Bounded Query Rewriting Using Cached Views
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Solution Overview
Problem
Existing search algorithms for big datasets are computationally intensive and time-consuming, requiring significant resources as they scale with the size of the dataset, making it challenging to achieve scale independence and efficient query answering.
Innovation Solution
The method involves obtaining a query and access constraints, selecting a cached view, generating a query rewriting plan by adding fetch, database, and access view operations, and executing it to fetch and update results without reapplying the query to the dataset, allowing for scale-independent searching by leveraging cached views and access constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If search algorithms are applied to big datasets, then query results can be obtained, but computational intensity and time consumption increase significantly
Solution Approach 1:
The patent pre-computes and stores query results in cached views before they are needed. When a query is received, the system checks if results are already available in the cached views, eliminating the need to re-execute computationally intensive search algorithms on big datasets, thus dramatically reducing query execution time while maintaining result accuracy
Solution Approach 2:
The patent creates cached views as copies of query results from the original big dataset. These cached views contain pre-computed data that can be quickly retrieved and updated without reprocessing the entire dataset, reducing the computational burden and time required for subsequent queries
2Measurement precision
If search algorithms scale with dataset size, then comprehensive results are achieved, but scale independence is lost
Solution Approach 1:
The patent segments the big dataset into multiple cached views, each storing results for specific query patterns or data subsets. This segmentation allows the system to handle large datasets by working with smaller, manageable cached views that are independent of the overall dataset size, achieving scale independence while maintaining query completeness through selective view updates
Solution Approach 2:
The patent changes the parameter of data storage from keeping the entire big dataset in memory to storing selective query results in cached views. This parameter change enables the system to achieve scale independence by bounding the memory usage to the size of cached views rather than the full dataset size, while still providing comprehensive results through intelligent view selection and updates
3Measurement precision
If queries are reapplied to the entire dataset, then updated results are obtained, but computational burden increases
Solution Approach 1:
The patent extracts and caches only the specific query results that are needed, rather than maintaining and reprocessing the entire dataset. When updates are required, the system extracts only the changed data portions and updates the relevant cached views, significantly reducing computational resource usage while maintaining result accuracy
Solution Approach 2:
The patent maintains continuous availability of query results through cached views that are updated incrementally as data changes occur in the underlying dataset. This continuous maintenance approach avoids the need to reapply queries to the entire dataset periodically, reducing computational burden while ensuring result accuracy through incremental updates
Data Source
AI summary
A mechanism for rewriting a bounded query onto a view is disclosed. A query, access constraints, and cached view(s) are obtained. The cached view(s) contain results of the query as applied to a dataset stored in a database. A query rewriting plan is generated by adding a fetch from query operation to the plan when a rewritten query variable can be fetched from a query constant. A fetch from database operation is added to the plan when a rewritten query variable can be fetched as a query variable that obtains bounded data from the database. An access view operation is added to the plan when a rewritten query variable can be fetched from the cached view. The query rewriting plan is executed to fetch retrieved data. The query is executed on the retrieved data to determine updated results to the query without reapplying the query to the dataset.