Interactive Data Assembly Tool (IDAT)¶

Overview¶

The Interactive Data Assembly Tool (IDAT) is AIDDDMAP's sophisticated visual workspace for creating, modifying, and managing data workflows. It combines an intuitive canvas-based interface with AI agent assistance to streamline data preparation, analysis, and publishing processes.

Core Features¶

1. Visual Canvas Environment¶

interface IDATPanelConfig {
  dimensions: {
    width: number;
    height: number;
  };
  grid: {
    size: number;
    snap: boolean;
  };
  theme: {
    background: string;
    gridColor: string;
    nodeColors: Record<string, string>;
  };
}

The canvas provides:

Drag-and-drop functionality for datasets and agents
Real-time node connections and workflow visualization
Interactive grid system with snapping capabilities
Customizable themes and visual styles

2. Node Types¶

enum NodeType {
  DATASET = "dataset",
  AGENT = "agent",
  TRANSFORM = "transform",
  OUTPUT = "output",
  ENCRYPTION = "encryption",
}

interface BaseNode {
  id: string;
  type: NodeType;
  position: { x: number; y: number };
  data: any;
}

interface DatasetNode extends BaseNode {
  type: NodeType.DATASET;
  data: {
    name: string;
    schema: SchemaDefinition;
    preview: DataPreview;
    metadata: DatasetMetadata;
  };
}

3. Agent Integration¶

IDAT seamlessly integrates with AIDDDMAP's AI agents:

interface AgentNode extends BaseNode {
  type: NodeType.AGENT;
  data: {
    agent: BaseAgent;
    status: AgentStatus;
    configuration: AgentConfig;
  };
}

// Example: Deploying an agent to the canvas
const workflow = new DataWorkflow();
workflow.addNode({
  type: NodeType.AGENT,
  data: {
    agent: new DataCuratorAgent(),
    configuration: {
      mode: "automatic",
      triggers: ["new_data", "schema_change"],
    },
  },
});

4. Real-time Processing¶

interface LiveDataConfig {
  source: DataSource;
  bufferSize: number;
  updateInterval: number;
  errorHandling: ErrorHandlingStrategy;
}

class LiveDataHandler {
  constructor(config: LiveDataConfig) {
    this.buffer = new CircularBuffer(config.bufferSize);
    this.interval = config.updateInterval;
  }

  async processIncomingData(data: any): Promise<void> {
    await this.buffer.add(data);
    await this.notifySubscribers(data);
  }
}

5. Workflow Management¶

interface WorkflowState {
  status: "idle" | "processing" | "error";
  progress: number;
  activeNodes: string[];
  errors: WorkflowError[];
  metrics: {
    processedItems: number;
    throughput: number;
    latency: number;
  };
}

// Example: Creating a data preparation pipeline
const pipeline = new DataPipeline()
  .addSource(new DatasetNode("raw_data"))
  .addTransformation(new CleansingNode())
  .addAgent(new DataCuratorAgent())
  .addValidation(new QualityCheckNode())
  .addDestination(new OutputNode("processed_data"));

User Interface Components¶

1. Main Canvas¶

Interactive workspace for visual workflow creation
Zoom and pan controls
Grid system with snap-to-grid functionality
Node selection and multi-select capabilities

2. Right Drawer (Agent Panel)¶

Access to all available AI agents
Agent configuration controls
Real-time agent status monitoring
Drag-and-drop deployment to canvas

3. Left Drawer (Task Zone)¶

Active task management
Live data monitoring
Quick access to common operations
Status notifications and alerts

Common operations (save, load, export)
View controls (zoom, fit, center)
Undo/redo functionality
Layout options

Integration Features¶

1. Encryption Integration¶

interface EncryptionNode extends BaseNode {
  type: NodeType.ENCRYPTION;
  mode: "FHE" | "ZK" | "Basic";
  config: FHEConfig | ZKConfig | BasicEncryptionConfig;
}

// Adding encryption to a workflow
workflow.addNode({
  type: NodeType.ENCRYPTION,
  mode: "FHE",
  config: {
    scheme: "CKKS",
    parameters: {
      polyModulusDegree: 8192,
      coeffModulusBits: [60, 40, 40, 60],
    },
  },
});

2. UADM Integration¶

// Deploy custom agent from UADM
const customAgent = await uadm.deployAgent({
  agent: new CustomAgent(),
  config: {
    hardwareAbstraction: {
      protocols: ["http", "websocket"],
      deviceTypes: ["server"],
    },
  },
});

workflow.addNode({
  type: NodeType.AGENT,
  data: { agent: customAgent },
});

Best Practices¶

1. Performance Optimization¶

// Implement data streaming for large datasets
const stream = new DataStream({
  batchSize: 1000,
  parallel: true,
  maxConcurrency: 4,
});

stream.on("data", async (batch) => {
  await workflow.processBatch(batch);
});

2. Error Handling¶

try {
  await workflow.execute();
} catch (error) {
  if (error instanceof ValidationError) {
    await workflow.handleValidationError(error);
  } else if (error instanceof ProcessingError) {
    await workflow.retry({
      maxAttempts: 3,
      backoff: "exponential",
    });
  }
}

3. Resource Management¶

const resources = new ResourceManager({
  maxMemory: "4GB",
  maxConcurrency: 8,
  timeout: 30000,
});

workflow.setResourceConstraints(resources);

Example Workflows¶

1. Data Preparation Pipeline¶

const preparationWorkflow = new DataWorkflow()
  .addSource(new DatasetNode("raw_data"))
  .addAgent(
    new DataQualityAgent({
      validations: ["completeness", "accuracy"],
    }),
  )
  .addAgent(
    new DataCuratorAgent({
      enrichment: ["metadata", "schema"],
    }),
  )
  .addEncryption(
    new EncryptionNode({
      mode: "FHE",
    }),
  )
  .addDestination(new OutputNode("marketplace"));

await preparationWorkflow.execute();

2. Real-time Analytics¶

const realtimeWorkflow = new RealTimeWorkflow()
  .setSource(
    new StreamingSource({
      type: "websocket",
      url: "ws://data-stream",
    }),
  )
  .addProcessing(
    new WindowedAggregation({
      window: "5m",
      operation: "average",
    }),
  )
  .addVisualization(new LiveDashboard());