Understand and manage the individual data values associated with your building equipment in Cloud BMS.
Points is a building-level module. Select a building from the sites list to access its points.
A point is the most granular unit of data in Cloud BMS. Each point represents a single data value associated with a device: a temperature sensor reading, a valve position, a setpoint, an occupancy status, or a fan speed command. Points are the foundation of monitoring, control, trending, and analytics in the platform.
Cloud BMS categorizes points into four types based on their origin and behavior:
Type
Description
Read-only
Data values that report a measurement or status. You can view and trend these points but cannot write to them. Examples: temperature sensors, humidity readings, power consumption.
Writable
Points that accept commands to change equipment behavior. Examples: temperature setpoints, fan speed commands, lighting levels.
Virtual
Calculated or manually defined points on virtual devices. These do not correspond to a physical sensor. Examples: aggregated totals, derived calculations.
Copied
Mirrors of existing points duplicated into another device context. The source device retains ownership of the data.
When you add a data source, Cloud BMS discovers the available devices and their points through the discovery process. Discovery queries the external system and imports the raw point names, values, and metadata.After discovery, points appear with their native names from the data source (for example, AHU1.SA-T or Zone3_OccSensor). You then normalize these raw names using device templates and the ontology mapping tools.
From the building view, click Points in the left navigation. The Points section contains four sub-pages: Points Status, Points List, Batch Update Points, and Mass Write Points.
Points Status gives you a real-time health summary of every point in the building. Status badges across the top show counts for Ok, Down, Fault, Disabled, No Value, Overridden, FDD, and Stale points. Click any badge to filter the table to that status.The table lists each point with its Name, Value, Field, and Device. Use the search bar to find a specific point by name.
Points Status shows live health badges and a sortable table of all building points
Points List provides the same point data with advanced filtering. Use the filter row at the top to narrow results by Field, Kind, Device Type, Ontology Type, Area, Data Sources, or Writability. You can also access Mass Write Points directly from this page.
Points List adds filtering controls for Field, Kind, Device Type, Ontology Type, Area, Data Sources, and Writability
Cloud BMS supports two export workflows for points. Use Export on Points Status or Points List to download the current point inventory. Use Export Point Timeseries to download historical values as CSV for analysis outside the platform.
Both Points Status and Points List include an Export button. The export captures the filtered point list as it appears on screen, not historical readings.Click Export to open the dialog. Enter a File Name, choose which Columns to include, then click Export to download the file.
The export dialog lets you name the file and choose which columns to include
Export Point Timeseries exports historical point data as CSV. Use it to share evidence with contractors, analyze data in Excel, or build reports outside Cloud BMS.Open the export from any of these locations:
Building-wide — query and export points across the site
Device Details
Single device — open the three-dot menu and select Export Points
System Details
System scope — exports points on devices in that system
1
Select points
In Export Points, stay on Select points to Export. Use the search bar and filters to narrow the list by Ontology Field, Device Type, Canonical Type, Device, Area, Ontology Type, Data Source, Writability, or Kind. Select one or more points, then click Next.
Step 1 filters and selects points for the timeseries export
2
Configure export settings
Choose Export Format, set the Time range, then configure format-specific options. Click Export to download the CSV.
Step 2 sets format, time range, granularity, and columns
Export formats
Format
Structure
Best for
Raw List
One row per collected sample; points grouped and sorted by point
Troubleshooting, audit trails, exports that need metadata per record
Pivoted
Time periods as rows; points as columns
Spreadsheet analysis, comparing multiple points on the same time axis
Raw List exports every collected value for the selected points. It does not support granularity or aggregation. You can choose which metadata columns to include, such as Point Display Name, Original Name, Field, Device Name, Date & Time (US), Value, Unit, and Status.Pivoted aligns data to time buckets and is optimized for summarized reporting. Select Granularity and, when granularity is not Raw, set an aggregation function per point.
Granularity (Pivoted only)
Option
Description
Raw
No time alignment
1 min, 2 mins, 5 mins, 15 mins, 30 mins
Sub-hourly buckets
1 hour, 1 day
Hourly or daily summaries
When you select a non-raw granularity, choose Original Name or Display Name (Ontology) for column headers. Review the Aggregate Functions section and set a function for each exported point.
Aggregation functions (Pivoted only)
Point type
Available functions
Number
Average, Sum, Median, Min, Max, First, Last, Count, Delta
Boolean / Enum
First, Last, Longest value active
Longest value active returns the value that was active for the longest duration within the alignment period. For example, with 1 hour granularity, the export uses the state that held for the most time during that hour.
Limits
Each export supports up to 500 points and 800,000 total rows. Row count depends on time range, number of points, and collection frequency. If the export exceeds the limit, reduce the time range or export fewer points.
Fill behavior (Pivoted)
When granularity is applied:
If a point is down for the entire alignment period, the export value is null or down for that period.
If a point has status OK but no new sample during the period, the export carries forward the last value — but only for gaps under one hour.
If a point has no value for longer than one hour, the export does not keep filling. Values are treated as missing or down based on status.
Mass write points sends write commands to multiple writable points in a single tracked job. Use it when you need the same priority, value, and duration applied across many setpoints or commands — for example seasonal setpoint changes, releasing overrides overnight, or peak demand reductions.This workflow writes live values to equipment. To change point metadata, writability, or normalization instead, use Points batch update. For portfolio-wide jobs across many buildings, see Mass write.
You can also open Mass Write Points from Points List.The Mass Write Points page lists previous jobs for the building. Search by job name, review status and statistics, or click Write Points to start a new job.
The Mass Write Points page lists previous jobs and provides access to Write Points
From the portfolio view, open Mass Write.
Click Points in the left-side menu.
Click Write Points to start a new job or open an existing job from the list.
See Mass write for the full portfolio workflow, permissions, and screenshots.
Only writable points appear in the mass write list. Points are not selected by default — select targets explicitly with row checkboxes.Use Select all to include every point across all pages. After Select all, deselect individual exceptions as needed.Use Filters to narrow the list by point type, canonical type, area, ontology field, data source type, and other attributes. To find non-templated points, set Ontology Field to Not Assigned.Search supports three match modes — Starts with, Contains, and Exact — across these fields:
Field
Description
Point Name
Display or raw point name
Path
Point path in the data source
Ontology Field
Normalized ontology field name
Device Name
Device that owns the point
Toggle between List and Grouped view in the upper right.
Grouped view simplifies selection for large point sets, especially non-templated points.In grouped mode:
Points are grouped by name and kind.
Each group shows the group name, point count, and device count.
Expand a group to review the underlying point list with the same columns as the standard table.
Select entire groups. You cannot include or exclude individual points inside a group.
Filters still apply to the full list before grouping. Grouping does not replace filters.
When writing very large point sets, confirm that target data sources can handle the write volume. This matters when many buildings share a single data source.
From Mass Write Points, click Write Points to start a new job.
2
Filter buildings (portfolio only)
On portfolio-level jobs, use the Filters panel on the left to select buildings by country, state, city, or building name. Click Apply to load points from those buildings.
3
Filter and search points
Use Filters to narrow the list by Kind, Area, Canonical, Field, Datasource, and other attributes. Use the search bar to find points by name. Set Ontology Field to Not Assigned when you need non-templated points.
4
Select points
Choose targets in List or Grouped view. Use row checkboxes or group checkboxes, or click Select all in the selection banner to include every point across all pages. Deselect any exceptions.
The Select Points step shows filters, List and Grouped view toggle, and a selection count banner
Click Continue to Set Values when the selection is final.
You cannot remove points in later steps. Verify your selection before continuing.
5
Set values and execute
Enter a Name, Reason Type, and Reason Description. Points are grouped automatically by ontology field, unit, and accepted values. Configure each group:
Field
Description
Priority
Write priority: Override, Auto, Set, or Release
Value
Value to write to every point in the group. Negative values are supported for numeric points.
Duration
How long the value persists before reverting. Check Permanent to keep the value until released.
Click Save for each group. Color highlights indicate missing information.
Set Name, Reason Type, and priority, value, and duration for each point group
After saving all groups, click Execute to start the write operation.
After execution, Cloud BMS opens the job details page with status, timestamp, reason, user, and success and failure counts per group. Click the blue point counts to view individual points and their write results.
Job details shows status, reason, user, and per-group write results
From job details, use the three-dot menu to:
Action
Behavior
Edit Details
Update the job name or description
Re-run Job
Re-execute using all original points. You can edit the selection and write configuration before running
Retry Job with failed points
Re-execute immediately for failed points only. You can edit the configuration before running
The job actions menu provides options to edit, re-run, or retry failed points
Click Export to download job results as CSV. Enter a File Name, choose which Columns to include, then click Export.The CSV export includes Building ID, Building Name, Device ID, Device Name, Name, Field, Priority, Value, Duration, Status, and Failed Reason.
Export job results with a custom file name and column selection
Completed jobs remain listed on the Mass Write Points page. Open the three-dot menu on any job row to edit details, re-run, or retry failed points.
Manage previous jobs from the Mass Write Points list
Point normalization standardizes raw point names from different manufacturers into a consistent ontology format. This is critical for cross-building analytics, automated fault detection, and dashboard generation.Cloud BMS provides an ML-powered auto-suggest tool that recommends standardized names based on the raw point name. The mapping follows the ontology field format:[aggregator]_[descriptor]_[component]_[measurement descriptor]_[measurement]_[point type]For example:
Raw Point Name
Standardized Name
SA-T
supply_air_temperature_sensor
RA-T
return_air_temperature_sensor
CLG-SP
cooling_temperature_setpoint
FAN-CMD
fan_run_command
OCC-STS
occupancy_status
Consistent normalization enables platform-wide benefits: simplified dashboard generation (filter and select points by ontology field), cross-building comparisons, and automated fault detection rules that apply universally.
Writable points allow you to command equipment directly from Cloud BMS. See command and control points for how to send commands from device details or a floor plan.To make a point writable (or remove writability), you can modify it individually from the device details or in bulk using the points batch update feature.
Some points report enumerated values (enums) rather than numeric readings. For example, an operating mode point might report values like 0, 1, 2 that correspond to Off, Heat, Cool.Enum mapping translates these numeric codes into human-readable labels. Navigate to the enum mapping interface from the device or point settings to configure these translations.
Enum mappings are critical for features like OSS, FDD, and FTT. For example, OSS requires a correctly mapped Occupied Mode enum to determine when a space is in occupied or unoccupied mode. Incorrect mappings can cause false fault detections or missed schedule optimizations.
