BMS Timeseries Workflows for Faster Building Data Queries
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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 visualization and query times.
Innovation Solution
A BMS that includes a data collector to gather raw data samples, a timeseries processing engine to apply predefined workflows for processing and generating derived data timeseries, and a storage interface to store both raw and derived data in a timeseries database, allowing for pre-processing and optimization of workflows to reduce query time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If raw timeseries data is stored without significant organization or processing at data collection time, then data storage simplicity is improved, but data presentation delay increases due to processing required at query time
Solution Approach 1:
The system performs preliminary processing of raw timeseries data at data collection time by identifying applicable workflows and generating derived data timeseries in advance. The timeseries processing engine processes raw data through predefined workflows to create aggregated, filtered, or transformed derived data that is stored alongside raw data, eliminating the need for processing at query time and thus reducing data presentation delay.
2Device complexity
If data processing is performed at query time, then data storage complexity is reduced, but query response time deteriorates due to processing delays
Solution Approach 1:
The system pre-processes raw timeseries data by applying predefined workflows at data collection time rather than at query time. The timeseries processing engine identifies workflows that apply to each raw data timeseries and generates derived data timeseries in advance, storing both raw and derived data in the database. This preliminary action significantly reduces query response time as applications can directly retrieve pre-processed derived data without waiting for processing at query time.
3Adaptability or versatility
If multiple processing steps are performed at query time, then data processing flexibility is improved, but overall system performance deteriorates due to processing delays
Solution Approach 1:
The system performs multiple processing steps in advance by applying predefined workflows at data collection time. The timeseries processing engine executes multiple processing operations including aggregation, filtering, and transformation to generate derived data timeseries before they are needed. This preliminary execution of multiple processing steps maintains system flexibility through configurable workflows while dramatically improving overall productivity by eliminating processing delays at query time.
Data Source
AI summary
A building management system (BMS) includes building equipment configured to provide raw data samples of one or more data points in the BMS. The BMS further includes a data collector configured to collect raw data samples from the building equipment and generate one or more raw data timeseries comprising a plurality of the raw data samples. The BMS also includes a timeseries processing engine. The timeseries processing engine is configured to identify one or more timeseries processing workflows that apply to the raw data timeseries, each of the workflows comprising a predefined sequence of timeseries processing operation. The timeseries processing engine is further configured to process the raw data timeseries using the identified timeseries processing workflows to generate one or more derived data timeseries. The BMS further includes a timeseries storage interface configured to store the raw data timeseries and the derived data timeseries in a timeseries database.