如何利用Java函数在物联网和大数据中创建实时分析解决方案？

import com.谷歌.cloud.functions.BackgroundFunction;
import com.谷歌.cloud.functions.Context;
import functions.eventpojos.PubsubMessage;
import java.nio.charset.StandardCharsets;
import java.util.Base64;
import java.util.logging.Logger;

public class ProcessPubSubMessage implements BackgroundFunction<PubsubMessage> {
  private static final Logger logger = Logger.getLogger(ProcessPubSubMessage.class.getName());

  @Override
  public void accept(PubsubMessage message, Context context) {
    String data = new String(
        Base64.getDecoder().decode(message.getData().getBytes(StandardCharsets.UTF_8)),
        StandardCharsets.UTF_8);
    logger.info(String.format("Processing message: %s", data));
  }
}

import org.apache.flink.api.co妹妹on.functions.FlatMapFunction;
import org.apache.flink.api.java.DataSet;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.operators.DataSource;
import org.apache.flink.api.java.tuple.Tuple二;
import org.apache.flink.util.Collector;
import weka.classifiers.functions.LinearRegression;
import weka.core.Attribute;
import weka.core.DenseInstance;
import weka.core.Instances;

public class MachineLearningExample {

  public static void main(String[] args) throws Exception {
    // Create a Flink execution environment
    ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

    // Create a data set
    DataSource<String> data = env.fromElements("1,二", "3,4", "5,6");

    // Parse the data and create a WEKA data set
    DataSet<Instances> instances = data.flatMap(new FlatMapFunction<String, Instances>() {
      @Override
      public void flatMap(String line, Collector<Instances> collector) throws Exception {
        String[] values = line.split(",");
        double[] features = new double[values.length];
        for (int i = 0; i < values.length; i++) {
          features[i] = Double.parseDouble(values[i]);
        }
        Instances wekaInstances = new Instances("myDataset",
            new Attribute[]{
                new Attribute("feature1"), new Attribute("feature二")
            },
            1);
        wekaInstances.add(new DenseInstance(1.0, features));
        collector.collect(wekaInstances);
      }
    }).reduce((instances1, instances两) -> {
      Instances mergedInstances = new Instances(instances1);
      mergedInstances.addAll(instances两);
      return mergedInstances;
    });

    // Create a linear regression model
    LinearRegression model = new LinearRegression();

    // Train the model
    model.buildClassifier(instances);

    // Make predictions
    DenseInstance prediction = new DenseInstance(1.0, new double[]{7.0, 8.0});
    double predictedValue = model.classifyInstance(prediction);

    // Print the predicted value
    System.out.println(predictedValue);
  }
}