Mapping Table Rows to Characters for Pattern Recognition
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Solution Overview
Problem
Accessing large relational tables in databases is time-consuming and computational-intensive, and traditional SQL statements lack a convenient way to recognize patterns within these tables.
Innovation Solution
Mapping table rows to a collection of characters, allowing for pattern recognition through string processing routines that search the character collection in response to queries, enhancing data access flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional SQL statements are used to access large relational tables, then data access flexibility is maintained, but query processing becomes time-consuming and computational-intensive
Solution Approach 1:
The patent segments the large relational table into multiple smaller partitions based on the mapped character representation. By dividing the data access task into smaller segments that can be processed independently on different processors, the system achieves parallel query processing, thereby improving query processing speed while distributing computational complexity across multiple processing units
Solution Approach 2:
The patent introduces an intermediary mapping layer that converts table row identifiers into character representations. This intermediary character-based indexing system acts as a mediator between the query interface and the underlying large table, enabling faster pattern recognition and reducing the computational burden of directly accessing millions of rows in traditional relational tables
2Adaptability or versatility
If traditional SQL statements are used for data access, then flexibility in accessing data is maintained, but pattern recognition capability is limited
Solution Approach 1:
The patent changes the parameter representation from traditional row identifiers to character-based mappings. By transforming the data access paradigm from numeric row IDs to character representations, the system enables pattern recognition capabilities that leverage string matching algorithms, thereby enhancing adaptability for pattern-based queries while maintaining ease of operation through familiar character-based search interfaces
Data Source
AI summary
Table rows are mapped to corresponding characters, where the mapping produces a collection of the characters. A query is received to identify a pattern in the table rows. The collection of the characters is accessed to process the query.