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DFS Pro Fusion Tasks

Fusion tasks combine data from multiple datasets. Use them when one workflow needs to match, merge, compare, enrich, or reconcile records from different sources.

Examples:

  • combine sensor history with maintenance history;
  • match inspection findings to work orders;
  • align source-system asset IDs with FactVerse assets;
  • merge operational records from several sites;
  • prepare a reviewed output dataset for predictive maintenance or AI Agent workflows.

Open Data Fusion​

Go to:

Data Integration > Data Fusion

The page shows fusion tasks, mode, status, output dataset, and run actions.

Fusion modes​

ModeUse when
Rule MatchingMatching logic is deterministic, such as same asset ID, same timestamp window, or known key columns.
Semantic MatchingNames, aliases, descriptions, or relationships need to be compared.
LLM AssistedThe task needs language-based assistance and every uncertain result will be reviewed.

Use rule matching first when stable keys are available. Use semantic or LLM-assisted modes when source records use different names, aliases, or descriptions.

Create a fusion task​

  1. Open Data Fusion.
  2. Select Create Fusion Task.
  3. Enter a task name.
  4. Add a description.
  5. Choose a fusion mode.
  6. Select input datasets.
  7. Select a method when the task requires reusable processing logic.
  8. Set the output dataset name or output dataset.
  9. Configure conflict threshold when available.
  10. Save the task.

Use a name that describes the business output.

Examples:

  • Asset sensor and work order alignment
  • Inspection finding to maintenance record match
  • Equipment alias reconciliation
  • PDM signal feature merge

Run the task​

Use Run from the fusion task list or detail page.

During execution, a task may move through statuses such as queued, running, completed, failed, cancelled, or review.

After starting a task:

  1. Watch status.
  2. Open run history.
  3. Review total, matched, and conflict counts.
  4. Open review queue if status indicates review.
  5. Use output dataset only after required review is complete.

Review run history​

Run history helps users understand what happened during execution.

Check:

  • triggered by;
  • started at;
  • duration;
  • total records;
  • matched records;
  • conflict records;
  • error message when failed.

If a task fails, fix the dataset, method, mapping, or output dataset issue before retrying.

Review uncertain output​

Fusion can produce conflicts, source disagreements, low-confidence matches, or manual flags. These should go through the review queue.

Reviewers should compare:

  • input dataset records;
  • matching keys;
  • source timestamps;
  • confidence;
  • conflict reason;
  • output record;
  • downstream impact.

Retry or cancel​

Use retry after fixing a failed task. Use cancel when a queued or running task should stop because the input or configuration is wrong.

Before retry:

  • confirm input datasets exist and are accessible;
  • confirm output dataset is writable;
  • confirm method status is usable;
  • check the last error message;
  • check whether review items remain open.

Output dataset​

A completed fusion task can produce an output dataset. Treat that output as governed data:

  • preview rows;
  • profile columns;
  • assign a steward if it will be reused;
  • validate the dataset after review;
  • check lineage before replacing or deprecating it.

Next step​

Use Review Queue to resolve fusion conflicts and rejected rows.