bigbasealpha
Version:
Professional Grade Custom Database System - A sophisticated, dependency-free database with encryption, caching, indexing, and web dashboard
1,697 lines (1,448 loc) • 50.3 kB
JavaScript
import { EventEmitter } from 'events';
import crypto from 'crypto';
/**
* BigBaseAlpha Advanced Analytics Engine
* Comprehensive analytics, business intelligence, and data mining capabilities
*/
export class AnalyticsEngine extends EventEmitter {
constructor(config = {}) {
super();
this.config = {
enableRealTimeAnalytics: config.enableRealTimeAnalytics !== false,
enablePredictiveAnalytics: config.enablePredictiveAnalytics !== false,
enableAnomalyDetection: config.enableAnomalyDetection !== false,
enableBusinessIntelligence: config.enableBusinessIntelligence !== false,
enableDataMining: config.enableDataMining !== false,
analyticsRetention: config.analyticsRetention || 30, // days
batchProcessingInterval: config.batchProcessingInterval || 3600000, // 1 hour
anomalyThreshold: config.anomalyThreshold || 0.95,
maxHistorySize: config.maxHistorySize || 10000,
...config
};
this.database = null;
// Analytics data storage
this.metrics = new Map();
this.reports = new Map();
this.insights = new Map();
this.anomalies = new Map();
this.predictions = new Map();
// Business Intelligence components
this.kpis = new Map();
this.dashboards = new Map();
this.dataMiningResults = new Map();
this.correlations = new Map();
this.patterns = new Map();
// Analysis processors
this.processors = new Map();
this.scheduledAnalytics = new Map();
// Statistical models
this.models = new Map();
this.trends = new Map();
// Performance tracking
this.stats = {
totalAnalytics: 0,
reportsGenerated: 0,
anomaliesDetected: 0,
predictionsGenerated: 0,
kpisTracked: 0,
dataSetsAnalyzed: 0,
insightsGenerated: 0,
startTime: null,
lastAnalysis: null
};
this.isInitialized = false;
}
/**
* Initialize analytics engine
*/
async init() {
try {
this.stats.startTime = new Date();
// Initialize built-in processors
this._initializeProcessors();
// Start background analytics
if (this.config.enableRealTimeAnalytics) {
this._startRealTimeAnalytics();
}
this._startBatchProcessing();
this.isInitialized = true;
console.log('✅ Analytics Engine initialized');
this.emit('initialized');
} catch (error) {
throw new Error(`Failed to initialize Analytics Engine: ${error.message}`);
}
}
/**
* Set database instance
*/
setDatabase(database) {
this.database = database;
// Subscribe to database events for real-time analytics
if (this.database && this.config.enableRealTimeAnalytics) {
this.database.on('insert', (data) => this._processEvent('insert', data));
this.database.on('update', (data) => this._processEvent('update', data));
this.database.on('delete', (data) => this._processEvent('delete', data));
this.database.on('query', (data) => this._processEvent('query', data));
}
}
/**
* Initialize built-in processors
*/
_initializeProcessors() {
// Collection analytics processor
this.processors.set('collection_analytics', {
name: 'Collection Analytics',
process: (data) => this._processCollectionAnalytics(data),
schedule: 'hourly',
enabled: true
});
// Query performance processor
this.processors.set('query_performance', {
name: 'Query Performance Analytics',
process: (data) => this._processQueryPerformance(data),
schedule: 'realtime',
enabled: true
});
// User behavior processor
this.processors.set('user_behavior', {
name: 'User Behavior Analytics',
process: (data) => this._processUserBehavior(data),
schedule: 'daily',
enabled: true
});
// Storage analytics processor
this.processors.set('storage_analytics', {
name: 'Storage Analytics',
process: (data) => this._processStorageAnalytics(data),
schedule: 'hourly',
enabled: true
});
// Anomaly detection processor
this.processors.set('anomaly_detection', {
name: 'Anomaly Detection',
process: (data) => this._processAnomalyDetection(data),
schedule: 'realtime',
enabled: this.config.enableAnomalyDetection
});
// Predictive analytics processor
this.processors.set('predictive_analytics', {
name: 'Predictive Analytics',
process: (data) => this._processPredictiveAnalytics(data),
schedule: 'daily',
enabled: this.config.enablePredictiveAnalytics
});
}
/**
* Process real-time event
*/
_processEvent(eventType, data) {
const timestamp = new Date();
const eventId = this._generateId();
const event = {
id: eventId,
type: eventType,
data,
timestamp,
processed: false
};
// Process with real-time processors
for (const [processorId, processor] of this.processors) {
if (processor.enabled && processor.schedule === 'realtime') {
try {
processor.process(event);
} catch (error) {
console.error(`Analytics processor error (${processorId}):`, error);
}
}
}
this.stats.totalAnalytics++;
this.stats.lastAnalysis = timestamp;
}
/**
* Generate analytics report
*/
async generateReport(type, options = {}) {
const reportId = this._generateId();
const startTime = new Date();
try {
let reportData;
switch (type) {
case 'performance':
reportData = await this._generatePerformanceReport(options);
break;
case 'usage':
reportData = await this._generateUsageReport(options);
break;
case 'growth':
reportData = await this._generateGrowthReport(options);
break;
case 'security':
reportData = await this._generateSecurityReport(options);
break;
case 'custom':
reportData = await this._generateCustomReport(options);
break;
default:
throw new Error(`Unknown report type: ${type}`);
}
const report = {
id: reportId,
type,
data: reportData,
options,
generatedAt: startTime,
duration: Date.now() - startTime,
format: options.format || 'json'
};
this.reports.set(reportId, report);
this.stats.reportsGenerated++;
this.emit('reportGenerated', report);
return report;
} catch (error) {
throw new Error(`Failed to generate ${type} report: ${error.message}`);
}
}
/**
* Generate performance report
*/
async _generatePerformanceReport(options = {}) {
const timeframe = options.timeframe || '24h';
const collections = options.collections || [];
const data = {
summary: {
totalQueries: await this._getQueryCount(timeframe),
averageResponseTime: await this._getAverageResponseTime(timeframe),
slowQueries: await this._getSlowQueries(timeframe),
errorRate: await this._getErrorRate(timeframe)
},
queryPatterns: await this._getQueryPatterns(timeframe),
performanceTrends: await this._getPerformanceTrends(timeframe),
recommendations: await this._getPerformanceRecommendations()
};
return data;
}
/**
* Generate usage report
*/
async _generateUsageReport(options = {}) {
const timeframe = options.timeframe || '30d';
const data = {
summary: {
totalUsers: await this._getUserCount(timeframe),
activeUsers: await this._getActiveUserCount(timeframe),
totalSessions: await this._getSessionCount(timeframe),
averageSessionDuration: await this._getAverageSessionDuration(timeframe)
},
usagePatterns: await this._getUsagePatterns(timeframe),
popularFeatures: await this._getPopularFeatures(timeframe),
geographicDistribution: await this._getGeographicDistribution(timeframe)
};
return data;
}
/**
* Generate growth report
*/
async _generateGrowthReport(options = {}) {
const timeframe = options.timeframe || '90d';
const data = {
summary: {
userGrowthRate: await this._getUserGrowthRate(timeframe),
dataGrowthRate: await this._getDataGrowthRate(timeframe),
queryGrowthRate: await this._getQueryGrowthRate(timeframe)
},
growthTrends: await this._getGrowthTrends(timeframe),
projections: await this._getGrowthProjections(timeframe),
milestones: await this._getGrowthMilestones(timeframe)
};
return data;
}
/**
* Detect anomalies in data
*/
async detectAnomalies(collection, field, options = {}) {
if (!this.database) {
throw new Error('Database not available');
}
const documents = await this.database.find(collection, {});
const values = documents.map(doc => doc[field]).filter(val => val !== undefined);
const anomalies = this._statisticalAnomalyDetection(values, options);
const result = {
id: this._generateId(),
collection,
field,
anomalies: anomalies.map((anomaly, index) => ({
index,
value: anomaly.value,
score: anomaly.score,
severity: anomaly.score > this.config.anomalyThreshold ? 'high' : 'medium'
})),
summary: {
totalValues: values.length,
anomaliesFound: anomalies.length,
anomalyRate: (anomalies.length / values.length) * 100
},
detectedAt: new Date()
};
this.anomalies.set(result.id, result);
this.stats.anomaliesDetected += anomalies.length;
return result;
}
/**
* Generate predictions
*/
async generatePredictions(type, data, options = {}) {
const predictionId = this._generateId();
try {
let predictions;
switch (type) {
case 'linear_trend':
predictions = this._linearTrendPrediction(data, options);
break;
case 'seasonal':
predictions = this._seasonalPrediction(data, options);
break;
case 'growth':
predictions = this._growthPrediction(data, options);
break;
default:
throw new Error(`Unknown prediction type: ${type}`);
}
const result = {
id: predictionId,
type,
predictions,
confidence: this._calculateConfidence(predictions, data),
generatedAt: new Date(),
options
};
this.predictions.set(predictionId, result);
this.stats.predictionsGenerated++;
return result;
} catch (error) {
throw new Error(`Failed to generate ${type} predictions: ${error.message}`);
}
}
/**
* Get insights for collection
*/
async getInsights(collection, options = {}) {
if (!this.database) {
throw new Error('Database not available');
}
const documents = await this.database.find(collection, {});
const insights = {
id: this._generateId(),
collection,
summary: {
totalDocuments: documents.length,
fieldAnalysis: this._analyzeFields(documents),
dataQuality: this._analyzeDataQuality(documents),
patterns: this._findPatterns(documents)
},
recommendations: this._generateInsightRecommendations(documents),
generatedAt: new Date()
};
this.insights.set(insights.id, insights);
return insights;
}
/**
* Create custom analytics dashboard
*/
createDashboard(name, config) {
const dashboardId = this._generateId();
const dashboard = {
id: dashboardId,
name,
config,
widgets: config.widgets || [],
refreshInterval: config.refreshInterval || 60000,
createdAt: new Date(),
lastUpdated: new Date()
};
return dashboard;
}
/**
* Statistical anomaly detection using Z-score
*/
_statisticalAnomalyDetection(values, options = {}) {
const threshold = options.threshold || 2.5;
const mean = values.reduce((sum, val) => sum + val, 0) / values.length;
const variance = values.reduce((sum, val) => sum + Math.pow(val - mean, 2), 0) / values.length;
const stdDev = Math.sqrt(variance);
return values
.map((value, index) => ({
index,
value,
score: Math.abs((value - mean) / stdDev)
}))
.filter(item => item.score > threshold);
}
/**
* Linear trend prediction
*/
_linearTrendPrediction(data, options = {}) {
const periods = options.periods || 10;
const n = data.length;
// Calculate linear regression
const sumX = data.reduce((sum, _, i) => sum + i, 0);
const sumY = data.reduce((sum, val) => sum + val, 0);
const sumXY = data.reduce((sum, val, i) => sum + i * val, 0);
const sumXX = data.reduce((sum, _, i) => sum + i * i, 0);
const slope = (n * sumXY - sumX * sumY) / (n * sumXX - sumX * sumX);
const intercept = (sumY - slope * sumX) / n;
// Generate predictions
const predictions = [];
for (let i = n; i < n + periods; i++) {
predictions.push({
period: i,
value: slope * i + intercept,
trend: slope > 0 ? 'increasing' : slope < 0 ? 'decreasing' : 'stable'
});
}
return predictions;
}
/**
* Analyze data fields
*/
_analyzeFields(documents) {
const fieldStats = {};
documents.forEach(doc => {
Object.keys(doc).forEach(field => {
if (!fieldStats[field]) {
fieldStats[field] = {
type: typeof doc[field],
count: 0,
nullCount: 0,
uniqueValues: new Set()
};
}
fieldStats[field].count++;
if (doc[field] === null || doc[field] === undefined) {
fieldStats[field].nullCount++;
} else {
fieldStats[field].uniqueValues.add(doc[field]);
}
});
});
// Convert to analysis format
const analysis = {};
Object.keys(fieldStats).forEach(field => {
const stats = fieldStats[field];
analysis[field] = {
type: stats.type,
completeness: ((stats.count - stats.nullCount) / stats.count) * 100,
uniqueness: (stats.uniqueValues.size / stats.count) * 100,
cardinality: stats.uniqueValues.size
};
});
return analysis;
}
/**
* Start real-time analytics processing
*/
_startRealTimeAnalytics() {
// Process metrics every 5 seconds
setInterval(() => {
this._processRealTimeMetrics();
}, 5000);
}
/**
* Start batch processing
*/
_startBatchProcessing() {
setInterval(() => {
this._processBatchAnalytics();
}, this.config.batchProcessingInterval);
}
/**
* Process real-time metrics
*/
_processRealTimeMetrics() {
const timestamp = new Date();
// Example real-time metrics
const metrics = {
timestamp,
activeConnections: Math.floor(Math.random() * 100) + 50,
queriesPerSecond: Math.floor(Math.random() * 50) + 10,
memoryUsage: Math.floor(Math.random() * 30) + 50,
cpuUsage: Math.floor(Math.random() * 40) + 10
};
this.metrics.set(`realtime_${timestamp.getTime()}`, metrics);
this.emit('realTimeMetrics', metrics);
}
/**
* Process batch analytics
*/
async _processBatchAnalytics() {
console.log('🔄 Processing batch analytics...');
for (const [processorId, processor] of this.processors) {
if (processor.enabled && processor.schedule !== 'realtime') {
try {
await processor.process();
console.log(`✅ Processed ${processor.name}`);
} catch (error) {
console.error(`❌ Batch analytics error (${processorId}):`, error);
}
}
}
}
/**
* Get analytics statistics
*/
getStats() {
return {
...this.stats,
metrics: {
total: this.metrics.size,
realTimeProcessors: Array.from(this.processors.values())
.filter(p => p.schedule === 'realtime' && p.enabled).length
},
reports: {
total: this.reports.size,
byType: this._getReportsByType()
},
insights: {
total: this.insights.size
},
anomalies: {
total: this.anomalies.size,
recent: Array.from(this.anomalies.values())
.filter(a => Date.now() - a.detectedAt < 86400000).length
},
predictions: {
total: this.predictions.size
}
};
}
/**
* Get reports by type
*/
_getReportsByType() {
const byType = {};
for (const report of this.reports.values()) {
byType[report.type] = (byType[report.type] || 0) + 1;
}
return byType;
}
/**
* Generate unique ID
*/
_generateId() {
return `analytics_${Date.now()}_${crypto.randomBytes(4).toString('hex')}`;
}
/**
* Placeholder methods for report generation
*/
async _getQueryCount(timeframe) { return Math.floor(Math.random() * 10000) + 1000; }
async _getAverageResponseTime(timeframe) { return Math.floor(Math.random() * 50) + 10; }
async _getSlowQueries(timeframe) { return Math.floor(Math.random() * 20) + 5; }
async _getErrorRate(timeframe) { return Math.random() * 5; }
async _getQueryPatterns(timeframe) { return []; }
async _getPerformanceTrends(timeframe) { return []; }
async _getPerformanceRecommendations() { return ['Add indexes', 'Optimize queries']; }
async _getUserCount(timeframe) { return Math.floor(Math.random() * 1000) + 100; }
async _getActiveUserCount(timeframe) { return Math.floor(Math.random() * 500) + 50; }
async _getSessionCount(timeframe) { return Math.floor(Math.random() * 5000) + 500; }
async _getAverageSessionDuration(timeframe) { return Math.floor(Math.random() * 1800) + 300; }
async _getUsagePatterns(timeframe) { return []; }
async _getPopularFeatures(timeframe) { return []; }
async _getGeographicDistribution(timeframe) { return []; }
async _getUserGrowthRate(timeframe) { return Math.random() * 20 + 5; }
async _getDataGrowthRate(timeframe) { return Math.random() * 30 + 10; }
async _getQueryGrowthRate(timeframe) { return Math.random() * 25 + 5; }
async _getGrowthTrends(timeframe) { return []; }
async _getGrowthProjections(timeframe) { return []; }
async _getGrowthMilestones(timeframe) { return []; }
_analyzeDataQuality(documents) { return { score: 85, issues: [] }; }
_findPatterns(documents) { return []; }
_generateInsightRecommendations(documents) { return []; }
_calculateConfidence(predictions, data) { return Math.random() * 30 + 70; }
_seasonalPrediction(data, options) { return []; }
_growthPrediction(data, options) { return []; }
/**
* Process collection analytics
*/
_processCollectionAnalytics(data) {
// Implementation for collection analytics
}
/**
* Process query performance analytics
*/
_processQueryPerformance(data) {
// Implementation for query performance
}
/**
* Process user behavior analytics
*/
_processUserBehavior(data) {
// Implementation for user behavior
}
/**
* Process storage analytics
*/
_processStorageAnalytics(data) {
// Implementation for storage analytics
}
/**
* Process anomaly detection
*/
_processAnomalyDetection(data) {
// Implementation for anomaly detection
}
/**
* Process predictive analytics
*/
_processPredictiveAnalytics(data) {
// Implementation for predictive analytics
}
/**
* Generate custom report
*/
async _generateCustomReport(options) {
return {
message: 'Custom report generated',
options
};
}
/**
* Generate security report
*/
async _generateSecurityReport(options) {
return {
summary: {
securityScore: 85,
vulnerabilities: 2,
lastSecurityScan: new Date()
},
recommendations: [
'Enable encryption at rest',
'Implement stronger authentication'
]
};
}
// ===== BUSINESS INTELLIGENCE METHODS =====
/**
* Create KPI (Key Performance Indicator)
*/
async createKPI(kpiConfig) {
const kpi = {
id: kpiConfig.id || this.generateId('kpi'),
name: kpiConfig.name,
type: kpiConfig.type || 'metric',
calculation: kpiConfig.calculation || 'average',
target: kpiConfig.target,
thresholds: kpiConfig.thresholds || { warning: 0, critical: 0 },
unit: kpiConfig.unit || '',
description: kpiConfig.description || '',
createdAt: Date.now(),
updatedAt: Date.now(),
currentValue: null,
previousValue: null,
trend: 'stable',
status: 'unknown',
history: []
};
this.kpis.set(kpi.id, kpi);
this.stats.kpisTracked++;
console.log(`📊 KPI created: ${kpi.name} (${kpi.id})`);
this.emit('kpiCreated', kpi);
return kpi;
}
/**
* Update KPI value
*/
async updateKPI(kpiId, value, timestamp = Date.now()) {
const kpi = this.kpis.get(kpiId);
if (!kpi) {
throw new Error(`KPI not found: ${kpiId}`);
}
kpi.previousValue = kpi.currentValue;
kpi.currentValue = value;
kpi.updatedAt = timestamp;
// Add to history
kpi.history.push({ value, timestamp });
// Keep only recent history
if (kpi.history.length > this.config.maxHistorySize) {
kpi.history = kpi.history.slice(-this.config.maxHistorySize);
}
// Calculate trend
kpi.trend = this.calculateKPITrend(kpi);
// Determine status
kpi.status = this.determineKPIStatus(kpi);
this.emit('kpiUpdated', kpi);
// Check thresholds and generate alerts
this.checkKPIThresholds(kpi);
return kpi;
}
/**
* Calculate KPI trend
*/
calculateKPITrend(kpi) {
if (kpi.previousValue === null || kpi.currentValue === null) {
return 'stable';
}
const change = ((kpi.currentValue - kpi.previousValue) / kpi.previousValue) * 100;
if (Math.abs(change) < 1) return 'stable';
return change > 0 ? 'increasing' : 'decreasing';
}
/**
* Determine KPI status based on thresholds
*/
determineKPIStatus(kpi) {
if (kpi.currentValue === null) return 'unknown';
const { target, thresholds } = kpi;
const value = kpi.currentValue;
if (kpi.type === 'performance' || kpi.type === 'time') {
// Lower is better for performance metrics
if (value <= target) return 'good';
if (value <= thresholds.warning) return 'warning';
return 'critical';
} else {
// Higher is better for most other metrics
if (value >= target) return 'good';
if (value >= thresholds.warning) return 'warning';
return 'critical';
}
}
/**
* Check KPI thresholds and generate alerts
*/
checkKPIThresholds(kpi) {
if (kpi.status === 'critical' || kpi.status === 'warning') {
const insight = {
type: 'kpi_threshold_alert',
severity: kpi.status,
kpiId: kpi.id,
kpiName: kpi.name,
message: `${kpi.name} has reached ${kpi.status} threshold`,
value: kpi.currentValue,
target: kpi.target,
timestamp: Date.now(),
recommendations: this.generateKPIRecommendations(kpi)
};
this.addInsight(insight);
}
}
/**
* Generate KPI recommendations
*/
generateKPIRecommendations(kpi) {
const recommendations = [];
switch (kpi.type) {
case 'performance':
recommendations.push('Review system performance metrics');
recommendations.push('Optimize slow-running processes');
break;
case 'quality':
recommendations.push('Implement data validation rules');
recommendations.push('Review data entry processes');
break;
case 'engagement':
recommendations.push('Analyze user behavior patterns');
recommendations.push('Consider user experience improvements');
break;
default:
recommendations.push('Review current processes and procedures');
recommendations.push('Consider process optimization');
}
return recommendations;
}
/**
* Create business dashboard
*/
async createDashboard(dashboardConfig) {
const dashboard = {
id: dashboardConfig.id || this.generateId('dashboard'),
name: dashboardConfig.name,
description: dashboardConfig.description || '',
widgets: dashboardConfig.widgets || [],
layout: dashboardConfig.layout || 'grid',
refreshRate: dashboardConfig.refreshRate || 30000, // 30 seconds
createdAt: Date.now(),
updatedAt: Date.now(),
views: 0,
lastViewed: null,
isPublic: dashboardConfig.isPublic || false
};
this.dashboards.set(dashboard.id, dashboard);
console.log(`📈 Dashboard created: ${dashboard.name} (${dashboard.id})`);
this.emit('dashboardCreated', dashboard);
return dashboard;
}
/**
* Perform data mining analysis
*/
async performDataMining(collection, options = {}) {
const startTime = Date.now();
console.log(`⛏️ Starting data mining analysis on collection: ${collection}...`);
const analysis = {
id: this.generateId('mining'),
collection,
startTime,
status: 'running',
patterns: [],
anomalies: [],
correlations: [],
insights: [],
statistics: {}
};
try {
// Get data from collection
const data = await this.database.query(collection, {});
if (!data || data.length === 0) {
throw new Error('No data found for analysis');
}
// Pattern detection
analysis.patterns = await this.detectPatterns(data, options);
// Anomaly detection
analysis.anomalies = await this.detectDataAnomalies(data, options);
// Correlation analysis
analysis.correlations = await this.analyzeCorrelations(data, options);
// Generate statistics
analysis.statistics = this.generateDataStatistics(data);
// Generate insights
analysis.insights = this.generateMiningInsights(analysis);
analysis.status = 'completed';
analysis.completedAt = Date.now();
analysis.executionTime = analysis.completedAt - startTime;
this.dataMiningResults.set(analysis.id, analysis);
this.stats.dataSetsAnalyzed++;
console.log(`✅ Data mining completed: ${analysis.patterns.length} patterns, ${analysis.anomalies.length} anomalies found`);
this.emit('dataMiningCompleted', analysis);
return analysis;
} catch (error) {
analysis.status = 'failed';
analysis.error = error.message;
analysis.failedAt = Date.now();
console.error('❌ Data mining failed:', error);
this.emit('dataMiningFailed', analysis);
throw error;
}
}
/**
* Detect patterns in data
*/
async detectPatterns(data, options) {
const patterns = [];
if (!Array.isArray(data) || data.length === 0) {
return patterns;
}
// Frequency patterns
const frequencyPatterns = this.detectFrequencyPatterns(data);
patterns.push(...frequencyPatterns);
// Sequence patterns
const sequencePatterns = this.detectSequencePatterns(data);
patterns.push(...sequencePatterns);
// Temporal patterns
const temporalPatterns = this.detectTemporalPatterns(data);
patterns.push(...temporalPatterns);
return patterns;
}
/**
* Detect frequency patterns
*/
detectFrequencyPatterns(data) {
const frequencies = {};
const patterns = [];
// Count frequencies
data.forEach(item => {
const key = JSON.stringify(item);
frequencies[key] = (frequencies[key] || 0) + 1;
});
// Find high-frequency patterns
const sortedFreqs = Object.entries(frequencies)
.sort(([, a], [, b]) => b - a)
.slice(0, 10); // Top 10
sortedFreqs.forEach(([pattern, frequency], index) => {
if (frequency > 1) {
patterns.push({
type: 'frequency',
pattern: JSON.parse(pattern),
frequency,
rank: index + 1,
confidence: (frequency / data.length) * 100
});
}
});
return patterns;
}
/**
* Detect sequence patterns
*/
detectSequencePatterns(data) {
const patterns = [];
// Simple sequence detection (consecutive patterns)
for (let i = 0; i < data.length - 2; i++) {
const sequence = data.slice(i, i + 3);
// Check if this sequence appears elsewhere
let count = 0;
for (let j = 0; j < data.length - 2; j++) {
const compareSequence = data.slice(j, j + 3);
if (JSON.stringify(sequence) === JSON.stringify(compareSequence)) {
count++;
}
}
if (count > 1) {
patterns.push({
type: 'sequence',
pattern: sequence,
frequency: count,
confidence: (count / (data.length - 2)) * 100
});
}
}
return patterns;
}
/**
* Detect temporal patterns
*/
detectTemporalPatterns(data) {
const patterns = [];
// Look for timestamp-based patterns
const timestampData = data.filter(item =>
item.timestamp || item.createdAt || item.date
);
if (timestampData.length > 0) {
// Group by hour of day
const hourlyDistribution = {};
timestampData.forEach(item => {
const timestamp = item.timestamp || item.createdAt || item.date;
const hour = new Date(timestamp).getHours();
hourlyDistribution[hour] = (hourlyDistribution[hour] || 0) + 1;
});
// Find peak hours
const peakHour = Object.entries(hourlyDistribution)
.reduce(([maxHour, maxCount], [hour, count]) =>
count > maxCount ? [hour, count] : [maxHour, maxCount]
, ['0', 0]);
if (peakHour[1] > 0) {
patterns.push({
type: 'temporal',
pattern: 'peak_hour',
hour: parseInt(peakHour[0]),
frequency: peakHour[1],
confidence: (peakHour[1] / timestampData.length) * 100
});
}
}
return patterns;
}
/**
* Detect data anomalies
*/
async detectDataAnomalies(data, options) {
const anomalies = [];
if (!Array.isArray(data) || data.length === 0) {
return anomalies;
}
// Statistical anomalies
const statisticalAnomalies = this.detectStatisticalAnomalies(data);
anomalies.push(...statisticalAnomalies);
// Pattern-based anomalies
const patternAnomalies = this.detectPatternAnomalies(data);
anomalies.push(...patternAnomalies);
return anomalies;
}
/**
* Detect statistical anomalies
*/
detectStatisticalAnomalies(data) {
const anomalies = [];
// Find numerical fields
const numericalFields = this.identifyNumericalFields(data);
numericalFields.forEach(field => {
const values = data.map(item => item[field]).filter(val => typeof val === 'number');
if (values.length > 0) {
const mean = values.reduce((sum, val) => sum + val, 0) / values.length;
const stdDev = Math.sqrt(
values.reduce((sum, val) => sum + Math.pow(val - mean, 2), 0) / values.length
);
// Z-score based anomaly detection
values.forEach((value, index) => {
const zScore = Math.abs((value - mean) / stdDev);
if (zScore > 2.5) { // 2.5 standard deviations
anomalies.push({
type: 'statistical',
field,
value,
zScore,
index,
severity: zScore > 3 ? 'high' : 'medium'
});
}
});
}
});
return anomalies;
}
/**
* Detect pattern-based anomalies
*/
detectPatternAnomalies(data) {
const anomalies = [];
// Check for unusual patterns compared to normal behavior
if (data.length > 10) {
const recentData = data.slice(-10); // Last 10 records
const historicalData = data.slice(0, -10);
// Compare recent patterns with historical
const recentPatterns = this.extractSimplePatterns(recentData);
const historicalPatterns = this.extractSimplePatterns(historicalData);
recentPatterns.forEach(pattern => {
const historicalMatch = historicalPatterns.find(hp =>
JSON.stringify(hp.pattern) === JSON.stringify(pattern.pattern)
);
if (!historicalMatch) {
anomalies.push({
type: 'pattern',
pattern: pattern.pattern,
description: 'New pattern not seen in historical data',
severity: 'medium'
});
}
});
}
return anomalies;
}
/**
* Extract simple patterns from data
*/
extractSimplePatterns(data) {
const patterns = [];
// Extract field value patterns
if (data.length > 0) {
const fields = Object.keys(data[0]);
fields.forEach(field => {
const uniqueValues = [...new Set(data.map(item => item[field]))];
if (uniqueValues.length < data.length * 0.8) { // Not too diverse
patterns.push({
field,
pattern: uniqueValues,
frequency: data.length
});
}
});
}
return patterns;
}
/**
* Analyze correlations in data
*/
async analyzeCorrelations(data, options) {
const correlations = [];
if (!Array.isArray(data) || data.length < 2) {
return correlations;
}
const numericalFields = this.identifyNumericalFields(data);
// Calculate correlations between numerical fields
for (let i = 0; i < numericalFields.length; i++) {
for (let j = i + 1; j < numericalFields.length; j++) {
const field1 = numericalFields[i];
const field2 = numericalFields[j];
const correlation = this.calculateCorrelation(data, field1, field2);
if (Math.abs(correlation) > 0.3) { // Significant correlation
correlations.push({
field1,
field2,
correlation,
strength: this.getCorrelationStrength(correlation),
direction: correlation > 0 ? 'positive' : 'negative'
});
}
}
}
return correlations.sort((a, b) => Math.abs(b.correlation) - Math.abs(a.correlation));
}
/**
* Calculate correlation coefficient
*/
calculateCorrelation(data, field1, field2) {
const values1 = data.map(item => item[field1]).filter(val => typeof val === 'number');
const values2 = data.map(item => item[field2]).filter(val => typeof val === 'number');
if (values1.length !== values2.length || values1.length === 0) {
return 0;
}
const mean1 = values1.reduce((sum, val) => sum + val, 0) / values1.length;
const mean2 = values2.reduce((sum, val) => sum + val, 0) / values2.length;
let numerator = 0;
let sumSq1 = 0;
let sumSq2 = 0;
for (let i = 0; i < values1.length; i++) {
const diff1 = values1[i] - mean1;
const diff2 = values2[i] - mean2;
numerator += diff1 * diff2;
sumSq1 += diff1 * diff1;
sumSq2 += diff2 * diff2;
}
const denominator = Math.sqrt(sumSq1 * sumSq2);
return denominator === 0 ? 0 : numerator / denominator;
}
/**
* Get correlation strength description
*/
getCorrelationStrength(correlation) {
const abs = Math.abs(correlation);
if (abs >= 0.8) return 'very strong';
if (abs >= 0.6) return 'strong';
if (abs >= 0.4) return 'moderate';
if (abs >= 0.2) return 'weak';
return 'very weak';
}
/**
* Identify numerical fields in data
*/
identifyNumericalFields(data) {
if (data.length === 0) return [];
const fields = Object.keys(data[0]);
return fields.filter(field => {
const sampleValues = data.slice(0, Math.min(100, data.length))
.map(item => item[field])
.filter(val => val !== null && val !== undefined);
const numericalCount = sampleValues.filter(val => typeof val === 'number').length;
return numericalCount > sampleValues.length * 0.8; // 80% numerical
});
}
/**
* Generate data statistics
*/
generateDataStatistics(data) {
const stats = {
totalRecords: data.length,
fields: {},
summary: {}
};
if (data.length === 0) return stats;
const fields = Object.keys(data[0]);
fields.forEach(field => {
const values = data.map(item => item[field]).filter(val => val !== null && val !== undefined);
const numericalValues = values.filter(val => typeof val === 'number');
stats.fields[field] = {
totalValues: values.length,
nullValues: data.length - values.length,
uniqueValues: new Set(values).size,
dataType: this.inferDataType(values),
statistics: numericalValues.length > 0 ? {
min: Math.min(...numericalValues),
max: Math.max(...numericalValues),
mean: numericalValues.reduce((sum, val) => sum + val, 0) / numericalValues.length,
median: this.calculateMedian(numericalValues)
} : null
};
});
return stats;
}
/**
* Infer data type
*/
inferDataType(values) {
if (values.length === 0) return 'unknown';
const types = values.map(val => typeof val);
const typeCounts = types.reduce((acc, type) => {
acc[type] = (acc[type] || 0) + 1;
return acc;
}, {});
return Object.entries(typeCounts)
.sort(([, a], [, b]) => b - a)[0][0];
}
/**
* Calculate median
*/
calculateMedian(values) {
const sorted = [...values].sort((a, b) => a - b);
const mid = Math.floor(sorted.length / 2);
return sorted.length % 2 !== 0
? sorted[mid]
: (sorted[mid - 1] + sorted[mid]) / 2;
}
/**
* Generate mining insights
*/
generateMiningInsights(analysis) {
const insights = [];
// Pattern insights
if (analysis.patterns.length > 0) {
const topPattern = analysis.patterns.reduce((top, pattern) =>
pattern.confidence > top.confidence ? pattern : top
);
insights.push({
type: 'pattern_discovery',
message: `Discovered ${analysis.patterns.length} patterns, with highest confidence: ${topPattern.confidence.toFixed(1)}%`,
impact: 'medium',
actionable: true
});
}
// Anomaly insights
if (analysis.anomalies.length > 0) {
const highSeverityAnomalies = analysis.anomalies.filter(a => a.severity === 'high');
insights.push({
type: 'anomaly_detection',
message: `Found ${analysis.anomalies.length} anomalies (${highSeverityAnomalies.length} high severity)`,
impact: 'high',
actionable: true
});
}
// Correlation insights
if (analysis.correlations.length > 0) {
const strongCorrelations = analysis.correlations.filter(c =>
Math.abs(c.correlation) > 0.7
);
insights.push({
type: 'correlation_analysis',
message: `Identified ${analysis.correlations.length} correlations (${strongCorrelations.length} strong)`,
impact: 'medium',
actionable: true
});
}
return insights;
}
/**
* Add insight to the system
*/
addInsight(insight) {
insight.id = this.generateId('insight');
insight.timestamp = insight.timestamp || Date.now();
this.insights.set(insight.id, insight);
this.stats.insightsGenerated++;
this.emit('insightGenerated', insight);
console.log(`💡 Insight generated: ${insight.message}`);
}
/**
* Generate business intelligence report
*/
async generateBIReport(reportConfig) {
const startTime = Date.now();
console.log(`📋 Generating BI report: ${reportConfig.name}...`);
const report = {
id: reportConfig.id || this.generateId('bi_report'),
name: reportConfig.name,
type: reportConfig.type || 'comprehensive',
parameters: reportConfig.parameters || {},
createdAt: startTime,
status: 'generating',
sections: {},
insights: [],
recommendations: []
};
try {
// Generate different sections based on report type
switch (report.type) {
case 'executive':
report.sections = await this.generateExecutiveReport(report.parameters);
break;
case 'operational':
report.sections = await this.generateOperationalReport(report.parameters);
break;
case 'financial':
report.sections = await this.generateFinancialReport(report.parameters);
break;
case 'comprehensive':
default:
report.sections = await this.generateComprehensiveReport(report.parameters);
}
// Generate insights and recommendations
report.insights = this.generateReportInsights(report);
report.recommendations = this.generateReportRecommendations(report);
report.status = 'completed';
report.completedAt = Date.now();
report.executionTime = report.completedAt - startTime;
this.reports.set(report.id, report);
this.stats.reportsGenerated++;
console.log(`✅ BI report generated: ${report.name} (${report.executionTime}ms)`);
this.emit('biReportGenerated', report);
return report;
} catch (error) {
report.status = 'failed';
report.error = error.message;
report.failedAt = Date.now();
console.error(`❌ BI report generation failed: ${report.name}`, error);
this.emit('biReportFailed', report);
throw error;
}
}
/**
* Generate executive report
*/
async generateExecutiveReport(parameters) {
return {
summary: {
title: 'Executive Summary',
kpis: this.getKPISummary(),
trends: this.getTrendSummary(),
alerts: this.getActiveAlerts()
},
performance: {
title: 'Performance Overview',
metrics: await this.getPerformanceMetrics(),
comparisons: this.getPerformanceComparisons()
},
growth: {
title: 'Growth Analysis',
metrics: this.getGrowthMetrics(),
projections: this.getGrowthProjections()
}
};
}
/**
* Generate operational report
*/
async generateOperationalReport(parameters) {
return {
system: {
title: 'System Health',
metrics: await this.getSystemMetrics(),
alerts: this.getSystemAlerts(),
resources: this.getResourceUtilization()
},
performance: {
title: 'Performance Metrics',
queryPerformance: this.getQueryPerformance(),
throughput: this.getThroughputMetrics(),
errors: this.getErrorMetrics()
},
usage: {
title: 'Usage Analytics',
patterns: this.getUsagePatterns(),
peaks: this.getUsagePeaks(),
trends: this.getUsageTrends()
}
};
}
/**
* Generate financial report
*/
async generateFinancialReport(parameters) {
return {
revenue: {
title: 'Revenue Analysis',
current: this.getCurrentRevenue(),
trends: this.getRevenueTrends(),
projections: this.getRevenueProjections()
},
costs: {
title: 'Cost Analysis',
breakdown: this.getCostBreakdown(),
trends: this.getCostTrends(),
optimization: this.getCostOptimization()
},
profitability: {
title: 'Profitability Metrics',
margins: this.getProfitMargins(),
roi: this.getROIMetrics(),
efficiency: this.getEfficiencyMetrics()
}
};
}
/**
* Generate comprehensive report
*/
async generateComprehensiveReport(parameters) {
const executive = await this.generateExecutiveReport(parameters);
const operational = await this.generateOperationalReport(parameters);
const financial = await this.generateFinancialReport(parameters);
return {
...executive,
...operational,
...financial,
analytics: {
title: 'Analytics Summary',
dataMining: this.getDataMiningResults(),
predictions: this.getPredictionResults(),
correlations: this.getCorrelationResults()
}
};
}
/**
* Helper methods for report generation
*/
getKPISummary() {
const kpis = Array.from(this.kpis.values());
return {
total: kpis.length,
good: kpis.filter(kpi => kpi.status === 'good').length,
warning: kpis.filter(kpi => kpi.status === 'warning').length,
critical: kpis.filter(kpi => kpi.status === 'critical').length,
topKPIs: kpis.slice(0, 5)
};
}
getTrendSummary() {
// Mock trend data
return {
overall: 'positive',
performance: 'stable',
growth: 'increasing',
satisfaction: 'improving'
};
}
getActiveAlerts() {
const recentInsights = Array.from(this.insights.values())
.filter(insight => Date.now() - insight.timestamp < 86400000) // Last 24 hours
.filter(insight => insight.severity === 'critical' || insight.severity === 'warning');
return recentInsights.slice(0, 10);
}
async getPerformanceMetrics() {
// Mock performance data
return {
averageResponseTime: 85.3,
throughput: 1540,
errorRate: 0.3,
availability: 99.8
};
}
getPerformanceComparisons() {
return {
previousPeriod: {
responseTime: { current: 85.3, previous: 92.1, change: -7.4 },
throughput: { current: 1540, previous: 1420, change: 8.5 },
errorRate: { current: 0.3, previous: 0.5, change: -40.0 }
}
};
}
/**
* Get analytics summary for API
*/
getAnalyticsSummary() {
return {
kpis: {
total: this.kpis.size,
critical: Array.from(this.kpis.values()).filter(kpi => kpi.status === 'critical').length,
warning: Array.from(this.kpis.values()).filter(kpi => kpi.status === 'warning').length
},
reports: {
total: this.reports.size,
completed: Array.from(this.reports.values()).filter(r => r.status === 'completed').length,
failed: Array.from(this.reports.values()).filter(r => r.status === 'failed').length
},
insights: {
total: this.insights.size,
recent: Array.from(this.insights.values()).filter(i => Date.now() - i.timestamp < 86400000).length
},
dataMining: {
total: this.dataMiningResults.size,
patterns: Array.from(this.dataMiningResults.values()).reduce((sum, result) => sum + result.patterns.length, 0),
anomalies: Array.from(this.dataMiningResults.values()).reduce((sum, result) => sum + result.anomalies.length, 0)
},
statistics: this.stats
};
}
// Mock data methods (to be replaced with real implementations)
getGrowthMetrics() { return { userGrowth: 15.3, revenueGrowth: 8.9, dataGrowth: 22.1 }; }
getGrowthProjections() { return { next30Days: 12.5, next90Days: 35.2 }; }
getSystemMetrics() { return { cpu: 65.4, memory: 78.2, disk: 45.1 }; }
getSystemAlerts() { return []; }
getResourceUtilization() { return { peak: 85.3, average: 67.2 }; }
getQueryPerformance() { return { average: 85.3, p95: 150.2, p99: 280.5 }; }
getThroughputMetrics() { return { qps: 1540, peak: 2340 }; }
getErrorMetrics() { return { rate: 0.3, total: 45 }; }
getUsagePatterns() { return []; }
getUsagePeaks() { return []; }
getUsageTrends() { return []; }
getCurrentRevenue() { return { monthly: 125000, annual: 1500000 }; }
getRevenueTrends() { return { growth: 8.9, trend: 'increasing' }; }
getRevenueProjections() { return { nextMonth: 135000, nextQuarter: 420000 }; }
getCostBreakdown() { return { infrastructure: 45000, personnel: 85000, other: 15000 }; }
getCostTrends() { return { infrastructure: 5.2, personnel: 3.1 }; }
getCostOptimization() { return { potential: 12000, recommendations: [] }; }
getProfitMargins() { return { gross: 78.5, net: 23.4 }; }
getROIMetrics() { return { current: 156.7, target: 180.0 }; }
getEfficiencyMetrics() { return { operational: 87.3, financial: 92.1 }; }
getDataMiningResults() { return Array.from(this.dataMiningResults.values()).slice(0, 5); }
getPredictionResults() {