Back-End Modules
Task Prioritization Engine

Task Prioritization Engine

Overview

The Task Prioritization Engine (TPE) is the core component of PDeck that analyzes tasks and determines their relative importance and urgency. It uses various factors such as deadlines, user preferences, task relationships, and historical data to create an optimized task list for the user.

Key Responsibilities

  1. Analyze tasks and their attributes
  2. Consider user preferences and work patterns
  3. Evaluate task relationships and dependencies
  4. Calculate priority scores for tasks
  5. Generate an ordered list of tasks
  6. Continuously update priorities as new information becomes available

Technology Stack

  • Node.js for the core implementation
  • Redis for caching and real-time updates
  • Machine Learning libraries (e.g., TensorFlow.js) for advanced prioritization algorithms (optional for MVP)

Implementation Guidelines

1. Setting Up the Task Prioritization Engine

Create a new module for the Task Prioritization Engine:

// services/taskPrioritizationEngine.js
const Task = require('../models/Task')
const UserPreference = require('../models/UserPreference')
const KnowledgeGraph = require('../services/knowledgeGraphService')
 
class TaskPrioritizationEngine {
  constructor() {
    this.weightFactors = {
      dueDate: 0.3,
      userImportance: 0.25,
      dependencyLevel: 0.2,
      estimatedEffort: 0.15,
      recency: 0.1
    }
  }
 
  async prioritizeTasks(userId) {
    const tasks = await Task.find({ user: userId })
    const userPreferences = await UserPreference.findOne({ user: userId })
    const prioritizedTasks = await this.calculatePriorities(tasks, userPreferences)
    await this.updateTaskPriorities(prioritizedTasks)
    return prioritizedTasks
  }
 
  // ... (other methods)
}
 
module.exports = new TaskPrioritizationEngine()

2. Implementing Priority Calculation

Create methods to calculate priority scores for tasks:

class TaskPrioritizationEngine {
  // ... (previous code)
 
  async calculatePriorities(tasks, userPreferences) {
    const now = new Date()
    const priorityScores = await Promise.all(tasks.map(async task => {
      const score = await this.calculateTaskScore(task, userPreferences, now)
      return { task, score }
    }))
 
    return priorityScores.sort((a, b) => b.score - a.score)
  }
 
  async calculateTaskScore(task, userPreferences, now) {
    const dueDateScore = this.calculateDueDateScore(task.dueDate, now)
    const userImportanceScore = this.calculateUserImportanceScore(task.importance, userPreferences)
    const dependencyScore = await this.calculateDependencyScore(task)
    const effortScore = this.calculateEffortScore(task.estimatedEffort)
    const recencyScore = this.calculateRecencyScore(task.createdAt, now)
 
    return (
      dueDateScore * this.weightFactors.dueDate +
      userImportanceScore * this.weightFactors.userImportance +
      dependencyScore * this.weightFactors.dependencyLevel +
      effortScore * this.weightFactors.estimatedEffort +
      recencyScore * this.weightFactors.recency
    )
  }
 
  calculateDueDateScore(dueDate, now) {
    if (!dueDate) return 0
    const daysUntilDue = (dueDate - now) / (1000 * 60 * 60 * 24)
    return Math.max(0, 1 - daysUntilDue / 14) // Normalize to 0-1, assuming 2 weeks as max
  }
 
  calculateUserImportanceScore(importance, userPreferences) {
    return importance / 5 // Assuming importance is rated 1-5
  }
 
  async calculateDependencyScore(task) {
    const dependentTasks = await KnowledgeGraph.getTaskDependencies(task._id)
    return dependentTasks.length / 10 // Normalize to 0-1, assuming max 10 dependencies
  }
 
  calculateEffortScore(estimatedEffort) {
    return 1 - (estimatedEffort / 480) // Normalize to 0-1, assuming max 8 hours (480 minutes)
  }
 
  calculateRecencyScore(createdAt, now) {
    const daysSinceCreation = (now - createdAt) / (1000 * 60 * 60 * 24)
    return Math.max(0, 1 - daysSinceCreation / 7) // Normalize to 0-1, assuming 1 week as max
  }
}

3. Updating Task Priorities

Implement a method to update task priorities in the database:

class TaskPrioritizationEngine {
  // ... (previous code)
 
  async updateTaskPriorities(prioritizedTasks) {
    const updateOperations = prioritizedTasks.map((item, index) => ({
      updateOne: {
        filter: { _id: item.task._id },
        update: { $set: { priority: index + 1 } }
      }
    }))
 
    await Task.bulkWrite(updateOperations)
  }
}

4. Handling Real-time Updates

Implement a method to handle real-time updates and re-prioritize tasks:

class TaskPrioritizationEngine {
  // ... (previous code)
 
  async handleUpdate(userId, updateType, updateData) {
    switch (updateType) {
      case 'taskCreated':
      case 'taskUpdated':
      case 'taskDeleted':
        await this.prioritizeTasks(userId)
        break
      case 'userPreferencesUpdated':
        await this.prioritizeTasks(userId)
        break
      case 'externalDataUpdated':
        await this.handleExternalDataUpdate(userId, updateData)
        break
    }
  }
 
  async handleExternalDataUpdate(userId, updateData) {
    // Process external data update (e.g., new email, calendar event)
    // This might involve creating new tasks or updating existing ones
    // Then re-prioritize
    await this.prioritizeTasks(userId)
  }
}

5. Implementing Caching

Use Redis to cache prioritized task lists for quicker access:

const redis = require('redis')
const { promisify } = require('util')
 
class TaskPrioritizationEngine {
  constructor() {
    // ... (previous initialization)
    this.redisClient = redis.createClient(process.env.REDIS_URL)
    this.getAsync = promisify(this.redisClient.get).bind(this.redisClient)
    this.setAsync = promisify(this.redisClient.set).bind(this.redisClient)
  }
 
  async getPrioritizedTasks(userId) {
    const cachedTasks = await this.getAsync(`prioritized_tasks:${userId}`)
    if (cachedTasks) {
      return JSON.parse(cachedTasks)
    }
 
    const prioritizedTasks = await this.prioritizeTasks(userId)
    await this.setAsync(`prioritized_tasks:${userId}`, JSON.stringify(prioritizedTasks), 'EX', 300) // Cache for 5 minutes
    return prioritizedTasks
  }
 
  async invalidateCache(userId) {
    await this.redisClient.del(`prioritized_tasks:${userId}`)
  }
 
  // Call invalidateCache after any update that affects prioritization
}

6. Implementing Machine Learning (Optional for MVP)

For advanced prioritization, consider implementing a machine learning model:

const tf = require('@tensorflow/tfjs-node')
 
class TaskPrioritizationEngine {
  // ... (previous code)
 
  async trainModel(userId) {
    const tasks = await Task.find({ user: userId })
    const features = tasks.map(task => [
      this.calculateDueDateScore(task.dueDate, new Date()),
      task.importance / 5,
      task.estimatedEffort / 480,
      // Add more features as needed
    ])
    const labels = tasks.map(task => task.priority / tasks.length) // Normalize priorities
 
    const model = tf.sequential()
    model.add(tf.layers.dense({ units: 10, activation: 'relu', inputShape: [features[0].length] }))
    model.add(tf.layers.dense({ units: 1, activation: 'sigmoid' }))
    model.compile({ optimizer: 'adam', loss: 'meanSquaredError' })
 
    const xs = tf.tensor2d(features)
    const ys = tf.tensor2d(labels, [labels.length, 1])
 
    await model.fit(xs, ys, { epochs: 100 })
 
    // Save the model for future use
    await model.save(`file://./models/prioritization_${userId}`)
  }
 
  async predictPriority(task, userId) {
    const model = await tf.loadLayersModel(`file://./models/prioritization_${userId}/model.json`)
    const features = tf.tensor2d([[
      this.calculateDueDateScore(task.dueDate, new Date()),
      task.importance / 5,
      task.estimatedEffort / 480,
      // Add more features as needed
    ]])
 
    const prediction = model.predict(features)
    return prediction.dataSync()[0]
  }
}

Best Practices

  1. Regularly review and adjust weight factors based on user feedback and system performance.
  2. Implement proper error handling and logging throughout the prioritization process.
  3. Use background jobs for time-consuming prioritization tasks, especially for users with many tasks.
  4. Implement unit tests for each scoring function and the overall prioritization algorithm.
  5. Monitor system performance and optimize database queries for efficiency.
  6. Consider user timezone and working hours when calculating due date scores.
  7. Implement a feedback mechanism for users to adjust task priorities manually, which can be used to improve the algorithm.

Next Steps

  1. Implement more sophisticated scoring algorithms that take into account task context and user behavior patterns.
  2. Develop a system to handle recurring tasks and their prioritization.
  3. Create a dashboard for users to view and understand task priorities.
  4. Implement A/B testing to compare different prioritization algorithms.
  5. Develop an API endpoint for other services to request task reprioritization.
  6. Implement adaptive learning to improve prioritization based on user interactions with the prioritized list.
  7. Create visualizations (e.g., Gantt charts) to help users understand task priorities and dependencies.
Knowledge Graph ModuleDatabase and Caching