AI駆動型iPaaS実践ガイド2025 - 次世代統合プラットフォームの実装手法

class AIFieldMapper {
  constructor() {
    this.mappingRules = new Map();
    this.learnedMappings = new Map();
  }

  async intelligentMapping(sourceSchema, targetSchema) {
    const mappings = {};
    
    for (const sourceField of sourceSchema) {
      const candidates = await this.findCandidates(sourceField, targetSchema);
      const bestMatch = await this.selectBestMatch(sourceField, candidates);
      
      if (bestMatch.confidence > 0.8) {
        mappings[sourceField.name] = bestMatch.target;
      }
    }
    
    return mappings;
  }

  async findCandidates(sourceField, targetSchema) {
    const candidates = [];
    
    for (const targetField of targetSchema) {
      const similarity = await this.calculateSimilarity(sourceField, targetField);
      if (similarity > 0.5) {
        candidates.push({
          target: targetField.name,
          confidence: similarity,
          reasons: this.explainSimilarity(sourceField, targetField)
        });
      }
    }
    
    return candidates.sort((a, b) => b.confidence - a.confidence);
  }

  async calculateSimilarity(source, target) {
    // セマンティック類似度計算
    const nameScore = this.calculateNameSimilarity(source.name, target.name);
    const typeScore = this.calculateTypeSimilarity(source.type, target.type);
    const contextScore = await this.calculateContextSimilarity(source, target);
    
    return (nameScore * 0.4 + typeScore * 0.3 + contextScore * 0.3);
  }
}

{
  "aiMappingConfig": {
    "enableAutoMapping": true,
    "confidenceThreshold": 0.8,
    "learningMode": "active",
    "customRules": [
      {
        "pattern": ".*_id$",
        "targetPattern": ".*Id$",
        "confidence": 0.9
      },
      {
        "pattern": "email.*",
        "targetPattern": ".*email.*",
        "confidence": 0.95
      }
    ],
    "mlModels": {
      "fieldMatcher": "bert-base-uncased",
      "dataTypeClassifier": "custom-classifier-v2",
      "semanticAnalyzer": "openai-embedding-ada-002"
    }
  }
}

class AIIntegrationOrchestrator {
  constructor() {
    this.workflows = new Map();
    this.aiModels = new Map();
    this.metrics = new MetricsCollector();
  }

  async orchestrateIntegration(integrationConfig) {
    const workflow = await this.createWorkflow(integrationConfig);
    
    try {
      // 1. データ準備フェーズ
      const sourceData = await this.extractData(workflow.source);
      const preparedData = await this.prepareData(sourceData, workflow.aiConfig);
      
      // 2. AI処理フェーズ
      const aiResults = await this.runAIProcessing(preparedData, workflow);
      
      // 3. 検証フェーズ
      const validationResults = await this.validateResults(aiResults, workflow);
      
      // 4. 配信フェーズ
      if (validationResults.success) {
        await this.distributeData(aiResults.transformedData, workflow.targets);
      }
      
      // 5. 学習フェーズ
      await this.updateModels(aiResults, validationResults);
      
      return {
        success: true,
        processedRecords: aiResults.recordCount,
        confidence: aiResults.confidence,
        executionTime: Date.now() - workflow.startTime
      };
      
    } catch (error) {
      await this.handleError(error, workflow);
      throw error;
    }
  }

  async runAIProcessing(data, workflow) {
    const results = {
      transformedData: [],
      confidence: 0,
      recordCount: 0,
      anomalies: []
    };

    for (const record of data) {
      // 異常検知
      const anomalyScore = await this.detectAnomalies(record);
      if (anomalyScore > 0.9) {
        results.anomalies.push({
          record,
          score: anomalyScore,
          reasons: await this.explainAnomaly(record)
        });
        continue;
      }

      // データ変換
      const transformed = await this.aiTransformationEngine.transform(
        record,
        workflow.sourceSchema,
        workflow.targetSchema
      );

      results.transformedData.push(transformed.data);
      results.confidence += transformed.confidence;
      results.recordCount++;
    }

    results.confidence /= results.recordCount;
    return results;
  }
}

class AIIntegrationMonitor:
    def __init__(self):
        self.metrics_store = MetricsStore()
        self.alert_manager = AlertManager()
        self.predictive_analyzer = PredictiveAnalyzer()
    
    async def monitor_integration(self, integration_id):
        """統合プロセスのリアルタイム監視"""
        
        while True:
            # メトリクス収集
            metrics = await self.collect_metrics(integration_id)
            
            # 異常検知
            anomalies = await self.detect_anomalies(metrics)
            
            # 予測分析
            predictions = await self.predict_issues(metrics)
            
            # アラート判定
            if anomalies or predictions.risk_level > 0.8:
                await self.send_alerts(integration_id, anomalies, predictions)
            
            # メトリクス保存
            await self.metrics_store.store(integration_id, metrics)
            
            await asyncio.sleep(30)  # 30秒間隔で監視
    
    async def collect_metrics(self, integration_id):
        """メトリクス収集"""
        return {
            'throughput': await self.get_throughput(integration_id),
            'latency': await self.get_latency(integration_id),
            'error_rate': await self.get_error_rate(integration_id),
            'data_quality': await self.get_data_quality(integration_id),
            'ai_confidence': await self.get_ai_confidence(integration_id),
            'resource_usage': await self.get_resource_usage(integration_id)
        }
    
    async def predict_issues(self, metrics):
        """問題予測"""
        features = self.extract_features(metrics)
        
        # 複数の予測モデルを実行
        capacity_prediction = await self.predictive_analyzer.predict_capacity_issues(features)
        performance_prediction = await self.predictive_analyzer.predict_performance_degradation(features)
        quality_prediction = await self.predictive_analyzer.predict_data_quality_issues(features)
        
        return {
            'risk_level': max(capacity_prediction.risk, performance_prediction.risk, quality_prediction.risk),
            'capacity_issues': capacity_prediction,
            'performance_issues': performance_prediction,
            'quality_issues': quality_prediction,
            'recommendations': self.generate_recommendations(
                capacity_prediction, performance_prediction, quality_prediction
            )
        }

class IntelligentCache {
  constructor() {
    this.cache = new Map();
    this.aiPredictor = new CachePredictor();
  }

  async get(key) {
    // キャッシュヒット
    if (this.cache.has(key)) {
      await this.updateAccessPattern(key);
      return this.cache.get(key);
    }

    // キャッシュミス - 予測的プリロード
    const relatedKeys = await this.aiPredictor.predictRelatedKeys(key);
    await this.preloadCache(relatedKeys);

    return null;
  }

  async set(key, value, ttl = 3600) {
    // AI予測によるTTL調整
    const predictedAccess = await this.aiPredictor.predictAccessFrequency(key);
    const optimizedTTL = this.calculateOptimalTTL(predictedAccess, ttl);

    this.cache.set(key, {
      value,
      timestamp: Date.now(),
      ttl: optimizedTTL,
      accessCount: 0
    });

    // キャッシュサイズ管理
    await this.manageCacheSize();
  }

  async preloadCache(keys) {
    const preloadPromises = keys.map(async (key) => {
      if (!this.cache.has(key)) {
        const data = await this.fetchFromSource(key);
        await this.set(key, data);
      }
    });

    await Promise.all(preloadPromises);
  }
}

class ResourceOptimizer:
    def __init__(self):
        self.resource_monitor = ResourceMonitor()
        self.ml_optimizer = MLOptimizer()
    
    async def optimize_resources(self):
        """リソース使用量の最適化"""
        
        # 現在のリソース使用状況を取得
        current_usage = await self.resource_monitor.get_current_usage()
        
        # 最適化提案をAIで生成
        optimization_suggestions = await self.ml_optimizer.generate_suggestions(current_usage)
        
        # 実行可能な最適化を適用
        for suggestion in optimization_suggestions:
            if suggestion.confidence > 0.8 and suggestion.risk_level < 0.3:
                await self.apply_optimization(suggestion)
        
        return {
            'applied_optimizations': len([s for s in optimization_suggestions if s.applied]),
            'estimated_savings': sum([s.estimated_savings for s in optimization_suggestions if s.applied]),
            'performance_improvement': await self.measure_performance_improvement()
        }
    
    async def apply_optimization(self, suggestion):
        """最適化の適用"""
        if suggestion.type == 'cpu_scaling':
            await self.adjust_cpu_allocation(suggestion.parameters)
        elif suggestion.type == 'memory_optimization':
            await self.optimize_memory_usage(suggestion.parameters)
        elif suggestion.type == 'io_optimization':
            await self.optimize_io_operations(suggestion.parameters)
        
        suggestion.applied = True
        await self.log_optimization(suggestion)