In-Memory Engine for Search Integration
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Solution Overview
Problem
Computer systems face performance degradation due to the significant difference in data retrieval times between Random Access Memory (RAM) and disk storage, leading to slower data access and processing times for large datasets.
Innovation Solution
A computer system is designed to provide a natural language interface that customizes query processing by using contextual information to generate structured queries, integrate structured and unstructured data, and utilize an in-memory engine for faster data retrieval and analytics, enabling dynamic search query generation and incremental results retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in disk storage, then storage capacity is increased, but data retrieval time increases significantly
Solution Approach 1:
The patent segments data into hot data (frequently accessed) and cold data (infrequently accessed), storing hot data in RAM and cold data on disk. This segmentation allows the system to maintain large storage capacity while ensuring fast retrieval for commonly accessed data.
Solution Approach 2:
The patent introduces an intermediary component (cache memory or buffer) between disk storage and the processing system. This intermediary holds frequently accessed data, acting as a mediator that provides fast access to hot data while maintaining the ability to store large volumes of cold data on disk.
2Adaptability or versatility
If more data is processed, then analytical capabilities are improved, but processing speed decreases
Solution Approach 1:
The patent changes the physical state parameter of data by loading frequently accessed datasets into RAM, transforming them from disk-resident to memory-resident. This parameter change enables faster processing speeds for analytical operations while still maintaining the capability to work with large volumes of data.
Solution Approach 2:
The patent performs preliminary action by pre-loading datasets that are likely to be accessed into RAM before they are actually needed for processing. This anticipatory loading ensures that when analytical operations are initiated, the data is already in fast memory, maintaining processing speed.
Data Source
AI summary
Search integration is described. The actions include receiving, by one or more processors, a query. The actions include identifying search results that are responsive to the query. The actions include identifying, based on the query and the search results, structured data to supplement the search results, wherein the structured data is generated from binary table data that is received and deserialized by one or more processing modules. The actions include processing the structured data and the search results. The actions include providing, for output, the processed search results and the processed structured data results.