Event Forecasting via Partitioned BitSets and Logical Operations
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Solution Overview
Problem
Existing systems face inefficiencies in quickly determining and forecasting the number of events that match specific criteria and calculating accurate average aggregate values, due to the use of brute-force methods that are slow and inefficient.
Innovation Solution
The system preprocesses events into metadata and BitSets, allowing for logical operations to be performed on these BitSets to efficiently forecast matching results, reducing complexity and enabling fast computation of event counts and average aggregate values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If brute-force method is used to match all events with criteria, then complete evaluation is achieved, but processing speed and efficiency deteriorate
Solution Approach 1:
The patent segments the event dataset into multiple partitions and processes each partition independently. This divides the large-scale brute-force evaluation into smaller, parallelizable units, maintaining complete evaluation coverage while significantly improving processing throughput and efficiency.
Solution Approach 2:
The patent performs preliminary actions by pre-computing metadata and BitSets for events before the actual criterion evaluation. This preprocessing step organizes data in advance, enabling faster matching operations during the evaluation phase without sacrificing completeness.
2Measurement precision
If brute-force method is used to evaluate events, then accurate forecasting is achieved, but computational complexity increases
Solution Approach 1:
The patent changes the representation parameters of events by converting them into BitSets and metadata structures. This parameter transformation enables efficient logical operations while preserving the accuracy needed for forecasting, thereby reducing computational complexity without sacrificing measurement precision.
Solution Approach 2:
The patent substitutes the mechanical brute-force comparison approach with a more efficient system based on logical operations on BitSets. This replacement maintains forecasting accuracy while dramatically reducing the computational complexity of event evaluation.
3Reliability
If all events are processed to determine matching results, then complete criterion evaluation is achieved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing events into metadata and BitSets, organizing the data structure in advance. This allows the system to quickly evaluate criteria against pre-organized data, achieving complete criterion evaluation while minimizing processing time during the actual query phase.
Solution Approach 2:
The patent segments the event processing into distinct phases: pre-processing into partitions with metadata, and then criterion evaluation. This segmentation allows complete evaluation to be achieved systematically while reducing overall processing time through parallelization and efficient data organization.
Data Source
AI summary
A method and system provide the ability to forecast events that match a criterion. Input is received and includes an event set and a criterion. The event set includes events and each of the events is a record with features. Each feature is an instance of a feature type (FT) and feature value (FV) combination. The criterion is a logical statement that specifies a criterion FT, a criterion FV, and an operator. The event set is partitioned and a partition BitSet is built for each partition. Each partition length of each partition BitSet is based on a number of the events. Based on the partition BitSets, a partition FT-FV count is created. For each partition, a partition FT criterion BitSet is created and a partition Criterion BitSet is computed based thereon. The forecast is then computed based on the partition Criterion BitSet.