Content Addressable Query Results for Disparate Database Federation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional data storage and computing technologies face challenges in facilitating interoperability among disparate datasets due to differences in computing platforms, database technologies, and data formats, leading to inefficiencies in querying and data access, particularly as datasets grow in size and complexity.
Innovation Solution
A query engine is configured to generate and implement content addressable query results, allowing for the identification and retrieval of relevant data from multiple query result generation data sources without executing architecture-specific queries, by storing query results in memory and using content addressable query data to match query requests, thereby optimizing data operations and reducing computational resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple architecture-specific queries are executed against different database technologies and formats, then complete query results can be obtained from disparate data sources, but computational resources are consumed and query performance deteriorates
Solution Approach 1:
The system pre-generates and stores query results in a cache memory before they are actually needed. When a query is received, the system checks the cache first and returns stored results if available, avoiding the need to execute multiple architecture-specific queries against disparate database sources. This preliminary action of caching results resolves the contradiction by providing complete results (reliability) without consuming computational resources at query time.
2Adaptability or versatility
If multiple architecture-specific queries are executed to access data from different computing platforms and database technologies, then data interoperability among disparate datasets is achieved, but query execution time increases
Solution Approach 1:
The system creates copies of query results from multiple disparate data sources and stores them in a centralized cache memory. Instead of executing multiple architecture-specific queries against different computing platforms and database technologies each time data is needed, the system copies results once and serves them from cache. This copying approach maintains data interoperability (adaptability) while dramatically reducing query execution time by avoiding repeated access to external databases.
3Speed
If query results are cached in memory for fast access, then data access speed is improved, but memory resources are consumed
Solution Approach 1:
The system changes the state of query results by storing them in a cached format in memory with associated metadata including content addressable query data for identification. By transforming query results into a cached, addressable format with proper indexing, the system achieves fast data access speed while efficiently utilizing memory resources through structured storage and retrieval mechanisms.
Data Source
AI summary
Various embodiments relate generally to data science and data analysis, computer software and systems, and wired and wireless network communications to interface among repositories of disparate datasets and computing machine-based entities configured to access datasets, and, more specifically, to a computing and data storage platform to implement computerized tools to facilitate expedited queries based on query results generated by disparate computing and database architectures, according to at least some examples. For example, a method may include generating multiple results of queries, storing the multiple results of queries in a memory, and linking each of the multiple results of queries to a portion of data stored in a graph. The method can include receiving data representing a query and accessing data representing quiescent data values to form a query result.