Cloud Server Task Time Estimation Using Historical Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for estimating the time required to complete tasks rely on gut feelings and past experiences, which are unreliable and inconsistent, lacking a systematic approach based on factual data.
Innovation Solution
A cloud server system that calculates the time required to complete a task by determining a point in its life cycle, identifying similar completed tasks, calculating similarity scores and time ratios, and using these to estimate the total time needed, incorporating user activity data such as emails, phone calls, and meetings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If task time estimation is based on gut feelings and past experiences, then the estimation process is simple and quick, but the reliability and consistency of the estimation are poor
Solution Approach 1:
The system performs preliminary actions by collecting and storing historical task data, user activity data, and task characteristics in advance. This pre-collected data is then used to automatically generate estimates, eliminating the need for manual gut-feeling-based estimation and improving reliability without requiring complex real-time analysis.
Solution Approach 2:
The system creates copies of historical task data and uses these copies to generate estimates for new tasks. By analyzing patterns from past completed tasks with similar characteristics, the system can predict future task durations accurately without requiring complex theoretical models.
2Measurement precision
If task time estimation uses a systematic approach based on factual data, then the estimation reliability improves, but the system complexity and data processing requirements increase
Solution Approach 1:
The system segments the estimation process into distinct components: collecting historical task data, collecting user activity data, identifying task characteristics, calculating time ratios, and generating estimates. This segmentation allows each component to be processed independently using simple algorithms, reducing overall system complexity while maintaining high precision.
Solution Approach 2:
The system changes parameters by using multiple task characteristics (value, originating region, product mix) and calculating time ratios based on different phases of task completion. These parameter changes enable precise estimation without requiring complex algorithms, as the system simply compares actual vs. expected time across multiple dimensions.
3Measurement precision
If the system collects and analyzes user activity data such as emails, phone calls, and meetings, then the estimation accuracy improves, but the data collection and processing time increases
Solution Approach 1:
The system performs preliminary data collection by continuously gathering user activity data (emails, phone calls, meetings) and storing it in the activity database server in advance. This pre-collected data is readily available when needed for estimation, eliminating the need for time-consuming data collection at the moment of estimation and maintaining high precision without time loss.
Data Source
AI summary
In an embodiment, a cloud server determines a point in time in a current task, and identifies a corresponding point in time in each of a number of completed tasks retrieved from a first database. User activity data from a second database is used to calculate time spent on the current task up to the identified point in time, a ratio between time spent on each completed task after the corresponding point in time and time spent on the completed task before the corresponding point in time. The cloud server also calculates a similarity score between each completed task and the current task based on a number of parameters and using a predetermined algorithm. A weighted arithmetic mean of the time ratios can be calculated using the similarity scores as weights, and can be used in conjunction with the time spent on the current task up to the identified point in time, to calculate time required to complete the current task.