Object-Based Search Retrieval for Structure-Independent Data Access
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Solution Overview
Problem
Maintaining and searching large volumes of data in tables or databases is costly, time-consuming, error-prone, and heavily dependent on the underlying data structure, leading to cascading effects when changes occur, and existing search engines complicate internal applications with format-specific logic.
Innovation Solution
Transform structured data into object data, which is organized in memory as an object instance with embedded search functions, allowing efficient and processor-friendly search and retrieval without relying on the original data structure, enabling quick access and updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is maintained in traditional tables or databases, then search and retrieval can be performed, but the process becomes costly, time-consuming, and error-prone
Solution Approach 1:
The patent pre-generates and maintains multiple indices for each data element, including exact match indices, prefix indices, and inverted indices, before search operations are performed. This preliminary organization of data allows for extremely fast retrieval without requiring complex search algorithms to run on the raw data, thus reducing search time while maintaining accuracy
Solution Approach 2:
The patent introduces an intermediary indexing layer between the raw data and the search operation. Instead of searching directly in tables or databases, the search engine queries pre-built indices that mediate between the data and search requests, significantly accelerating retrieval while preserving data accuracy
2Productivity
If traditional search engines are used, then data can be searched, but the underlying data structure heavily influences response times
Solution Approach 1:
The patent creates a universal search engine architecture that automatically generates and maintains multiple types of indices (exact match, prefix, inverted, fuzzy) for any data structure. This universal approach allows the same search engine to efficiently handle different data formats and structures without requiring structure-specific optimization, thus improving both speed and adaptability
3Ease of operation
If format-specific logic is embedded in internal applications, then search functionality is achieved, but applications become complicated and dependent on table formats
Solution Approach 1:
The patent extracts search functionality from internal applications and consolidates it into a separate, dedicated search engine component. The search engine handles all search operations independently using its index-based architecture, freeing applications from embedding format-specific search logic. This reduces application complexity while maintaining ease of operation
Solution Approach 2:
The patent introduces the search engine as an intermediary layer between applications and data storage. Applications interact with the search engine through standardized interfaces rather than directly querying tables or databases, eliminating the need for applications to understand or adapt to underlying data formats, thus simplifying application design
4Adaptability or versatility
If changes are made to table structures, then data organization is updated, but cascading effects occur on internal applications
Solution Approach 1:
The patent pre-generates multiple types of indices for each data element and maintains them automatically as data changes. When table structures are updated, the search engine's index regeneration mechanism automatically adapts without requiring application changes, preventing cascading effects while maintaining system stability
Solution Approach 2:
The search engine acts as an intermediary buffer between data storage changes and applications. It absorbs the impact of structural changes through its automatic index regeneration, shielding applications from cascading effects while maintaining flexibility to adapt to new data formats
Data Source
AI summary
Structured data comprising primary data is transformed into object-based data comprising the primary data and at least one search function. The object-based data when loaded into memory provides the search function as executable instructions for an object instance. When an application calls the object instance with search terms as input, the search terms are located within the primary data (which is loaded into memory) by the search function, and the object instance returns corresponding field values from the primary data associated with the search terms to the application.