OLAP System for Business Location Prediction Using Composite Indicators
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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 due to the complexity and heterogeneity of data, making it time-consuming and challenging to analyze and present useful information for optimal decision-making.
Innovation Solution
A system and method that combines heterogeneous data sources, including structured, unstructured, and spatial data, using composite indicators and dimensionality reduction techniques to automatically identify and rank potential business locations, presented on a spatial map with a heat map for easy visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human insight and experience are used to combine heterogeneous factors, then location selection can be performed, but the process is time-consuming and inconsistent
Solution Approach 1:
The patent replaces manual human analysis with automated computer systems that process heterogeneous data. The system automatically collects, cleans, and analyzes data from multiple sources (census data, traffic patterns, competitor locations) using computational algorithms, eliminating the time-consuming manual process while maintaining analytical rigor.
Solution Approach 2:
The system performs self-service by automatically executing the complete location analysis pipeline without requiring continuous human intervention. The automated workflow includes data collection, cleaning, transformation, analysis, and visualization, allowing the system to process location data independently and consistently.
2Reliability
If human experts analyze heterogeneous data, then location predictions can be made, but human bias introduces uncertainty
Solution Approach 1:
The patent eliminates human bias by replacing subjective expert judgment with objective computational algorithms. The system uses standardized data processing pipelines and statistical models that apply consistent rules across all location analyses, ensuring reproducible and unbiased results that do not vary based on individual expert opinions.
Solution Approach 2:
The system transforms qualitative human judgment into quantitative parameters through automated data processing. By converting location factors into measurable data points (traffic flow rates, population densities, rental costs) and analyzing them through statistical models, the system replaces subjective bias with objective numerical analysis.
3Measurement precision
If detailed data analysis is performed, then more accurate predictions can be made, but the complexity of data processing increases
Solution Approach 1:
The patent segments the complex data processing task into distinct modular components: data collection from multiple sources, data cleaning and validation, data transformation to standardized formats, analysis through statistical models, and visualization. This segmentation allows each component to be optimized independently and simplifies the overall system architecture.
Solution Approach 2:
The system introduces intermediary processing layers that simplify the relationship between raw heterogeneous data and final predictions. The data cleaning and transformation modules act as intermediaries, converting complex raw data into standardized, analysis-ready formats, thereby reducing the complexity of subsequent analysis while maintaining precision.
4Adaptability or versatility
If heterogeneous data from multiple sources is collected, then more comprehensive analysis is possible, but data integration becomes arduous
Solution Approach 1:
The patent implements a universal data processing framework that handles multiple data types and sources through a single integrated system. The standardized data cleaning and transformation modules can process various formats (tabular data, spatial data, text) and sources (census bureaus, traffic agencies, business registries) using the same processing pipeline, eliminating the need for separate integration solutions for each data type.
Data Source
AI summary
A method and system that utilizes OLAP and supporting data structures 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 by employing machine learning. 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.