Scalar Interval Lookup via Sorting and Overlap Detection
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Solution Overview
Problem
Existing systems for evaluating scalar intervals in decision management systems are inefficient, leading to redundant evaluations and suboptimal execution performance due to lack of effective sorting and processing methods for overlapping and non-overlapping intervals.
Innovation Solution
The system sorts scalar intervals based on their left bounds and identifies overlaps to process them using optimal methods such as binary search for non-overlapping intervals and sequential search for overlapping intervals, transforming rules into a directed acyclic graph (DAG) and grouping similar branches to enhance execution efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If scalar intervals are processed using conventional evaluation methods, then the system can handle decision management queries, but the execution performance is suboptimal due to redundant evaluations
Solution Approach 1:
The patent applies preliminary action by sorting scalar intervals based on left bounds before processing queries. This pre-processing step organizes the intervals in advance, enabling efficient binary search operations during query execution and eliminating redundant evaluations by ensuring each scalar value is evaluated against the minimal necessary intervals.
2Reliability
If the system evaluates all scalar intervals for each query, then complete accuracy is achieved, but storage and memory usage increase
Solution Approach 1:
The patent applies segmentation by dividing scalar intervals into overlapping and non-overlapping groups based on comparison results. This segmentation allows the system to store and process only the necessary interval subsets for each query type, reducing overall storage requirements while maintaining evaluation accuracy through targeted processing of relevant interval groups.
3Productivity
If conventional processing methods are used for all scalar intervals, then implementation is simple, but runtime performance is suboptimal
Solution Approach 1:
The patent applies local quality by selecting different processing methods based on the specific characteristics of scalar interval groups. Non-overlapping intervals are processed using efficient binary search, while overlapping intervals require sequential evaluation. This localized adaptation of processing methods optimizes runtime performance for each interval type without unnecessarily complicating the overall system.
4Productivity
If scalar intervals are not sorted, then processing is straightforward, but redundant evaluations occur
Solution Approach 1:
The patent applies preliminary action by implementing sorting based on left bounds as a pre-processing step before query evaluation. This initial sorting investment eliminates redundant evaluations during runtime by organizing intervals in a structure that enables efficient binary search and targeted processing, reducing the total number of comparisons needed across multiple queries.
Data Source
AI summary
In one aspect, a computer implemented method for efficient value lookup in a set of scalar intervals is provided. The method includes determining, in response to a query for a scalar value, that the scalar value is located in a set of scalar intervals, wherein each of the scalar intervals comprises a left bound and a right bound. The method further includes sorting the scalar intervals based on left bounds. The method further includes comparing, in response to the sorting, a pair of scalar intervals to determine if the pair of scalar intervals overlaps. The method further includes identifying, based on the comparing indicating that the pair overlaps, a method of processing the scalar intervals.