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After building a historical analytics dashboard, the next step is predictive maintenance — using machine learning (ML) to anticipate future job failures before they happen.
This allows your Spring Boot app to alert admins early and prevent downtime.

1. Why Predictive Maintenance

Traditional monitoring reacts after failures.
Predictive maintenance analyzes historical trends — such as job duration, retry counts, and failure frequency — to detect risk patterns and trigger early warnings.
This approach transforms your Firebird maintenance system from reactive to proactive.

2. Data Preparation

You already have logs stored in the MAINTENANCE_LOG table.
To train a model, extract the relevant columns: 
SELECT job_name, duration_ms, retry_count, 
       CASE WHEN status = 'FAILED' THEN 1 ELSE 0 END AS failed
FROM maintenance_log;
This dataset provides features (duration_ms, retry_count) and a label (failed). You can export this data to CSV from your Spring Boot application: 
public void exportJobDataToCSV(String path) throws IOException {
    List<MaintenanceLog> logs = logRepo.findAll();
    try (FileWriter writer = new FileWriter(path)) {
        writer.write("job_name,duration_ms,retry_count,failed\n");
        for (MaintenanceLog log : logs) {
            writer.write(log.getJobName() + "," +
                         log.getDurationMs() + "," +
                         log.getRetryCount() + "," +
                         (log.getStatus().equals("FAILED") ? 1 : 0) + "\n");
        }
    }
}

3. Simple Prediction Logic with Linear Regression

For simplicity, you can integrate a lightweight Java ML library such as Smile or Tribuo, but we’ll use a simple heuristic-based predictor to illustrate the concept: 
@Service
public class FailurePredictorService {

    public boolean predictFailure(long avgDuration, int retryCount) {
        double riskScore = (avgDuration / 1000.0) + (retryCount * 5);
        return riskScore > 60; // example threshold
    }
}
This mock predictor calculates a basic “risk score.”
If a job often retries or takes longer than usual, it’s flagged as high risk.

4. Integrate Prediction into Scheduler

Use the predictor before running jobs to estimate failure probability: 
@Component
public class SmartScheduler {

    @Autowired
    private FailurePredictorService predictor;
    @Autowired
    private MaintenanceLogRepository logRepo;

    @Scheduled(cron = "0 0 2 * * *")
    public void predictiveCleanup() {
        long avgDuration = logRepo.averageJobDuration();
        int recentRetries = logRepo.countRecentRetries("Cleanup Job");

        if (predictor.predictFailure(avgDuration, recentRetries)) {
            System.out.println("⚠️ High failure risk detected for Cleanup Job. Skipping run.");
            logRepo.save(new MaintenanceLog("Cleanup Job", "SKIPPED_RISK",
                    LocalDateTime.now(), "Skipped due to high failure risk."));
            return;
        }

        // run normal job if safe
        System.out.println("Running Cleanup Job...");
        // ... existing cleanup logic ...
    }
}
This step adds intelligence: the system predicts failure likelihood and safely delays risky operations.

5. Add Risk Visualization to Dashboard

Extend your dashboard to include a “Failure Risk” column: 
<td th:text="${log.status == 'SKIPPED_RISK' ? 'High Risk' : 'Normal'}"></td>
And optionally, a color indicator for easy viewing: 
.risk { background-color: #fff3cd; } /* Yellow for risk */
Now the dashboard not only shows history — it also forecasts potential problems.
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