Top Operator Ranking Operations in Query Processing
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Solution Overview
Problem
Current database systems face inefficiencies in processing large data sets that exceed available memory, requiring frequent data transfer between main memory and disk, and struggle to efficiently execute complex queries that demand real-time data analysis, which is crucial for competitive decision-making in today's economic climate.
Innovation Solution
The implementation of a calculation engine with a top operator that sorts and processes data sets by aggregating values until a target sum is reached, allowing for efficient ranking and selection of top data rows based on cumulative sum, cumulative percent, or olympic ranking criteria, thereby optimizing data processing and reducing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If large data sets are loaded into memory for processing, then data analysis speed is improved, but memory requirements increase beyond available capacity
Solution Approach 1:
The patent segments the large data set into multiple groups based on base attributes, processing each group separately through the top operator. This allows the system to handle data larger than available memory by dividing it into manageable chunks that can be processed sequentially, resolving the contradiction between speed and memory requirements.
Solution Approach 2:
The patent introduces a hierarchical dimension to data processing by organizing data into groups based on base attributes and applying the top operator at different levels. This dimensional approach allows efficient processing of large datasets by navigating through hierarchical structures rather than loading everything into memory simultaneously.
2Reliability
If complex queries are executed to provide comprehensive data analysis, then decision-making quality is improved, but resource consumption increases
Solution Approach 1:
The top operator extracts only the necessary top-ranked rows from each group based on cumulative sum, cumulative percent, or olympic ranking criteria. This extraction principle allows the system to provide comprehensive analysis results without processing or transferring unnecessary data, thereby reducing resource consumption while maintaining decision-making quality.
Solution Approach 2:
The patent applies partial action by retrieving only the required portion of data (top N rows per group) rather than all data. This partial retrieval satisfies the need for comprehensive analysis while significantly reducing the computational resources required for data transport and processing.
3Adaptability or versatility
If all attributes are exposed in calculation scenarios for flexibility, then query adaptability is improved, but data processing complexity increases
Solution Approach 1:
The patent applies local quality by exposing different sets of attributes at different levels of the calculation scenario hierarchy. Base attributes are used for grouping while top attributes are used for ranking and selection. This localized attribute exposure provides query adaptability without requiring all attributes to be processed simultaneously, reducing overall complexity.
Data Source
AI summary
Described herein is a top operator of a calculation scenario of a calculation engine that can process and rank data according to certain logical criterion requested in a query. Based on the ranked data, the top operator can provide the data results in response to the query. The top operator can provide additional useful data in the data results when certain conditions are met. The top operator can provide the data results in a useful and efficient manner.