Self-indexing data structure for semantic metadata standardization
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Solution Overview
Problem
Database systems face inefficiencies in locating specific records due to the need to scan entire tables, which can be time-consuming, and existing indexes may not effectively handle unstructured or misspelled data, leading to challenges in data transformation and searching across semantic boundaries.
Innovation Solution
A self-indexing data structure and methodology that automatically generates indexes for database systems using a semantic metadata model, which standardizes and classifies data, allowing for efficient transformation and searching by converting raw data into standardized format and generating identifiers for rapid access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If database indexes are established to reduce record location time, then search speed is improved, but the complexity of data structure increases
Solution Approach 1:
The patent segments the data structure by creating a separate index structure that is distinct from the main data table. This index structure contains only the key fields needed for searching, separating the search functionality from the complete data storage, thereby improving search speed without significantly increasing overall system complexity.
Solution Approach 2:
The patent applies preliminary action by pre-processing the data to create an index structure before actual search operations. The index is built in advance containing sorted key fields and their locations, so that when search queries are executed, the system can quickly locate records without scanning the entire table, thus improving search speed.
2Quantity of substance
If indexes store only key fields to reduce disk space, then storage efficiency is improved, but the ability to handle unstructured and misspelled data deteriorates
Solution Approach 1:
The patent introduces an intermediary component - a semantic metadata model - that sits between the raw unstructured data and the index structure. This mediator standardizes and cleanses the data, correcting misspellings and normalizing formats, before creating the index. This allows the index to store compact key fields while still accurately representing the original unstructured data, maintaining both storage efficiency and data handling capability.
Solution Approach 2:
The system applies self-service by automatically standardizing and cleansing data through the semantic metadata model without requiring manual intervention. The model autonomously identifies and corrects misspellings, normalizes formats, and structures unstructured data, enabling the index to effectively represent diverse data types while maintaining compact storage.
3Measurement precision
If manual index creation is used to ensure accuracy, then data precision is improved, but the time and effort required increases
Solution Approach 1:
The patent implements self-service through automated index creation using the semantic metadata model. The system automatically standardizes data, identifies key fields, and generates the index structure without manual intervention. This automation maintains high data accuracy through systematic standardization rules while dramatically reducing the time and effort required compared to manual index creation.
Solution Approach 2:
The patent applies parameter changes by transforming unstructured data into standardized formats through the semantic metadata model. This transformation process automatically adjusts data parameters such as text normalization, format standardization, and structure organization, ensuring accurate index creation while eliminating the time-consuming nature of manual parameter adjustment.
Data Source
AI summary
A machine based tool and associated logic and methodology are used in converting data from an input form to a target form using context dependent conversion rules, and in efficiency generating an index that may be utilized to access the converted data in a database. Once the data has been converted, an index data structure for each data object may be automatically generated that encodes one or more characteristics or attributes of the converted data so that an entity may access the data using the index structure. As an example, the one or more characteristics may include categories, subcategories, or other attributes of the data.