Directed Data Indexing via Conceptual Relevance Analysis

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Solution Overview

Problem

Current data collection and indexing systems consume significant bandwidth, time, and storage resources, and often fail to capture the most relevant data for generating effective conceptual indexes due to the lack of directional data collection based on conceptual relevance.

Innovation Solution

Implementing a data connector and analysis engine that dynamically collect and index data by assessing its relevance to specific concepts, discarding irrelevant data and using references to access relevant data sources, thereby optimizing data collection and indexing processes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If data is collected from all available data sources without filtering, then the quantity of indexed data increases, but bandwidth consumption, time consumption, and storage requirements increase significantly

Engineering Contradiction:

Improvequantity of indexed dataVSAvoidbandwidth consumption

Core Design Contradiction:

Quantity of substanceVSLoss of energy

Solution Approach 1:

The system performs preliminary analysis of collected data to determine conceptual relevance to the target concept before proceeding with full indexing. The analysis engine evaluates whether data from a data source is relevant to the concept, and only relevant data is subsequently indexed. This preliminary filtering action prevents wasteful consumption of bandwidth and storage resources on irrelevant data.

Inventive Principle:

Principle #10Preliminary action

2Quantity of substance

If data is collected from all available data sources without filtering, then the quantity of indexed data increases, but the time required for data collection and indexing increases

Engineering Contradiction:

Improvequantity of indexed dataVSAvoiddata collection time

Core Design Contradiction:

Quantity of substanceVSLoss of time

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system extracts and processes only the relevant portions of data that pertain to the target concept, discarding irrelevant data. The analysis engine identifies and extracts conceptually relevant information from data sources, and the indexing system processes only this extracted relevant data, significantly reducing the time required for data collection and indexing while maintaining data quality.

Inventive Principle:

Principle #2Taking out (Extraction)

3Quantity of substance

If data is collected from all available data sources without filtering, then the quantity of indexed data increases, but storage requirements increase

Engineering Contradiction:

Improvequantity of indexed dataVSAvoidstorage requirements

Core Design Contradiction:

Quantity of substanceVSVolume of stationary object

Solution Approach 1:

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system discards irrelevant data that does not pertain to the target concept, eliminating unnecessary storage requirements. The analysis engine determines conceptual relevance, and data deemed irrelevant is discarded rather than stored, while relevant data is retained and indexed for future retrieval, optimizing storage resource utilization.

Inventive Principle:

Principle #34Discarding and recovering

4Quantity of substance

If conventional data collection methods are used without conceptual filtering, then all data is captured, but the relevance and usefulness of indexed data for conceptual searches decreases

Engineering Contradiction:

Improvequantity of indexed dataVSAvoidrelevance of indexed data

Core Design Contradiction:

Quantity of substanceVSLoss of information

Solution Approach 1:

The system changes the parameter of data selection from quantity-based to quality-based indexing. Instead of indexing all data regardless of relevance, the analysis engine evaluates the conceptual relevance of each data source to the target concept, and the indexing system adjusts its behavior to index only data that meets the relevance threshold, thereby improving the overall quality and usefulness of the indexed data for conceptual searches.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system uses feedback from the analysis engine regarding conceptual relevance to guide the data collection and indexing process. The analysis engine continuously evaluates data sources and provides feedback on their relevance to the target concept, which then informs the indexing system's decisions about what data to collect and index, ensuring that the indexed data remains highly relevant and useful for conceptual searches.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS11726972B2Directed data indexing based on conceptual relevance

Publication Date: 2023.08.15 MICRO FOCUS LLC

AI summary

A non-transitory machine-readable storage medium stores instructions that upon execution cause a processor to, in response to initiation of a data indexing for a search concept, retrieve content of a first data source via a data connector, the retrieved content including a reference to a second data source. The instructions further cause the processor to, in response to a determination that the retrieved content of the first data source is relevant to the search concept: index the retrieved content of the first data source; retrieve content of the second data source based on the reference; and determine whether the retrieved content of the second data source is relevant to the search concept.