Automated Document Revision Clustering for Storage Management
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Solution Overview
Problem
In multi-user environments, managing and conserving storage space for numerous document revisions becomes challenging due to the impracticality of retaining every revision, especially when storage is limited, and navigating through revisions to find specific changes is cumbersome.
Innovation Solution
The Automated Document Revision Management Server (ADRMS) automatically clusters revisions based on similarity, designating a representative revision for each cluster and using algorithms and heuristics to determine when to create new clusters or trim revisions, considering time, content, and context factors, thereby conserving storage space and simplifying navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all revisions of a document are stored permanently in a document store, then complete revision history is preserved, but storage space is consumed excessively
Solution Approach 1:
The patent segments the continuous revision history into discrete clusters grouped by metadata characteristics (author, date, document type). Each cluster represents a logical group of related revisions that can be managed independently, allowing the system to retain essential revision groups while eliminating redundant ones, thus preserving reliability while reducing storage consumption.
Solution Approach 2:
The system changes the parameters used to evaluate and manage revisions by introducing metadata-based clustering criteria (author, date, document type) rather than treating all revisions uniformly. This parameter transformation enables intelligent differentiation between essential and redundant revisions, allowing selective retention that maintains historical completeness where needed while reducing storage where redundant.
2Loss of information
If multiple revisions are stored for navigation and restoration, then document change tracking is improved, but user navigation becomes unwieldy
Solution Approach 1:
The patent segments the flat revision list into hierarchical clusters based on metadata groupings (by author, by date range, by document type). Users navigate through organized clusters rather than a linear sequence of individual revisions, reducing cognitive load and navigation steps while maintaining access to all necessary revision information for tracking and restoration.
Solution Approach 2:
The system adds a metadata-based organizational dimension to revision navigation. Instead of navigating solely through temporal sequence (one-dimensional), users can navigate through multiple organizational dimensions simultaneously (author, date, document type), creating a multi-dimensional navigation structure that reduces the effective distance to any target revision.
3Quantity of substance
If manual deletion of old revisions is implemented, then storage space is recovered, but time and effort are consumed
Solution Approach 1:
The patent implements self-service automation where the system automatically performs revision clustering, evaluation, and deletion based on predefined metadata criteria and storage thresholds. The system monitors its own storage usage, identifies redundant revision clusters autonomously, and executes deletion without user intervention, converting manual labor into automated self-management that recovers storage space while eliminating time consumption.
Solution Approach 2:
The system establishes a feedback loop that continuously monitors storage space consumption, automatically triggers clustering and deletion operations when thresholds are exceeded, and adjusts its behavior based on the results. This closed-loop feedback mechanism enables dynamic, adaptive storage management that automatically recovers space without requiring manual time investment from users.
Data Source
AI summary
Methods, systems and techniques for managing revisions of documents in a collaborative, multiuser document store are provided. Example embodiments provide an Automated Document Revision Management Server (“ADRMS”) to automatically cluster and remove revisions of file content for easy navigation and management. Revisions are trimmed when necessary to conserve storage space. The ADRMS creates logical clusters of revisions based upon some measure of their similarities. That is, revisions that are similar and can be represented by the latest revision in the cluster formulate one cluster, and those that are markedly dissimilar are placed in a different cluster. The logic used to cluster revisions accounts for time-based factors, content-based factors, and context-based factors to determine whether a revision is incremental and can be grouped in the same cluster or is significant enough to warrant a new cluster. Revisions may be trimmed based upon age and/or available space by a revision trimming component.