Dynamic Query Phase Generation for Natural Language Processing
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Solution Overview
Problem
Current data search methodologies are inefficient, leading to user frustration and wastage of system resources due to the challenge of navigating vast data collections and extracting relevant insights from diverse and incompatible data sources.
Innovation Solution
The automated generation of narrated analytics playlists, which curates data from multiple sources, identifies insights using machine-learning algorithms, and presents them in a summarized and contextualized manner, reducing the burden on users and system resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated phase generation and resource load reduction is implemented, then query processing efficiency is improved, but system complexity increases
Solution Approach 1:
The query processing system is divided into multiple phases (identification phase, expansion phase, execution phase) that are generated and executed separately. Each phase handles specific aspects of query processing, allowing the system to manage complexity through modular organization while improving overall processing efficiency through targeted optimization at each stage.
Solution Approach 2:
The system performs preliminary actions by pre-identifying and pre-expanding query phases before actual execution. Query phases are identified and expanded in advance, with resource loads reduced beforehand, so that when queries are executed, the heavy lifting of analysis and preparation has already been completed, improving runtime efficiency.
2Loss of information
If comprehensive data analysis is performed to provide targeted insights, then information quality is improved, but computational resource consumption increases
Solution Approach 1:
The system extracts and focuses only on the most relevant insights and information from comprehensive data analysis. By using phase expansion to identify and extract key query components and using filtering mechanisms to remove redundant information, the system delivers high-quality targeted insights while avoiding the computational cost of processing all possible data.
Solution Approach 2:
The system changes parameters such as phase expansion depth, resource allocation thresholds, and insight prioritization criteria to balance information quality against computational consumption. By dynamically adjusting these parameters based on query complexity and resource availability, the system optimizes the trade-off between providing comprehensive insights and consuming computational resources.
Data Source
AI summary
Techniques are described for dynamic phase generation and load reduction for a query. A query, for instance, is based on user input of a query in a natural language (NL) form, e.g., an NL query. Generally, an NL query may include multiple terms and/or phrases that make up a complex query, such as a sentence in a human-readable language. Accordingly, to enable a query result to be generated, the NL query is parsed into multiple logical sections and query contexts are determined for the logical sections. A set of search phases is generated based on the logical sections and the query contexts. The search phases can then be executed in a specific execution order to generate a query result for the NL query.