Smart Document Retention for ITSM Knowledge Preservation
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Solution Overview
Problem
ITSM systems face challenges in efficiently managing and retaining knowledge from older customer incidents due to data storage burdens and the risk of losing relevant information when documents are automatically deleted, leading to incorrect decision-making and resource wastage.
Innovation Solution
Implementing a smart document retention system that uses machine learning to extract knowledge from documents, compute similarity levels, and recommend unique documents for long-term storage, thereby preventing unintended deletion of useful information and optimizing storage resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If documents are automatically deleted after a specified period to manage storage space, then storage resources are conserved, but relevant knowledge information is lost
Solution Approach 1:
The system performs preliminary actions by automatically extracting knowledge from documents before deletion occurs. The machine learning model processes documents in advance to identify and extract valuable knowledge, which is then stored separately while the original documents can be deleted, preventing information loss while conserving storage space.
Solution Approach 2:
The system extracts valuable knowledge information from documents using machine learning techniques. The knowledge extraction module identifies and separates useful information from the original documents, creating a knowledge base that preserves essential information while allowing the original documents to be deleted for storage optimization.
2Loss of information
If human processors manually review each document for quality check, then information accuracy is improved, but processing time increases
Solution Approach 1:
The system enables self-service by using machine learning models to automatically review and evaluate documents for quality and knowledge value. The automated review process replaces manual human processing, maintaining information accuracy while significantly reducing the time required to evaluate documents for retention.
Solution Approach 2:
The system substitutes the mechanical manual review process with an automated machine learning-based review system. The machine learning model performs quality assessment and knowledge value evaluation automatically, replacing human processors and eliminating the time consumption associated with manual document review while maintaining high accuracy.
3Loss of information
If all documents are stored indefinitely to preserve knowledge, then information retention is improved, but storage resource consumption increases
Solution Approach 1:
The system applies local quality by differentiating the treatment of documents based on their individual knowledge value and importance. The machine learning model assesses each document's quality and extracts knowledge selectively, retaining only the most valuable information while deleting less important documents, thus optimizing storage resource consumption while maintaining essential knowledge retention.
Solution Approach 2:
The system extracts and retains only the essential knowledge information from documents rather than storing all documents indefinitely. The knowledge extraction process identifies and preserves only the most valuable information in a compressed format, reducing storage resource consumption while maintaining effective knowledge retention.
4Productivity
If documents are deleted without similarity comparison to simplify processing, then processing speed is improved, but decision-making accuracy deteriorates
Solution Approach 1:
The system performs preliminary knowledge extraction and document processing before deletion decisions are made. The machine learning model pre-processes documents to extract knowledge and compute similarity metrics, enabling accurate deletion decisions to be made quickly without sacrificing decision-making accuracy for the sake of processing speed.
Data Source
AI summary
Techniques for conserving system resources using smart document retention are disclosed. A computer system may identify a first portion of a plurality of documents as being decision-making documents based on each document in the first portion being scheduled for deletion within a specified period of time, identify a second portion of the plurality of documents as being non-decision-making documents based on each document in the second portion either being scheduled for deletion outside of the specified period of time or having been converted into and stored as a knowledge base document, and determine a corresponding level of similarity between each decision-making document and each non-decision-making document. The computer system may then cause identifications of a subset of the decision-making documents to be displayed on a computing device based on the corresponding level of similarity between each decision-making document in the subset and at least one of the non-decision-making documents.