Inferring Location Attributes from Structured Data Entries
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Solution Overview
Problem
Current data integration systems face challenges in integrating data from diverse sources due to differences in formats, value types, and lack of common schemas, leading to inefficient and inaccurate data aggregation, especially with structured datasets like flat files, where locale information such as time zones, currency, and date formats are not transparently available.
Innovation Solution
A method and system that use machine learning and entity recognition techniques to infer location attributes from structured datasets by selecting sample rows, identifying geospatial and temporal information, and applying consolidation rules to derive locale information from file names, column labels, and metadata, enabling semantic multi-dimensional data integration across different data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data integration methods are used to combine data from diverse sources, then data aggregation can be performed, but integration efficiency and accuracy deteriorate due to format differences and lack of common schemas
Solution Approach 1:
The system performs preliminary actions by inferring location attributes and generating location profiles before data integration occurs. This advance preparation includes identifying geospatial columns, determining location information, and establishing common schemas in advance, which resolves format differences and improves both integration efficiency and accuracy when data is actually combined
Solution Approach 2:
The system introduces an intermediary location profile that acts as a mediator between diverse data sources. This profile contains inferred location attributes and serves as a common reference framework that enables accurate integration of data with differing formats and schemas without requiring direct mapping between all source pairs
2Reliability
If locale information is not transparently available in structured datasets, then data storage simplicity is maintained, but data integration accuracy deteriorates due to missing metadata
Solution Approach 1:
The system implements self-service by automatically inferring location attributes directly from the structured dataset without requiring external metadata or manual input. The machine learning model examines the data itself to identify geospatial columns and determine location information, maintaining storage simplicity while improving integration accuracy through automated metadata generation
Solution Approach 2:
The system replaces manual metadata creation and location identification processes with automated machine learning techniques. Instead of requiring human analysts to examine and tag location information, the system uses entity recognition and pattern matching algorithms to automatically infer location attributes, reducing processing complexity while improving accuracy
3Measurement precision
If machine learning techniques are applied to identify geospatial and temporal information, then location attribute inference accuracy is improved, but processing time increases
Solution Approach 1:
The system applies partial action by selecting a sample of rows from each column rather than processing the entire dataset. This sampling approach allows the machine learning model to identify geospatial and temporal patterns with high accuracy while significantly reducing processing time. The inferred location attributes are then applied to the complete dataset, achieving both precision and efficiency
Data Source
AI summary
A system and method are provided for inferring location attributes from data entries. The method comprises for data entries in a structured data set format, a computer system selecting a sample of rows. The computer system then identifies columns containing geospatial and temporal information based on the column headings. The computer system next identifies location information within the structured data set. The computer system determines implied location information based on the identified location information. The computer system derives location values based on the identified and implied location information using consolidation rules, resulting in a final set of location attributes for the data entries. The computer system then associates the final set of location attributes with the data entries.