Opaque Data Parsing via Schema Generation From Data Dictionaries
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Solution Overview
Problem
Existing Decision Support Systems (DSS) face challenges in effectively pre-processing opaque data records, which lack self-describing formats, leading to inefficiencies and errors due to inaccurate data dictionaries and the need for manual validation.
Innovation Solution
A system and method for generating a schema from a data dictionary to parse opaque data records, using automated tools like Tabula to extract tables from PDF documents and validate the data dictionary, followed by generating a parser schema to accurately identify and extract data fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data dictionary maintenance is used for opaque data parsing, then data parsing can be performed, but accuracy deteriorates due to errors and inefficiencies
Solution Approach 1:
The system enables self-service by automatically generating and maintaining data dictionaries through schema inference from actual data records. The parser autonomously updates the data dictionary without manual intervention, allowing the system to self-correct and adapt to changing data formats while maintaining high accuracy.
Solution Approach 2:
The system performs preliminary action by pre-processing data samples to automatically generate the data dictionary before actual parsing operations. This upfront automation of dictionary creation eliminates subsequent manual maintenance needs and establishes accurate parsing rules in advance.
2Quantity of substance
If opaque data records are parsed without self-describing formats, then data transmission density is improved, but data parsing reliability deteriorates due to lack of format information
Solution Approach 1:
The system introduces an intermediary data dictionary that bridges the gap between opaque compact data and meaningful information. This intermediate structure stores inferred schemas and field definitions, enabling reliable parsing of dense opaque records without requiring self-describing formats in the transmitted data itself.
Solution Approach 2:
The system transitions from one-dimensional opaque byte streams to a two-dimensional structure by introducing the data dictionary as a separate dimensional layer. This additional dimension contains metadata about data structure, allowing the system to interpret compact opaque records reliably without adding redundancy to the transmitted data.
3Productivity
If data dictionaries are manually maintained for parsing, then parsing can be performed, but efficiency deteriorates due to errors and maintenance overhead
Solution Approach 1:
The system implements self-service by automatically generating data dictionaries from actual data records through schema inference. This eliminates manual dictionary maintenance entirely, allowing the system to self-update parsing rules based on observed data patterns, thereby improving efficiency while reducing operational complexity.
Solution Approach 2:
The system employs feedback mechanisms by continuously analyzing parsed data records and using the results to refine and update the data dictionary. This closed-loop approach automatically corrects errors and adapts to format changes, improving parsing efficiency without requiring manual intervention or increasing complexity.
Data Source
AI summary
Systems and methods for a multi-tenant parser generation platform. A human-readable document of a data provider system is accessed. The document includes a data dictionary table for opaque data of the data provider system that has a first type. Data dictionary information of the data dictionary table is extracted, and a schema file is generated from the extracted information. The schema file defines a parsing process for parsing an opaque data record of the first. The first schema file specifies each field of the opaque data record of the first type. Parsing instructions are generated based on the schema file. The parsing instructions are for parsing an opaque data record of the first type provided by the data provider system into a set of data fields. The parsing instructions are provided to an entity system external to the platform. The first entity system is associated with a first platform account.