BMS Nested Timeseries Workflows for Faster 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, a timeseries service, and a timeseries storage interface, which processes and stores data by applying workflows to generate derived timeseries data, allowing for pre-aggregation and efficient querying, and storing this data in a timeseries database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If raw timeseries data is stored without significant organization or processing at the time of data collection, then storage simplicity is improved, but data presentation speed deteriorates due to processing delays at query time
Solution Approach 1:
The patent applies preliminary action by pre-processing and organizing timeseries data at the time of collection into multiple hierarchical aggregations (daily, weekly, monthly, yearly) and storing them in an organized structure. This preliminary organization eliminates the need for processing delays at query time, as the data is already prepared in various aggregation levels for immediate retrieval and presentation.
2Loss of information
If applications retrieve and generate views of timeseries data in response to requests, then data freshness is improved, but query response time deteriorates
Solution Approach 1:
The patent pre-generates multiple hierarchical aggregations of timeseries data (daily, weekly, monthly, yearly summaries) and stores them in an organized structure. When queries are made, the system can immediately retrieve pre-computed aggregations rather than processing raw data in real-time, thus maintaining data freshness while dramatically reducing query response time.
Solution Approach 2:
The patent segments the timeseries data into multiple hierarchical levels of aggregation (raw data, daily summaries, weekly summaries, monthly summaries, yearly summaries). This segmentation allows the system to serve different query types from appropriate aggregation levels, balancing data freshness with fast response times by selecting the most appropriate pre-computed view.
3Ease of operation
If multiple aggregations of the same timeseries data are maintained, then data accessibility is improved, but storage requirements increase
Solution Approach 1:
The patent segments timeseries data into a hierarchical structure with multiple aggregation levels (daily, weekly, monthly, yearly). Each level stores summarized data appropriate to its time scope, making the system highly accessible for various query types. The segmentation strategy optimizes storage by storing only essential aggregated values at each level rather than redundant full datasets.
Solution Approach 2:
The patent applies local quality by storing different types of information at different hierarchical levels - raw detailed data at the lowest level and progressively more aggregated summaries at higher levels. Each level is optimized for its specific purpose, providing appropriate data granularity and aggregation type locally, which improves overall accessibility while managing storage efficiently through targeted retention of detail versus summary information.
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.