Business Location Prediction via Heterogeneous Data Integration
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Solution Overview
Problem
Current methods for selecting business locations rely heavily on human insight and experience, leading to inconsistencies, biases, and inefficiencies in analyzing heterogeneous data, which can result in suboptimal decision-making due to the complexity and time-consuming nature of data analysis.
Innovation Solution
A system that automatically identifies and combines heterogeneous data sources, using composite indicators and dimensionality reduction techniques to provide a rank-ordered list of optimal business locations on a spatial map, facilitating the integration of structured, unstructured, semi-structured, and spatial data for prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human insight and experience are used to analyze heterogeneous data for business location selection, then the process can be performed with current tools, but the analysis is time-consuming and produces biased results
Solution Approach 1:
The patent replaces manual human analysis of heterogeneous data with an automated machine learning system. The system automatically ingests, cleans, and analyzes diverse data sources (structured tables, unstructured text, spatial data) using computational algorithms, eliminating the time-consuming manual process while producing consistent, bias-free results.
Solution Approach 2:
The system performs self-service by automatically identifying relevant data sources, extracting meaningful features, and generating location predictions without requiring human intervention in the analysis process. The machine learning model autonomously processes heterogeneous data and produces ranked location recommendations.
2Loss of information
If heterogeneous data from multiple sources is analyzed manually, then comprehensive information can be gathered, but the complexity of data processing increases
Solution Approach 1:
The patent segments the data processing task into distinct modules: data ingestion from multiple sources, data cleaning and normalization, feature extraction, model training, and prediction generation. Each module handles a specific aspect of heterogeneous data processing, making the overall complex system manageable and maintainable.
Solution Approach 2:
The system introduces intermediate processing layers that transform heterogeneous data into a unified representation. Data from diverse sources (tables, text, spatial data) is converted into standardized features that can be processed by the machine learning model, simplifying the complexity of handling multiple data formats.
3Reliability
If manual analysis of location data is performed, then human judgment can be applied, but consistency and objectivity are compromised
Solution Approach 1:
The patent replaces subjective human judgment with objective machine learning algorithms that consistently apply the same criteria to all location candidates. The model processes data through fixed computational steps, ensuring reproducible and unbiased results that maintain reliability across different analyses.
4Measurement precision
If comprehensive data from multiple heterogeneous sources is integrated, then more accurate predictions can be made, but the time required for analysis increases
Solution Approach 1:
The system performs continuous data integration and processing operations that run efficiently in the background. The machine learning model continuously learns from incoming data and updates its predictions, allowing comprehensive data integration without significant time delays that would manual analysis would require.
Data Source
AI summary
A method and system for making predictions about business locations. The method includes providing a spatial map and analyzing heterogeneous data having a spatial component to find utilizable data. Relationships are automatically extracted from the utilizable data. The step of automatically extracting relationships includes generating a composite indicator, which correlates spatial data with unstructured data. The extracted relationships are presented on a spatial map to make a prediction about at least one business location. Preferably, the predictions are presented as a rank-ordered list on the spatial map and a heat map overlays the spatial map to indicate predictions about particular regions.