-Line Analytical Processing) aggregation technique and query optimization, and in particular relates to a multi-dimensional data cube increment aggregation and query optimization method, which more rapidly realizes the advantages of increment aggregation, high efficiency, small system load and convenience in maintenance through small-range aggregationof the increment data and collecting original aggregation and increment aggregation results during querying and solves the problem in the prior art. The method has the beneficial effects of high efficiency and small system load because the increment data are aggregated by using the characteristics of the increment data, and no reduction of the efficiency when collecting the original aggregation and a plurality of delta Cube aggregation results during querying.

Description

technical field [0001] The invention relates to the field of OLAP aggregation technology and query optimization, in particular to a multi-dimensional data cube incremental aggregation and query optimization method. Background technique [0002] Online Analytical Processing (OLAP) enables analysts and decision makers to access data quickly, consistently, and interactively from multiple perspectives to gain a deeper understanding of the data. However, with the increasing amount of data, users' demand for real-time decision-making is becoming more and more urgent. OLAP aggregation technology can effectively solve the problem of data query efficiency. For data warehouses that are incrementally updated at regular intervals, the traditional aggregation algorithm needs to aggregate all the data. However, the data volume of the data warehouse is huge. This method is inefficient and the system load is high, which is unbearable for users. Contents of the invention [0003] In order...

Claims

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Application Information

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IPC IPC(8): G06F17/30
Inventor 王璐华肖敏周伟强徐精忠
Owner ZHEJIANG HONGCHENG COMP SYST
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