Real-Time Data Prediction Contest System for DSaaS
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Solution Overview
Problem
Existing data science prediction contests face challenges such as delays due to limited internal staff, cheating issues, and the ephemeral relevance of historical data models, which hinder businesses in obtaining timely and accurate predictive analytics.
Innovation Solution
A real-time data prediction contest system that allows participants to build and deploy models as web services, enabling simultaneous crowd-sourcing of model selection and data gathering, with a central server transmitting live questions and receiving real-time responses for immediate scoring and compensation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If historical data prediction contests are used, then statistical research can be conducted with external community assistance, but the results suffer from delays, cheating issues, and ephemeral relevance
Solution Approach 1:
The system transitions from static historical data contests to dynamic real-time prediction contests where models continuously process live data streams. Participants submit models that respond to evolving questions, ensuring results remain current and relevant while maintaining community engagement.
Solution Approach 2:
The system pre-defines question formats and data structures before contests begin, allowing participants to prepare models in advance. This preliminary structuring enables faster processing of real-time data while maintaining contest integrity and reducing delays.
2Measurement precision
If historical data is used for competitions, then participants can develop predictive models, but cheating becomes inevitable and burden remains with the data consumer
Solution Approach 1:
Instead of providing historical data and asking participants to predict past outcomes (vulnerable to cheating), the system inverts the approach by providing real-time data streams and asking participants to predict future values. This makes cheating impossible as the answers must be generated based on live, evolving data that cannot be pre-computed.
Solution Approach 2:
The system introduces a centralized platform that mediates between data sources and participants. This intermediary validates all model submissions, ensures fair access to data, and automatically scores predictions, eliminating the need for data consumers to manually verify results and reducing opportunities for cheating.
3Adaptability or versatility
If internal staff are used to generate predictive analytics, then company data can be processed, but significant delays occur due to project backlogs
Solution Approach 1:
The system creates a universal platform that handles multiple prediction tasks simultaneously through a standardized question format. Internal staff can focus on defining business questions while the platform manages data processing, model evaluation, and result aggregation, significantly increasing analytics delivery speed without sacrificing adaptability.
Solution Approach 2:
The system enables self-service prediction generation where the automated platform processes data and generates predictions without requiring extensive internal staff involvement. Participants' models automatically process data streams and generate results, reducing the burden on internal teams while maintaining the ability to handle diverse business questions.
Data Source
AI summary
The invention relates to a computer-implemented system and method for providing data science as a service (DSaaS) using a real time data prediction contest. Participants in the real time data prediction contest are permitted to execute and submit algorithms, utilize third party data sources, and utilize sub-contests to generate data predictions for the data prediction contest. The participants in the data prediction contest may be humans or software robots. A category of sponsor confidential information related to the data prediction is defined and maintained as confidential by the sponsor, while various methods are implemented to obtain relevant algorithms and data for the data prediction. The sponsor receives data predictions from the participants on a real time or near real time basis, calculates a score for the data predictions, and compensates participants according to their score.