Scalar Index Co-occurrence Computation via Document ID Chaining
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Solution Overview
Problem
Existing techniques are inadequate for efficiently processing n-way co-occurrences of data tuples in scalar indexes, which are essential for advanced search queries across multiple document attributes.
Innovation Solution
A method involving a computer-readable storage medium with executable instructions that chains scalar indexes through document identification values to form sub-tuples, which are then combined to generate tuples responsive to queries, including frequency indicia, by scanning primary, secondary, and tertiary indexes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing techniques are used to process n-way co-occurrences of data tuples in scalar indexes, then the processing capability is limited, but the system complexity and processing time increase for advanced search queries across multiple document attributes
Solution Approach 1:
The patent segments the complex n-way co-occurrence processing into multiple independent scalar indexes, where each index handles a specific attribute or dimension. This allows parallel processing of different attribute combinations, significantly improving productivity while reducing processing time through distributed computation across multiple index structures.
Solution Approach 2:
The patent performs preliminary actions by pre-computing and storing co-occurrence data in scalar indexes before queries are executed. This pre-processing creates ready-to-use index structures that enable rapid retrieval during actual search operations, eliminating the need for complex real-time computations and reducing query processing time.
2Adaptability or versatility
If advanced search queries across multiple document attributes are implemented, then query functionality is enhanced, but processing efficiency decreases
Solution Approach 1:
The patent creates a universal scalar index structure that can handle multiple document attributes simultaneously through a standardized tuple format. Each scalar index is designed to be multi-functional, capable of processing different attribute types (text, numeric, date) through the same co-occurrence computation mechanism, thereby enhancing query versatility without sacrificing processing efficiency.
3Measurement precision
If co-occurrences of data tuples are computed efficiently, then query accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent introduces scalar indexes as intermediary structures that mediate between raw document data and final co-occurrence results. These indexes serve as intermediate computational layers that simplify the complexity by pre-organizing data in a structured format, enabling accurate co-occurrence computation without directly handling the full complexity of raw document relationships.
Data Source
AI summary
A non-transitory computer readable storage medium includes executable instructions to receive primary matched values with corresponding document identifications. The primary matched values correspond to a specified primary value, for example expressed in a search query. A secondary scalar index is scanned to match the document identifications with secondary matched values with corresponding secondary values. A tertiary scalar index is scanned to match the document identifications with tertiary matched values with corresponding tertiary values. The secondary values and tertiary values are provided as tuples. Each tuple has the specified primary value, a secondary value, a tertiary value and frequency indicia.