@codai/cbd
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Codai Better Database - High-Performance Vector Memory System with HPKV-inspired architecture and MCP server
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JavaScript
/**
* CBD Phase 4: Future Technologies Integration
*
* Next-generation database features including quantum computing,
* digital twins, blockchain, and mixed reality capabilities.
*
* Features:
* - Quantum computing integration for optimization
* - IoT and real-world modeling with digital twins
* - Immutable audit trail with blockchain
* - Mixed reality data visualization interfaces
*
* @version 4.0.0
* @since Phase 4: Innovation & Scale
*/
import { EventEmitter } from 'events';
/**
* Future Technologies Integration Service
*
* Manages quantum computing, digital twins, blockchain,
* and mixed reality capabilities for next-generation database features.
*/
export class FutureTechnologies extends EventEmitter {
quantumProcessors = new Map();
digitalTwins = new Map();
blockchainNetworks = new Map();
mixedRealitySessions = new Map();
constructor() {
super();
this.initializeFutureTechnologies();
}
/**
* Initialize quantum computing processor for database optimization
*/
async initializeQuantumProcessor(config) {
const startTime = Date.now();
try {
// Validate quantum configuration
await this.validateQuantumConfiguration(config);
// Initialize quantum processor
const processorId = `quantum_${config.provider}_${Date.now()}`;
// Connect to quantum backend
const connection = await this.connectToQuantumBackend(config);
// Register processor
this.quantumProcessors.set(processorId, config);
// Emit success event
this.emit('quantum_processor_initialized', {
processorId,
provider: config.provider,
backend: config.quantumBackend,
maxQubits: config.maxQubits,
initializationTime: Date.now() - startTime
});
return processorId;
}
catch (error) {
this.emit('quantum_initialization_failed', {
provider: config.provider,
error: error instanceof Error ? error.message : 'Unknown error',
duration: Date.now() - startTime
});
throw new Error(`Quantum processor initialization failed: ${error instanceof Error ? error.message : 'Unknown error'}`);
}
}
/**
* Perform quantum-enhanced database optimization
*/
async performQuantumOptimization(processorId, optimizationProblem, parameters) {
const startTime = Date.now();
try {
const config = this.quantumProcessors.get(processorId);
if (!config) {
throw new Error(`Quantum processor not found: ${processorId}`);
}
// Classical preprocessing
const classicalStart = Date.now();
const preprocessedProblem = await this.preprocessOptimizationProblem(optimizationProblem, parameters);
const classicalTime = Date.now() - classicalStart;
// Quantum processing
const quantumStart = Date.now();
const quantumResult = await this.executeQuantumOptimization(config, preprocessedProblem);
const quantumTime = Date.now() - quantumStart;
// Calculate results
const result = {
optimizationType: optimizationProblem,
classicalTime,
quantumTime,
speedupFactor: classicalTime > 0 ? classicalTime / quantumTime : 1,
confidenceLevel: quantumResult.confidence,
qubitsUsed: quantumResult.qubitsUsed,
energyEfficiency: quantumResult.energyRatio
};
// Emit success event
this.emit('quantum_optimization_completed', {
processorId,
optimizationType: optimizationProblem,
speedup: result.speedupFactor,
qubitsUsed: result.qubitsUsed,
totalTime: Date.now() - startTime
});
return result;
}
catch (error) {
this.emit('quantum_optimization_failed', {
processorId,
optimizationType: optimizationProblem,
error: error instanceof Error ? error.message : 'Unknown error',
duration: Date.now() - startTime
});
throw new Error(`Quantum optimization failed: ${error instanceof Error ? error.message : 'Unknown error'}`);
}
}
/**
* Create and manage digital twin for IoT and real-world modeling
*/
async createDigitalTwin(config) {
const startTime = Date.now();
try {
// Validate digital twin configuration
await this.validateDigitalTwinConfiguration(config);
// Initialize digital twin
const twinId = config.twinId || `twin_${Date.now()}`;
// Set up sensor connections
await this.connectSensors(config.sensors);
// Start real-time processing if enabled
if (config.realTimeProcessing) {
await this.startRealTimeProcessing(twinId, config);
}
// Register digital twin
this.digitalTwins.set(twinId, { ...config, twinId });
// Emit success event
this.emit('digital_twin_created', {
twinId,
modelType: config.modelType,
sensorsCount: config.sensors.length,
realTimeProcessing: config.realTimeProcessing,
creationTime: Date.now() - startTime
});
return twinId;
}
catch (error) {
this.emit('digital_twin_creation_failed', {
twinId: config.twinId,
modelType: config.modelType,
error: error instanceof Error ? error.message : 'Unknown error',
duration: Date.now() - startTime
});
throw new Error(`Digital twin creation failed: ${error instanceof Error ? error.message : 'Unknown error'}`);
}
}
/**
* Get digital twin insights and predictions
*/
async getDigitalTwinInsights(twinId) {
try {
const config = this.digitalTwins.get(twinId);
if (!config) {
throw new Error(`Digital twin not found: ${twinId}`);
}
// Collect sensor data
const sensorData = await this.collectSensorData(config.sensors);
// Detect anomalies
const anomalies = await this.detectAnomalies(sensorData, config.alertThresholds);
// Generate predictions
const predictions = config.predictiveAnalytics
? await this.generatePredictions(twinId, sensorData)
: {};
// Calculate risk score
const riskScore = this.calculateRiskScore(sensorData, anomalies, config.alertThresholds);
// Generate recommendations
const recommendations = await this.generateRecommendations(twinId, sensorData, anomalies);
const insight = {
twinId,
timestamp: new Date(),
sensorData,
anomalies,
predictions,
recommendations,
riskScore
};
// Emit insight event
this.emit('digital_twin_insight', {
twinId,
anomaliesCount: anomalies.length,
riskScore,
predictionsCount: Object.keys(predictions).length
});
return insight;
}
catch (error) {
this.emit('digital_twin_insight_failed', {
twinId,
error: error instanceof Error ? error.message : 'Unknown error'
});
throw new Error(`Digital twin insights failed: ${error instanceof Error ? error.message : 'Unknown error'}`);
}
}
/**
* Initialize blockchain network for immutable audit trails
*/
async initializeBlockchain(config) {
const startTime = Date.now();
try {
// Validate blockchain configuration
await this.validateBlockchainConfiguration(config);
// Connect to blockchain network
const networkId = `blockchain_${config.network}_${Date.now()}`;
// Deploy smart contracts if needed
if (config.smartContracts) {
await this.deployAuditContracts(config);
}
// Register blockchain network
this.blockchainNetworks.set(networkId, config);
// Emit success event
this.emit('blockchain_initialized', {
networkId,
network: config.network,
auditScope: config.auditScope.length,
smartContracts: config.smartContracts,
initializationTime: Date.now() - startTime
});
return networkId;
}
catch (error) {
this.emit('blockchain_initialization_failed', {
network: config.network,
error: error instanceof Error ? error.message : 'Unknown error',
duration: Date.now() - startTime
});
throw new Error(`Blockchain initialization failed: ${error instanceof Error ? error.message : 'Unknown error'}`);
}
}
/**
* Create blockchain audit record
*/
async createAuditRecord(networkId, operation, data) {
try {
const config = this.blockchainNetworks.get(networkId);
if (!config) {
throw new Error(`Blockchain network not found: ${networkId}`);
}
// Create data hash
const dataHash = await this.createDataHash(data);
// Get previous hash
const previousHash = await this.getPreviousHash(networkId);
// Create blockchain transaction
const transaction = await this.createBlockchainTransaction(config, {
operation,
dataHash,
previousHash,
timestamp: new Date()
});
const auditRecord = {
transactionHash: transaction.hash,
blockNumber: transaction.blockNumber,
timestamp: transaction.timestamp,
operation,
dataHash,
previousHash,
verified: transaction.verified,
gasUsed: transaction.gasUsed
};
// Emit audit event
this.emit('blockchain_audit_created', {
networkId,
operation,
transactionHash: transaction.hash,
blockNumber: transaction.blockNumber
});
return auditRecord;
}
catch (error) {
this.emit('blockchain_audit_failed', {
networkId,
operation,
error: error instanceof Error ? error.message : 'Unknown error'
});
throw new Error(`Blockchain audit record creation failed: ${error instanceof Error ? error.message : 'Unknown error'}`);
}
}
/**
* Initialize mixed reality visualization session
*/
async initializeMixedReality(config) {
const startTime = Date.now();
try {
// Validate mixed reality configuration
await this.validateMixedRealityConfiguration(config);
// Initialize MR session
const sessionId = `mr_session_${Date.now()}`;
// Connect to MR platform
await this.connectToMRPlatform(config);
// Load data models for visualization
await this.loadDataModelsForMR(config);
// Register MR session
this.mixedRealitySessions.set(sessionId, config);
// Emit success event
this.emit('mixed_reality_initialized', {
sessionId,
platform: config.platform,
visualizationType: config.visualizationType,
collaborativeMode: config.collaborativeMode,
initializationTime: Date.now() - startTime
});
return sessionId;
}
catch (error) {
this.emit('mixed_reality_initialization_failed', {
platform: config.platform,
error: error instanceof Error ? error.message : 'Unknown error',
duration: Date.now() - startTime
});
throw new Error(`Mixed reality initialization failed: ${error instanceof Error ? error.message : 'Unknown error'}`);
}
}
/**
* Get comprehensive future technologies analytics
*/
async getFutureTechnologiesAnalytics() {
return {
quantum: {
processors: this.quantumProcessors.size,
azureQuantum: Array.from(this.quantumProcessors.values()).filter(q => q.provider === 'azure_quantum').length,
awsBraket: Array.from(this.quantumProcessors.values()).filter(q => q.provider === 'aws_braket').length,
ibmQuantum: Array.from(this.quantumProcessors.values()).filter(q => q.provider === 'ibm_quantum').length,
googleQuantumAI: Array.from(this.quantumProcessors.values()).filter(q => q.provider === 'google_quantum_ai').length
},
digitalTwins: {
total: this.digitalTwins.size,
realTimeProcessing: Array.from(this.digitalTwins.values()).filter(dt => dt.realTimeProcessing).length,
predictiveAnalytics: Array.from(this.digitalTwins.values()).filter(dt => dt.predictiveAnalytics).length,
building: Array.from(this.digitalTwins.values()).filter(dt => dt.modelType === 'building').length,
factory: Array.from(this.digitalTwins.values()).filter(dt => dt.modelType === 'factory').length,
city: Array.from(this.digitalTwins.values()).filter(dt => dt.modelType === 'city').length
},
blockchain: {
networks: this.blockchainNetworks.size,
ethereum: Array.from(this.blockchainNetworks.values()).filter(bc => bc.network === 'ethereum').length,
polygon: Array.from(this.blockchainNetworks.values()).filter(bc => bc.network === 'polygon').length,
smartContracts: Array.from(this.blockchainNetworks.values()).filter(bc => bc.smartContracts).length,
immutableStorage: Array.from(this.blockchainNetworks.values()).filter(bc => bc.immutableStorage).length
},
mixedReality: {
sessions: this.mixedRealitySessions.size,
hololens: Array.from(this.mixedRealitySessions.values()).filter(mr => mr.platform === 'hololens').length,
collaborative: Array.from(this.mixedRealitySessions.values()).filter(mr => mr.collaborativeMode).length,
dataModels3D: Array.from(this.mixedRealitySessions.values()).filter(mr => mr.visualizationType === '3d_data_models').length,
realTimeAnalytics: Array.from(this.mixedRealitySessions.values()).filter(mr => mr.visualizationType === 'real_time_analytics').length
},
totalImplementations: this.quantumProcessors.size + this.digitalTwins.size + this.blockchainNetworks.size + this.mixedRealitySessions.size
};
}
// Private implementation methods
initializeFutureTechnologies() {
console.log('🚀 Initializing Future Technologies: Quantum, Digital Twins, Blockchain, Mixed Reality');
}
async validateQuantumConfiguration(config) {
if (config.maxQubits < 1 || config.maxQubits > 1000) {
throw new Error('Invalid qubit configuration: must be between 1 and 1000');
}
}
async connectToQuantumBackend(config) {
// Simulate quantum backend connection
return { connected: true, backend: config.quantumBackend };
}
async preprocessOptimizationProblem(problem, parameters) {
// Classical preprocessing logic
return { problem, parameters, preprocessed: true };
}
async executeQuantumOptimization(config, problem) {
// Simulate quantum optimization
return {
confidence: 0.95,
qubitsUsed: Math.min(32, config.maxQubits),
energyRatio: 0.85,
result: 'optimized'
};
}
async validateDigitalTwinConfiguration(config) {
if (config.sensors.length === 0) {
throw new Error('Digital twin must have at least one sensor');
}
}
async connectSensors(sensors) {
// Simulate sensor connections
console.log(`Connected to ${sensors.length} sensors: ${sensors.join(', ')}`);
}
async startRealTimeProcessing(twinId, config) {
// Start real-time processing
console.log(`Started real-time processing for digital twin: ${twinId}`);
}
async collectSensorData(sensors) {
// Simulate sensor data collection
const data = {};
sensors.forEach((sensor, index) => {
data[sensor] = Math.random() * 100 + index * 10;
});
return data;
}
async detectAnomalies(sensorData, thresholds) {
const anomalies = [];
Object.entries(sensorData).forEach(([sensor, value]) => {
const threshold = thresholds[sensor];
if (threshold && value > threshold) {
anomalies.push(`${sensor} exceeded threshold: ${value} > ${threshold}`);
}
});
return anomalies;
}
async generatePredictions(twinId, sensorData) {
// Simulate predictions
const predictions = {};
Object.entries(sensorData).forEach(([sensor, value]) => {
predictions[`${sensor}_prediction`] = value * 1.1 + Math.random() * 5;
});
return predictions;
}
calculateRiskScore(sensorData, anomalies, thresholds) {
let riskScore = 0;
// Base risk from anomalies
riskScore += anomalies.length * 20;
// Additional risk from sensor values approaching thresholds
Object.entries(sensorData).forEach(([sensor, value]) => {
const threshold = thresholds[sensor];
if (threshold) {
const proximityRisk = (value / threshold) * 10;
riskScore += Math.min(proximityRisk, 25);
}
});
return Math.min(riskScore, 100);
}
async generateRecommendations(twinId, sensorData, anomalies) {
const recommendations = [];
if (anomalies.length > 0) {
recommendations.push('Investigate anomalous sensor readings immediately');
recommendations.push('Consider implementing additional monitoring');
}
// Add sensor-specific recommendations
Object.entries(sensorData).forEach(([sensor, value]) => {
if (value > 80) {
recommendations.push(`Consider maintenance for ${sensor} (high reading: ${value.toFixed(2)})`);
}
});
return recommendations;
}
async validateBlockchainConfiguration(config) {
if (config.auditScope.length === 0) {
throw new Error('Blockchain configuration must specify at least one audit scope');
}
}
async deployAuditContracts(config) {
// Simulate smart contract deployment
console.log(`Deploying audit smart contracts on ${config.network}`);
}
async createDataHash(data) {
// Create hash of data (simplified)
const dataString = JSON.stringify(data);
let hash = 0;
for (let i = 0; i < dataString.length; i++) {
const char = dataString.charCodeAt(i);
hash = ((hash << 5) - hash) + char;
hash = hash & hash; // Convert to 32-bit integer
}
return `0x${Math.abs(hash).toString(16)}`;
}
async getPreviousHash(networkId) {
// Get previous transaction hash (simplified)
return `0x${Date.now().toString(16)}`;
}
async createBlockchainTransaction(config, data) {
// Simulate blockchain transaction
return {
hash: `0x${Date.now().toString(16)}${Math.random().toString(16).substr(2, 8)}`,
blockNumber: Math.floor(Date.now() / 1000) % 1000000,
timestamp: new Date(),
verified: true,
gasUsed: config.network === 'ethereum' ? Math.floor(Math.random() * 50000) + 21000 : undefined
};
}
async validateMixedRealityConfiguration(config) {
const supportedPlatforms = ['hololens', 'magic_leap', 'meta_quest', 'apple_vision'];
if (!supportedPlatforms.includes(config.platform)) {
throw new Error(`Unsupported MR platform: ${config.platform}`);
}
}
async connectToMRPlatform(config) {
// Simulate MR platform connection
console.log(`Connected to MR platform: ${config.platform}`);
}
async loadDataModelsForMR(config) {
// Simulate loading data models for visualization
console.log(`Loading data models for ${config.visualizationType} visualization`);
}
}
export default FutureTechnologies;
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