Automated Predictive Estimation in Project Management
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Solution Overview
Problem
Current project management systems face challenges in predictive estimation due to the need for manual integration of data mining software with project management environments, requiring domain-specific knowledge and human intervention, which is cumbersome and inefficient, especially with complex multi-dimensional data sets.
Innovation Solution
A system that generates statistical models using machine learning techniques, selects relevant data subsets, and applies these models to generate prediction estimates and highlight outliers, integrated within the project management environment to automate data analysis and reduce manual effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual integration of data mining software with project management environment is performed, then predictive estimation capability is achieved, but system complexity and human intervention requirements increase
Solution Approach 1:
The patent combines data mining functionality directly within the project management environment by integrating a data mining server with the project management server. This merging eliminates the need for separate manual integration steps and reduces human intervention while maintaining predictive estimation capabilities through automated model generation and application.
Solution Approach 2:
The project management server is enhanced to serve multiple functions: it manages project data, generates statistical models, selects appropriate models for prediction, and applies models to generate estimates. This multi-functionality reduces the need for separate dedicated data mining software and simplifies the overall system architecture.
2Measurement precision
If domain-specific knowledge and human specialists are deployed for model selection, then prediction accuracy improves, but time consumption and operational complexity increase
Solution Approach 1:
The system enables automated model selection through machine learning techniques where the project management server automatically evaluates multiple statistical models and selects the most appropriate one based on the data characteristics. This self-service approach eliminates the need for human specialists to manually select models while maintaining high prediction accuracy through algorithmic optimization.
Solution Approach 2:
The system automatically adjusts model parameters and selects models based on data characteristics without human intervention. The project management server evaluates multiple statistical models with different parameters and automatically configures the optimal model for each prediction task, replacing manual domain expertise with automated parameter optimization.
3Reliability
If data cleaning and characteristic selection are performed manually, then data quality improves, but productivity and processing speed decrease
Solution Approach 1:
The system performs automated data preprocessing, cleaning, and characteristic selection as preliminary actions before model generation. The project management server automatically prepares the data by cleaning it and selecting relevant characteristics without requiring manual intervention, thereby maintaining data quality while significantly improving processing speed and productivity.
4Ease of manufacture
If off-the-shelf data mining solutions are used, then development cost is reduced, but adaptability to specific enterprise data structures decreases
Solution Approach 1:
The system dynamically adapts to different enterprise data structures by automatically evaluating multiple statistical models and selecting the most appropriate one for each specific data set. This dynamic approach allows the use of general off-the-shelf data mining algorithms while maintaining high adaptability to various enterprise data structures through automated model selection and configuration.
Data Source
AI summary
Systems and methods for predictive estimation within an enterprise environment are provided. An enterprise environment is maintained with a plurality of clients and associated client data. The system generates one or more statistical models by analyzing the client data in the enterprise environment using one or more statistical algorithms, then stores the statistical models in a model database. The system receives a prediction estimate request from one of the plurality of clients with respect to the associated client data for the client. The system then selects, using a clustering algorithm, a subset of the associated client data, as well as best statistical model from the one or more statistical models based at least on the subset of the associated client data. The system then applies the statistical model to the subset of client data to generate prediction estimates and provides a visual arrangement of them to the client.