Multi-Stage Data Aggregation for Non-Cascadable Operations
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Solution Overview
Problem
Existing Online Analytical Processing (OLAP) techniques face inefficiencies in aggregating multidimensional data due to the high computational costs of pre-computing all possible aggregates, especially when dealing with non-cascadable operations and functional dependencies, which can lead to incorrect results and excessive data processing.
Innovation Solution
A method for multi-stage data aggregation that takes advantage of functional dependencies between fields, decomposing aggregation computations into multiple stages using non-cascadable operations such as determining distinct values, median, sum of extrema, and extremum of sums, and employing Cartesian products and join operations with wildcard values to reduce the size of the aggregation computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If pre-computation of all possible aggregates is performed to increase query efficiency, then query response time is improved, but computational cost and data processing time increase significantly
Solution Approach 1:
The patent segments the aggregation computation into multiple stages based on functional dependencies between fields. Instead of computing all possible aggregates in a single pass, the computation is divided into sequential stages where each stage processes a subset of aggregates, reducing the computational burden at each step while still achieving the desired query results.
Solution Approach 2:
The patent performs preliminary identification of functional dependencies between fields before executing the aggregation computation. This preliminary action allows the system to determine which aggregates can be computed in advance and which require later stages, optimizing the overall computation plan and reducing unnecessary processing.
2Productivity
If multi-stage aggregation is used to reduce computational burden, then data processing efficiency is improved, but system complexity increases
Solution Approach 1:
The system is segmented into multiple processing stages, each handling a specific subset of aggregation computations. This segmentation reduces the complexity of individual stages while maintaining overall productivity through coordinated multi-stage processing.
Solution Approach 2:
The patent implements dynamic stage execution based on the specific query requirements and functional dependencies identified. Not all stages are executed for every query - the system dynamically determines which stages are necessary, reducing actual system complexity while preserving the capability for efficient multi-stage processing when needed.
3Measurement precision
If non-cascadable operations are applied directly to all records, then measurement precision is maintained, but computational cost increases
Solution Approach 1:
The patent segments the application of non-cascadable operations across multiple stages, applying them only where necessary based on functional dependencies. This selective segmentation maintains measurement precision by ensuring operations are applied correctly while reducing overall computational cost by avoiding redundant applications.
Solution Approach 2:
Different processing approaches are applied to different parts of the data based on local functional dependencies. Non-cascadable operations are applied with appropriate precision only where required by the specific data relationships, rather than uniformly across all records, optimizing the balance between accuracy and computational cost.
Data Source
AI summary
Aggregating data includes accepting a first data set that includes records, each record holding a value for each of a plurality of fields. A second data set is generated from the first data set. The second data set includes one or more aggregated records each corresponding to one or more records from the first data set that match values in a subset of fields. A third data set is generated from the second data set. The third data set includes one or more aggregated records each corresponding to one or more aggregated records of the first data set that match values in a subset of fields. An aggregate value associated with an aggregated record in the third data set represents a result of performing a non-cascadable operation on values associated with a plurality of records from the first data set.