Cloud Data Reduction Analysis System

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Solution Overview

Problem

The increasing complexity and cost of managing vast amounts of data in databases lead to challenges in balancing infrastructure maintenance and data access, resulting in potential legal and compliance risks due to inadequate data retention practices.

Innovation Solution

A system comprising a central module that assesses the data reduction potential of a source repository and conveys this information to the source module, utilizing in-memory databases, archiving, and deletion methods to optimize data storage and retention periods.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If data is retained in databases for extended periods to meet legal requirements, then compliance with data retention regulations is improved, but storage costs and infrastructure complexity increase

Engineering Contradiction:

Improvecompliance with data retention regulationsVSAvoidinfrastructure complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent segments data into different retention categories (legal retention, business value, archival) and applies different storage strategies to each segment. Hot data is kept in primary databases, warm data in archival storage, and cold data in long-term storage solutions, thereby reducing overall infrastructure complexity while maintaining compliance.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system dynamically changes storage parameters (retention periods, storage location, access frequency) based on data characteristics and legal requirements. This allows optimization of storage resources while ensuring compliance with varying data retention regulations across different data types and jurisdictions.

Inventive Principle:

Principle #35Parameter changes

2Reliability

If data is retained in databases for extended periods to meet legal requirements, then compliance with data retention regulations is improved, but storage costs increase

Engineering Contradiction:

Improvecompliance with data retention regulationsVSAvoidstorage costs

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

Different storage quality levels are applied to different data based on their specific retention requirements and access patterns. Frequently accessed data receives higher quality storage, while data with strict retention requirements but low access frequency is moved to cost-effective archival storage, optimizing the balance between compliance and cost.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system automatically adjusts storage parameters such as retention duration, storage location, and access policies based on legal requirements and business value assessment, enabling cost optimization while maintaining necessary compliance standards.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If data access is increased to support business operations, then business data accessibility is improved, but infrastructure performance and latency are worsened

Engineering Contradiction:

Improvebusiness data accessibilityVSAvoiddata access latency

Core Design Contradiction:

ProductivityVSSpeed

Solution Approach 1:

Data is segmented by access frequency and business criticality, with hot data kept in high-performance databases for rapid access and cold data moved to archival storage. This segmentation maintains high productivity for critical operations while reducing the performance burden of storing all data at high speed.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary actions by pre-processing and indexing data during off-peak hours, and by proactively moving data between storage tiers based on predicted access patterns. This reduces access latency during business operations without compromising data accessibility.

Inventive Principle:

Principle #10Preliminary action

4Productivity

If data is retained beyond necessary periods, then data availability is improved, but legal and compliance risks increase

Engineering Contradiction:

Improvedata availabilityVSAvoidlegal and compliance risk

Core Design Contradiction:

ProductivityVSObject-affected harmful factors

Solution Approach 1:

The system dynamically adjusts retention parameters based on legal requirements, data type, and jurisdiction-specific regulations. This ensures data is retained for the optimal duration - long enough to maintain availability when needed, but not so long as to create legal and compliance risks from excessive retention.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system implements feedback mechanisms that continuously monitor data retention status, access patterns, and regulatory changes, automatically adjusting retention policies to maintain data availability while minimizing legal risks from improper retention.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS10782883B1Cloud-based ad-hoc impact analysis of data saving potentials

Publication Date: 2020.09.22 SAP SE

AI summary

Disclosed herein are system, method, and computer program product embodiments for the assessing of data reduction potential of a source repository of a source module, by a central module, the generation of data savings potential statistics of the source repository by the central module, and the subsequent generation of visual representation of the statistics, and displaying of the visual representation of data reduction potential information.