Data warehouse security OLAP method on memory cloud computing platform

Abstract

The invention relates to a data warehouse security OLAP method on a memory cloud computing platform. The method comprises the following steps: eliminating semantic information of a fact table on an OLAP computing model, wherein the fact table only stores numeric data and does not store data description information; carrying out dimension table querying and encoding on an inner-enterprise private data platform by adopting an encoding-based computing method on the OLAP computing model, realizing semantic-free encoding-based multi-dimensional computation on a fact table storage cloud platform, returning an encoded OLAP query result to the inner-enterprise private data platform to carry out semantic analysis, and returning a real query result; and increasing security dimension to the cloud platform fact table storage to encode the measure attribute, so as to change the original measure attribute storage manner and prevent the measure attribute of the fact table from being illegally accessed. The method is capable of supporting the storage and computation of enterprise data warehouse data on the cloud platform, improving the cost performance of the OLAP computation and ensuring the enterprise data security, and can be applied to the enterprise memory OLAP cloud computation or other OLAP computations.

Description

technical field [0001] The invention relates to a query processing method in the field of data warehouses, in particular to a data warehouse security OLAP (analytical query processing) method on a memory cloud computing platform oriented to the analysis and processing of enterprise sensitive data. Background technique [0002] In-memory computing is a big data solution. Although the current commercial in-memory database cluster has reached a very large scale and data storage and processing capabilities, in-memory computing is still an expensive solution at this stage. The configuration of the in-memory computing platform And operating costs are very high, and a trend in memory computing in the future is memory cloud computing services. The huge amount of data in the data warehouse requires the cloud platform to become a persistent data storage platform to reduce the cost of data migration. However, the data warehouse stores the core business data of enterprises and departmen...

AI Technical Summary

Problems solved by technology

In-memory cloud computing services face two contradictory problems: one is to permanently store as much data as possible on the cloud computing platform in order to improve the performance of in-memory computing and reduce the cost of big data movement; the other is to improve data security It is necessary to store as few key business data as possible that may cause data security risks on the cloud computing platform

Huge fact table storage is suitable for using the memory cloud computing platform to reduce its data storage cost, but it requires a data security mechanism to ensure that the fact data is difficult to be illegally analyzed and interpreted, and it also requires multi-platform collaborative computing problems caused by data division through security requirements

[0005] Cloud computing is a shared data storage and computing service that optimizes hardware resources and costs, but its commercial operation model is difficult to meet the special data security requirements of enterprises or important departments, so it requires a sensitive data and "Divide and conquer" storage and computing strategies for sensitive data on enterprise private system platforms and cloud computing platforms

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