Database System Transforming Unstructured Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional databases are inefficient in handling and integrating unstructured data, leading to incomplete search results and increased retrieval times due to incompatibility with structured data formats, resulting in missing information and errors.
Innovation Solution
A database management system that fetches external unstructured data, transforms it into structured data by determining lowest denominators and joinable keys, and merges it with internal data, ensuring compatibility and efficient search and retrieval processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional databases store only structured data, then data storage efficiency is maintained, but the ability to integrate and process unstructured data is lost
Solution Approach 1:
The patent introduces an intermediary data transformation layer that converts unstructured data from external sources into structured format compatible with the database. This mediator component handles the complexity of parsing, validating, and formatting unstructured data before it enters the database, allowing the database itself to remain relatively simple while gaining the capability to process diverse data types.
Solution Approach 2:
The system dynamically adjusts data parameters and formats based on the source and type of incoming data. By transforming unstructured data into structured formats with standardized parameters, the system adapts to different data sources while maintaining consistent storage structures, thus improving adaptability without proportionally increasing complexity.
2Quantity of substance
If unstructured data is added to the database without transformation, then data volume increases, but search accuracy and retrieval efficiency deteriorate
Solution Approach 1:
The system performs preliminary transformation of unstructured data into structured format before it is stored in the database. This pre-processing step includes parsing, validating, and formatting the data according to predefined schemas, ensuring that when data is later retrieved and searched, it is already in the correct format for efficient and accurate querying.
Solution Approach 2:
The patent replaces manual data processing mechanisms with automated transformation systems that use parsers, validators, and formatters to convert unstructured data. This substitution of mechanical processing with systematic algorithmic transformation maintains high search accuracy while handling large volumes of data efficiently.
3Reliability
If data transformation processes are implemented, then data compatibility is improved, but processing time and system resources increase
Solution Approach 1:
Data transformation is performed as a preliminary action during the data ingestion phase, before the data needs to be stored or queried. By completing the transformation upfront, the system ensures data compatibility is established early, and subsequent retrieval operations do not need to spend additional time on transformation, thus minimizing overall processing time.
Solution Approach 2:
The transformation system operates autonomously during data ingestion, automatically detecting data types, applying appropriate transformation rules, and validating data without requiring manual intervention. This self-service capability reduces processing time by eliminating human involvement in the transformation process while maintaining high reliability of data compatibility.
Data Source
AI summary
A system for merging data into a database is disclosed. During operation, the system may fetch a set of data from a data source external to the database. The system may determine that the fetched set of data is unstructured data, and then transform the fetched set of data into structured data. The system may also determine one or more lowest denominators for the fetched set of data, determine that the fetched set of data does not meet the one or more lowest denominators, and transform the fetched set of data to meet the one or more lowest denominators. The system may further determine one or more joinable keys for the fetched set of data, and merge the fetched set of data into the database.