BMS Virtual Points and Data Rollups for Faster Timeseries Retrieval
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Solution Overview
Problem
Building management systems (BMS) face delays in data presentation due to the need for applications to retrieve and process raw timeseries data from relational databases, leading to inefficient data retrieval and visualization.
Innovation Solution
Implementing a BMS with a data collector that aggregates raw data samples into rollup timeseries, stored in a timeseries database, allowing for efficient retrieval and processing, along with a virtual point calculator to create virtual data points and a scalable rules engine for fault detection, optimizing data integration and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If raw timeseries data is stored in a relational database without significant organization or processing, then data storage is simple, but data retrieval and processing time is significantly delayed
Solution Approach 1:
The system performs preliminary aggregation of raw data samples into rollup timeseries data during data collection, rather than waiting until query time. This pre-processing creates organized, aggregated data structures that can be quickly retrieved and presented, eliminating the time delay that would occur if processing were done at query time while maintaining simple storage through the use of a timeseries database
2Measurement precision
If applications retrieve and process raw timeseries data in response to requests, then data accuracy is maintained, but data presentation is significantly delayed
Solution Approach 1:
The system pre-aggregates raw data samples into rollup timeseries during data collection phases, creating processed data structures in advance. When applications request data, they receive pre-processed rollup timeseries that maintains accuracy while enabling immediate presentation without the need for time-consuming processing at query time
Solution Approach 2:
The system segments data into two types: raw data samples for complete accuracy and rollup timeseries for efficient presentation. This segmentation allows applications to use pre-processed aggregated data for most presentation needs while maintaining the option to access raw data when maximum precision is required, thus balancing accuracy with presentation speed
3Productivity
If data is aggregated and pre-processed, then data presentation speed is improved, but system complexity increases
Solution Approach 1:
The system implements automatic aggregation where the data collector automatically generates rollup timeseries from raw data samples without requiring manual intervention or complex external processing systems. This self-service approach to data aggregation simplifies the overall system architecture while still achieving fast data presentation through pre-processed rollup timeseries
Data Source
AI summary
A building management system (BMS) includes building equipment, a data collector, a sample aggregator, and an application. The building equipment is operable to monitor and control a variable in the BMS and to provide raw data samples of the variable. The data collector is configured to collect the raw data samples from the building equipment and generate a raw data timeseries including a plurality of the raw data samples. The sample aggregator is configured to automatically generate a data rollup timeseries including a plurality of aggregated data samples. The aggregated data samples are generated by aggregating the raw data samples as the raw data samples are collected from the building equipment. Both timeseries are stored in a timeseries database. The application is configured to retrieve the raw data timeseries and the data rollup timeseries from the timeseries database in response to a request for timeseries data associated with the variable.