Categorical Data Dynamic Decoding for IT Operational Analysis
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Solution Overview
Problem
Current systems face challenges in processing, storing, and analyzing vast amounts of data associated with information technology operational activities, struggling to evaluate interdependencies and technical language processing efficiently, leading to increased memory requirements and processing time.
Innovation Solution
A computerized system that performs operational data processing, technical language processing, categorical data encoding, and dynamic data decoding to reduce memory requirements and processing time, while evaluating interdependencies and technology change incidents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing systems process and store massive amounts of IT operational data, then data storage capacity increases, but memory requirements and processing time increase significantly
Solution Approach 1:
The patent transforms operational data by changing its structural parameters from traditional flat storage to a hierarchical categorical structure with encoded representations. This parameter transformation enables efficient compression and faster retrieval operations while maintaining data integrity and query capabilities.
Solution Approach 2:
The patent segments operational data into distinct categorical dimensions and hierarchies, organizing data into structured categories that can be independently processed and retrieved. This segmentation enables selective access to specific data portions without processing entire datasets, reducing processing time.
2Loss of information
If existing systems store detailed operational data, then data completeness improves, but memory requirements increase
Solution Approach 1:
The patent creates encoded categorical representations that serve as compressed copies of the original operational data. These encoded forms retain essential information for analysis and retrieval while occupying significantly less storage space than the raw detailed data.
Solution Approach 2:
The patent transforms detailed operational data into categorical encodings that change the representation parameters from verbose detailed records to compact categorical identifiers, reducing memory requirements while preserving analytical value.
3Measurement precision
If existing systems analyze interdependencies of IT activities, then evaluation accuracy improves, but device complexity increases
Solution Approach 1:
The patent segments the complex analysis task into manageable categorical dimensions and hierarchical levels. By organizing data into structured categories, the system can analyze interdependencies within each category and across categories systematically, improving evaluation accuracy without overwhelming system complexity.
Solution Approach 2:
The patent introduces categorical dimensions as an additional organizational layer for analyzing IT operational data. This dimensional transformation enables the system to evaluate interdependencies by examining relationships across multiple categorical axes simultaneously, enhancing analytical depth while maintaining manageable complexity through structured organization.
Data Source
AI summary
Embodiments of the present invention relate to apparatuses, systems, methods and computer program products for a technology configuration system. Specifically, the system typically provides operational data processing of a plurality of records associated with information technology operational activities, for dynamic transformation of data and evaluation of interdependencies of technology resources. In other aspects, the system typically provides technical language processing of the plurality of records for transforming technical and descriptive data, and constructing categorical activity records. The system may be configured to achieve significant reduction in memory storage and processing requirements by performing categorical data encoding of the plurality of records. The system may employ a dynamic categorical data decoding process, which delivers a reduction in processing time when the encoded records are decoded for evaluating the exposure of technology change events to technology incidents and modifying such technology change events.