Nested Timeseries Workflows for Faster BMS Data Visualization
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Solution Overview
Problem
Building management systems (BMS) face delays in data presentation due to the need for raw timeseries data to be retrieved and processed in response to requests, leading to inefficient data processing and visualization.
Innovation Solution
A BMS that includes a data collector, a timeseries service, and a timeseries storage interface, which collects data from building equipment, applies processing workflows to generate derived timeseries, and stores them in a database, allowing for pre-aggregated data to be used for efficient querying and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If raw timeseries data is stored in a relational database without significant organization or processing at the time of data collection, then the system maintains data simplicity and storage efficiency, but applications experience significant delays when retrieving and processing data at query time
Solution Approach 1:
The system performs preliminary data processing by generating derived timeseries data from raw sensor data at the time of data collection rather than at query time. The data collector applies processing workflows to transform raw data into derived data with specific aggregations, transformations, or calculations already performed. This preliminary action eliminates the need for applications to retrieve and process raw data, significantly reducing query-time delays while maintaining system simplicity through automated processing.
2Productivity
If derived timeseries data is generated and stored in advance, then data presentation speed is improved, but the system requires additional processing resources and storage capacity
Solution Approach 1:
The system applies local quality by storing multiple versions of the same timeseries data with different processing characteristics. Rather than uniformly processing all data the same way, the data collector generates derived timeseries data with specific local transformations (aggregations, filters, calculations) tailored to particular application needs. This allows efficient data presentation for specific queries while maintaining storage efficiency by only creating derived versions when and where needed.
3Adaptability or versatility
If multiple processing workflows are applied to generate nested derived timeseries, then data versatility and analytical capability are enhanced, but the complexity of data management increases
Solution Approach 1:
The system implements nested doll by creating hierarchical levels of derived timeseries data where second-level derived data is generated from first-level derived data, which itself was generated from raw sensor data. The data collector identifies processing workflows that operate on previously generated derived timeseries, applying additional transformations in a nested manner. This nested structure provides versatile data processing capabilities while managing complexity through organized, layered processing stages.
Data Source
AI summary
A building management system (BMS) includes building equipment configured to provide samples of one or more data points in the building management system and a timeseries service. The timeseries service is configured to identify a first timeseries processing workflow that uses an input timeseries as an input and defines processing operations to be applied to the samples of the input timeseries, perform the processing operations defined by the first timeseries processing workflow to generate a first derived timeseries comprising a first set of derived timeseries samples, identify a second timeseries processing workflow that uses the first derived timeseries as an input and defines processing operations to be applied to the samples of the first derived timeseries, and perform the processing operations defined by the second timeseries processing workflow to generate a second derived timeseries comprising a second set of derived timeseries samples.