User-Defined Autotagging Policies for Data Center Accuracy
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Solution Overview
Problem
Existing data center management systems face challenges with manual data tagging, leading to errors, inefficiencies, and increased cognitive load due to typos, synonyms, and the need for users to understand complex regular expressions for autotagging policies.
Innovation Solution
Implementing a system and method for auto-tagging policies that allow users to define tagging structures automatically, using a processor and computer-readable storage medium to identify objects within a data center asset, associate contextual meanings, and apply tags conforming to user-defined structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data tagging is used, then users can directly control tagging, but errors increase and efficiency decreases due to typos, synonyms, and cognitive load
Solution Approach 1:
The system enables self-service autotagging by allowing users to define their own tagging structures and policies without requiring expertise in regular expressions or complex pattern matching. The autotagging engine automatically applies these user-defined structures to data, eliminating manual tagging while maintaining user control and reducing errors from manual input.
Solution Approach 2:
The patent introduces an intermediary layer between manual tagging and automated tagging. This intermediary consists of user-defined tagging structures and policies that guide the autotagging process, ensuring that automated tagging aligns with user expectations while eliminating the need for users to directly write complex tagging patterns.
2Extent of automation
If complex regular expressions are used for autotagging, then automation increases, but user burden increases due to the need to understand and create complex patterns
Solution Approach 1:
The patent replaces complex, difficult-to-manage regular expressions with simpler, more intuitive tagging structures that can be easily defined and modified by users. These user-defined structures act as disposable alternatives to complex patterns, achieving the same autotagging goal without the cognitive overhead.
Solution Approach 2:
The system changes the parameters of tag definition from complex regular expression patterns to simpler user-defined structures with configurable properties. This parameter change maintains automation capability while significantly reducing the complexity users must understand and manage.
3Adaptability or versatility
If manual tagging is performed, then tagging can be customized, but time consumption increases and redundancy increases
Solution Approach 1:
The system performs preliminary action by having users define tagging structures and policies in advance. Once defined, these structures are automatically applied to data through the autotagging engine, eliminating the need for repeated manual tagging while preserving customization. The preliminary structure definition enables fast, consistent automated tagging.
Solution Approach 2:
The user-defined tagging structures serve multiple functions: they guide autotagging, ensure consistency across different data sets, and can be reused across multiple tagging scenarios. This universality eliminates redundant manual tagging work while maintaining adaptability to different customization needs.
Data Source
AI summary
A system, method, and computer-readable medium for performing a data center monitoring and management operation. The data center monitoring and management operation includes: identifying an object within a data center asset; indicating a desire to provide a contextual meaning for the object; designating a user defined tagging structure; generating an auto tagging policy based upon the user defined tagging structure; and, instantiating a tag, the tag associating the contextual meaning with the object, the tag automatically conforming to the user defined tagging structure, the instantiating using the auto tagging policy.