Crowdsourcing Task Assignment Using Tier Data Structure
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing task multi-assignment methods in crowdsourcing-based projects face limitations in scalability and efficiency due to the need for extensive scanning processes and load issues, particularly when assigning tasks to multiple workers, which can lead to resource wastage and decreased performance.
Innovation Solution
A tier data structure-based method for task multi-assignment, utilizing a combination of queues and stacks in assignment, rejection, and reassignment tier storages, allows for efficient task distribution by managing key-value pairs and enabling quick scanning and assignment without load, thereby supporting flexible expansion and concurrency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the same task is assigned to multiple workers simultaneously, then task completion reliability is improved, but the complexity of managing task data increases
Solution Approach 1:
The patent segments the monolithic task data structure into multiple independent data items, each representing a single worker's assignment status. This segmentation allows each data item to be managed independently through the stack LIFO structure, reducing the complexity of tracking multiple assignments while maintaining reliability through systematic management of each individual assignment.
Solution Approach 2:
The patent introduces a temporal dimension to task assignment management by using a stack structure where each data item is pushed and popped in sequence. This transforms the complex multi-dimensional problem of tracking multiple simultaneous assignments into a simpler one-dimensional LIFO sequence, making the system more manageable while preserving all assignment information.
2Speed
If local memory dependent on web application server is used for task assignment, then data access speed is improved, but service scalability deteriorates
Solution Approach 1:
The patent introduces a session-based intermediary layer between the web application server and the task data. The stack-based task session stores task data locally in the user's browser, acting as an intermediary that provides both fast local access and automatic synchronization with the server, thereby enabling scalability without sacrificing access speed.
Solution Approach 2:
The system performs preliminary actions by pre-loading task data into the browser's session storage before the user actually needs it. This allows the user to work with task data at high speed from local memory while the system handles scalability through asynchronous server communication, resolving the contradiction between speed and scalability.
3Productivity
If extensive scanning process is used for task assignment, then task distribution thoroughness is improved, but processing time increases
Solution Approach 1:
The patent inverts the traditional scanning approach by using a LIFO stack structure where the most recently added task data is accessed first. This eliminates the need for extensive scanning through the entire data set, as the system automatically provides the next task to be assigned without searching, thereby maintaining thorough task distribution while dramatically reducing processing time.
Solution Approach 2:
The stack-based approach allows the system to skip the time-consuming scanning process entirely by directly accessing the top element of the stack. This enables the system to rush through task assignment operations efficiently while still ensuring all tasks are distributed appropriately, resolving the contradiction between thoroughness and speed.
Data Source
AI summary
A task multi-assignment method using a tier data structure of a crowdsourcing-based project, the method being a task assignment method using a tier data structure of a crowdsourcing-based project of a multi-assignment method of duplicately assigning each task to a plurality of n different workers, includes: configuring an assignment tier storage including a queue into which task data corresponding to a task that is not assigned to the worker is enqueued, and one or more jth stacks into which task data corresponding to tasks that are each assigned j times (wherein j is a natural number less than n) are pushed; and assigning a task of the project to a worker (hereinafter, referred to as a target worker) who requests task assignment and requesting the target worker to perform the task, by using the assignment tier storage.