DICOM Metadata Linking for Faster Massive File Retrieval
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The challenge of managing and searching large volumes of heterogeneous data, particularly with file types like DICOM, results in inflated storage loads and slowed search processes due to separate metadata storage, making it difficult to extract maximum benefits from these data sets.
Innovation Solution
Transforming and compressing DICOM tags into a single string format, reducing metadata documents, and creating associations between files with common metadata to enhance searchability and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If metadata is stored separately from files, then file storage flexibility is improved, but storage load increases and search speed decreases
Solution Approach 1:
The patent merges previously separate metadata storage with file storage by creating associations between files and their metadata through common links. This allows metadata to remain logically separate for flexibility while being physically integrated in the database structure to improve search performance, resolving the contradiction between storage flexibility and search speed.
2Reliability
If large volumes of heterogeneous data are aggregated, then data robustness is improved, but storage load and management complexity increase
Solution Approach 1:
The patent segments heterogeneous data into unified data structures with standardized fields and types. By dividing complex heterogeneous data into structured components with defined schemas, the system maintains data robustness through comprehensive aggregation while reducing management complexity through standardized organization and querying capabilities.
3Adaptability or versatility
If DICOM tags are stored as separate metadata documents, then data structure flexibility is improved, but storage efficiency decreases
Solution Approach 1:
The patent combines multiple separate metadata documents into a single consolidated metadata structure associated with each file through common links. This merging reduces the total quantity of stored metadata while preserving structural flexibility by maintaining organized fields and types within the unified structure, directly addressing the contradiction between flexibility and storage efficiency.
Data Source
AI summary
System and methods for massive data management and tagging are disclosed herein. A method for automated file linking can include creating a sample set of files from a set of files, at least some of the files including metadata. The method can include identifying common metadata between files in the sample set of files from the file set, and identifying at least one link, one of which links can include the common metadata between files in the sample set of files. The method can include identifying files in the set of files, each of the identified files containing the link in their metadata, generating an association between the files containing the link in their metadata, and storing the association between the files containing the link in their metadata.