Adaptive Data Storage Management via Cognitive Analysis
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Solution Overview
Problem
Current information management systems are inefficient due to the time-consuming process of deciding on data storage methods, requiring human intervention, and the uncertainty of future data field additions, leading to inefficiencies in data storage and retrieval.
Innovation Solution
An adaptive information storage management system that analyzes incoming data streams using predetermined rules and cognitive computing techniques to automatically identify optimal storage systems, eliminating the need for human intervention and optimizing storage allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If application developers manually decide on data storage methods and engage with database teams, then storage decisions can be customized according to specific requirements, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system enables self-service by automatically analyzing incoming data streams and determining optimal storage methods without requiring application developers to manually consult with database teams. The automated data processing engine evaluates data characteristics and selects appropriate storage systems, eliminating the time-consuming manual decision-making process while maintaining storage decision quality
Solution Approach 2:
The patent replaces the mechanical human interaction process (developers engaging with database teams) with an automated computational system. The data processing engine uses algorithms and cognitive computing techniques to substitute human decision-making, thereby reducing time loss while preserving the precision of storage decisions through systematic data analysis
2Speed
If schema-based information storage systems are used with predefined structures, then data retrieval can be efficient, but the system becomes inflexible when additional fields need to be added in future
Solution Approach 1:
The system applies dynamics by continuously monitoring incoming data streams and adapting storage decisions in real-time. The data processing engine evaluates current data characteristics and dynamically selects or modifies storage schemas, allowing the system to transition between structured and unstructured storage approaches based on evolving data requirements while maintaining retrieval efficiency
Solution Approach 2:
The patent utilizes parameter changes by modifying storage schema parameters based on analyzed data patterns. When additional fields are anticipated or required, the system adjusts schema parameters dynamically, transforming rigid predefined structures into flexible configurations that accommodate future data needs without sacrificing current retrieval performance
3Productivity
If cognitive computing techniques are used to automatically analyze data streams, then human intervention is eliminated and processing speed improves, but the system complexity increases
Solution Approach 1:
The system reduces complexity through segmentation by dividing the data processing function into distinct modular components. The data processing engine is separated from the storage system, with clear interfaces between them. This modular architecture allows cognitive computing techniques to be implemented in the processing engine without overwhelming the entire system, enabling high productivity while managing complexity through organized functional segments
4Stability of the object's composition
If automated rule-based systems are implemented to determine storage allocation, then consistency in storage decisions is improved, but the ability to handle unique or edge-case scenarios may be reduced
Solution Approach 1:
The system resolves the contradiction between consistency and adaptability through feedback mechanisms. The data processing engine analyzes storage performance and data pattern outcomes, using this feedback to refine its decision-making rules. This continuous learning process maintains consistent application of proven effective storage decisions while adapting to unique scenarios through accumulated knowledge, balancing stability with versatility
Data Source
AI summary
A system and a method for adaptive information storage management is provided. One or more parameters from an incoming data stream is identified based on a set of predetermined rules. The identified parameters correspond to a set of predetermined parameters. A subset of rules is applied, from the set of predetermined rules, on the incoming data stream. The subset of rules represent a series of iterative rules associated with each identified parameter. One or more data storage allocation files are generated that represent results of application of the set of predetermined rules and the subset of rules. The results include at least a storage system type identified from the storage system as optimal for storing the incoming data stream.