Intelligent Data Distribution via Access Pattern Forecasting

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Solution Overview

Problem

Current data management systems face challenges in dynamically reorganizing data partitions to optimize performance and adapt to changing data access patterns, leading to potential downtime and inefficiencies in handling large datasets.

Innovation Solution

A data management system that forecasts data access patterns to automatically and transparently reassign data records to different partitions, dynamically replicating data to optimize performance and reduce network traffic, while removing excess partitions to facilitate future replications.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If data is statically distributed across partitions, then system structure is simple and easy to manage, but performance degrades when access patterns change and manual reorganization is required

Engineering Contradiction:

Improveadaptability to changing access patternsVSAvoidcomplexity of data distribution management

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system performs self-service by automatically detecting changing access patterns and dynamically reorganizing data partitions without manual intervention. The data management system monitors query patterns, identifies hot spots, and autonomously redistributes data to optimize performance, allowing the system to adapt to changing workloads while maintaining simplicity for users.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent implements dynamics by transitioning from static data distribution to dynamic redistribution based on real-time access patterns. Data partitions are continuously adjusted according to observed query behaviors, enabling the system to adapt its structure dynamically rather than requiring fixed manual configuration.

Inventive Principle:

Principle #15Dynamics

2Productivity

If data is manually reorganized to optimize performance, then performance can be improved for known patterns, but system downtime occurs and manual administration is required

Engineering Contradiction:

Improvedata processing performanceVSAvoidsystem downtime during reorganization

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary action by proactively detecting emerging access patterns and pre-reorganizing data partitions before performance degradation occurs. By monitoring query patterns in real-time and anticipating hot spots, the system redistributes data in advance, preventing performance issues rather than reacting to them after downtime is required.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent ensures continuity of useful action by implementing dynamic data redistribution that occurs without system downtime. The data management system continuously monitors access patterns and performs incremental reorganization operations while the system remains operational, eliminating interruptions and maintaining continuous productivity.

Inventive Principle:

Principle #20Continuity of useful action

3Reliability

If data is replicated across multiple partitions, then system resilience and availability improve, but network traffic increases and storage efficiency decreases

Engineering Contradiction:

Improvesystem resilienceVSAvoidnetwork traffic overhead

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The system applies local quality by replicating data selectively based on local access patterns rather than uniformly across all partitions. The data management system identifies specific hot spots and replicates only those data portions that require frequent access, maintaining high resilience for critical data while avoiding unnecessary replication elsewhere, thus reducing network traffic overhead.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The patent implements parameter changes by dynamically adjusting replication factors and data distribution parameters based on observed access patterns. The system modifies replication levels adaptively, increasing replication for high-demand data and reducing it for less frequently accessed data, optimizing the balance between reliability and network efficiency.

Inventive Principle:

Principle #35Parameter changes

4Quantity of substance

If data partitions are increased to handle larger datasets, then storage capacity increases, but management complexity and coordination overhead increase

Engineering Contradiction:

Improvedata storage capacityVSAvoidpartition management complexity

Core Design Contradiction:

Quantity of substanceVSDevice complexity

Solution Approach 1:

The system performs self-service by automatically managing the complexity of partition operations through intelligent algorithms. The data management system autonomously handles data redistribution, replication, and partitioning decisions based on access patterns, eliminating the need for manual partition management and reducing coordination overhead even as the number of partitions scales to handle large datasets.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS11544290B2Intelligent data distribution and replication using observed data access patterns

Publication Date: 2023.01.03 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Embodiments for providing intelligent data replication and distribution in a computing environment. Data access patterns of one or more queries issued to a plurality of data partitions may be forecasted. Data may be dynamically distributed and replicated to one or more existing data partitions or additional of the plurality of data partitions according to the forecasting.