Compressed Frequent Item Set Tracking for Building Event Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems face challenges in finding relationships between vast amounts of transactional data, particularly in building-related events, due to the large quantities of data generated by systems like BIM and BMS, making it difficult to identify connections between individual events and alarms.
Innovation Solution
The method involves using a Compressed Frequent Item Set (CFIS) to analyze transactional data, where new transaction sets are compared to existing sets, and counts are incremented or updated based on matches, with intersections between events being calculated to determine support and confidence values for relationships between item sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to analyze vast amounts of transactional data from BIM and BMS systems, then complete data analysis can be performed, but the processing time and computational resources required become excessively large
Solution Approach 1:
The patent segments the transactional data into transaction sets with specific structures (header, body, trailer sections) and divides the analysis into incremental updates rather than processing all data at once. This segmentation allows the system to handle vast amounts of data from BIM and BMS systems in manageable portions, reducing processing time while maintaining relationship detection accuracy.
Solution Approach 2:
The patent performs preliminary actions by pre-defining relationship rules, event types, and parameter structures before analyzing the transactional data. The system prepares templates for building events, alarms, and maintenance tasks in advance, allowing for faster processing when actual data arrives without sacrificing detection precision.
2Loss of information
If all transactional data is stored and processed in detail, then comprehensive relationship analysis is possible, but memory requirements become excessively large
Solution Approach 1:
The patent extracts only the essential elements from transactional data for storage and processing. Instead of storing complete transaction records, the system extracts key event identifiers, timestamps, and relationship-relevant parameters into a condensed format. This extraction maintains the information needed for relationship analysis while dramatically reducing memory requirements.
Solution Approach 2:
The patent inverts the traditional approach by not storing the raw data itself but storing processed relationships and patterns derived from the data. The system inverts the storage strategy to keep only the analytical results and relationship structures rather than the original voluminous transactional data, reducing memory usage while preserving analytical capabilities.
3Measurement precision
If detailed analysis of each individual event is performed, then relationship detection accuracy is improved, but the complexity of the analysis system increases
Solution Approach 1:
The patent implements universal relationship rules that can analyze multiple types of building events, alarms, and maintenance tasks using the same framework. The system uses multi-functional event templates and relationship patterns that work across different data types and sources, reducing system complexity while maintaining accurate relationship detection across diverse transactional data.
4Productivity
If incremental updates are performed on frequent item sets, then processing efficiency is improved, but ensuring data consistency becomes more difficult
Solution Approach 1:
The patent implements feedback mechanisms in the incremental update process where each update to frequent item sets is validated against consistency rules. The system provides feedback loops that check data integrity, validate relationship rules, and ensure consistency across incremental updates, maintaining reliability while preserving processing efficiency gains.
Data Source
AI summary
A new transaction set is compared to a plurality of transaction sets represented in a Compressed Frequent Item Set (CFIS), wherein the CFIS maintains a count for each transaction set represented in the CFIS. When the new transaction set matches a transaction set represented in the CFIS, the count for the matching transaction set in the CFIS is incremented. When the new transaction set does not match any transaction sets represented in the CFIS, the new transaction set is added to the CFIS. If there are intersections between two or more events of the new transaction set and the events of the plurality of transaction sets represented in the CFIS, the count for the transaction sets in the CFIS that intersect with two or more of the events of the new transaction set is incremented.