Merging Location Data Sets via Machine Learning
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Solution Overview
Problem
Conventional approaches to merging location-based data sets are time-consuming and require manual intervention due to inconsistencies in data sources and standards, making it difficult to combine disparate data sets effectively.
Innovation Solution
A computer system transforms data sets into a standardized schema, uses a machine learning model to join and filter data sets based on geographical distances and match scores, enabling automated merging of location-based data sets across various types, such as company, place of interest, user position, and weather data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual ad hoc interventions are used to merge data sets, then data merging can be performed with flexibility, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The system performs automated data merging using machine learning models that self-learn from data patterns and automatically match records across different data sets without requiring manual intervention. The ML models autonomously handle schema alignment, record matching, and data fusion tasks.
Solution Approach 2:
Manual mechanical operations (human operators performing ad hoc merging) are replaced with automated computational systems using machine learning algorithms. The system substitutes human cognitive and manual labor with ML-based automated processes for data matching and integration.
2Adaptability or versatility
If conventional approaches are used to merge data sets from disparate sources, then data can be combined, but inconsistencies in standards and schemas make merging difficult
Solution Approach 1:
The system transforms data from different sources by changing their schema parameters to a unified standardized format. Machine learning models learn the mapping between various data schemas and automatically transform attributes, data types, and structures to ensure compatibility across disparate data sources.
Solution Approach 2:
A standardized schema acts as an intermediary layer between disparate data sources. The system introduces this intermediate representation that mediates the integration process, allowing data from different sources with different standards to be merged through the common standardized interface.
3Productivity
If automated processing is implemented to reduce manual effort, then processing speed increases, but the complexity of the system increases
Solution Approach 1:
The system performs preliminary actions by pre-processing data to extract features and pre-align schemas before the actual merging operation. Machine learning models are pre-trained on sample data to learn matching patterns, enabling faster automated processing during production without requiring complex real-time decision-making.
Data Source
AI summary
A computer system merges location-based data sets. Each of a plurality of data sets are transformed into a standardized schema, including at least two data sets including information indicating a geographic location. The schemas of the plurality of data sets are combined by data set type to produce a resulting data set for each data set type. The schemas of a first and second data sets are joined to produce a merged data set using a machine learning model to identify corresponding rows of the schemas. The schema of the merged data set is joined with the schemas of the resulting data sets for the data set types to produce a new data set. A resulting merged data set in the standardized schema is produced. Embodiments of the present invention further include a method and program product for merging location-based data sets in substantially the same manner described above.