Abstract Data Type Search Engine with Skimming and Approximate Retrieval
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Solution Overview
Problem
Existing search and management systems for abstract data types lack efficient querying capabilities and flexibility, particularly in preserving relative order and allowing for easy updates, which is crucial for diverse data types like medical data storage.
Innovation Solution
A generalized search engine framework that indexes abstract data type definitions as field-value pairs, enabling powerful querying and updates, with features like skimming and approximate retrieval, and supports various data models through a uniform strategy for document indexing and querying.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing search and management systems are used for abstract data types, then basic storage is achieved, but querying capabilities are inefficient and flexibility is limited
Solution Approach 1:
The system segments data into abstract data type definitions (ADTs) with field-value pairs, allowing independent indexing and querying of individual fields. This segmentation enables efficient querying by allowing the search engine to access only relevant portions of data rather than scanning entire records, thereby improving querying efficiency while maintaining flexibility in how data can be queried and updated.
Solution Approach 2:
The patent creates a universal search engine framework that handles multiple data types through a common ADT structure. The system can index and query diverse data types (medical data, text, numerical data) using the same field-value pair mechanism, improving adaptability while maintaining efficient querying through consistent indexing strategies across different data types.
2Stability of the object's composition
If relative order is preserved in data structures, then data integrity is maintained, but system complexity increases
Solution Approach 1:
The system performs preliminary indexing of field-value pairs with their positional information during data ingestion. By pre-processing and storing order information in the index structure itself, the system maintains relative order without requiring complex runtime operations. The index is constructed beforehand to reflect the hierarchical and sequential relationships, reducing system complexity during query operations while preserving data integrity.
3Reliability
If data models are made rigid for consistent querying, then query reliability improves, but ease of updates decreases
Solution Approach 1:
The patent implements dynamic data models where abstract data type definitions can be modified without rigid schema constraints. The field-value pair structure allows new fields to be added and existing fields to be modified while maintaining query reliability through consistent indexing. The system dynamically adapts to schema changes by updating the index structure, enabling easy updates while preserving reliable querying through the maintained field-value relationships.
Data Source
AI summary
A generalized search engine is provided for abstract data types with skimming and approximate retrieval. According to various embodiments, an abstract data type definition is generated. The abstract data type definition comprises a plurality of field-value pairs. The abstract data type definition is indexed in an index. A document definition is generated. The document definition comprises at least one key value, and further comprises the abstract data type definition. The document definition is indexed in the index.