PLCS Database Schema Mapping for Data Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Managing information related to complex assets, such as naval vessels, across multiple participants with different schemas, terminology, and naming conventions leads to errors, inefficiencies, and increased costs due to incomplete or inaccurate data, as existing systems lack effective data exchange and retrieval capabilities.
Innovation Solution
A database system that transforms and consolidates data from disparate sources into a common format using a PLCS schema, allowing multiple users to interact and update a shared database without needing to know the schema, ensuring accurate, extensive, and error-free data storage and retrieval, compliant with standardized terminology and naming conventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored in multiple separate databases with different schemas, terminology and naming conventions, then each participant can use their own local schema, but data accuracy and consistency deteriorate due to errors and omissions
Solution Approach 1:
The patent introduces a common database as an intermediary between multiple participants with different local schemas. This common database receives data from various sources, standardizes it according to a unified schema, and distributes it back to participants. The intermediary resolves the contradiction by allowing participants to maintain their local schemas while ensuring data accuracy through centralized standardization and validation processes.
2Loss of information
If data is manually re-generated or re-formatted from separate databases, then data can be consolidated, but time consumption and error rates increase
Solution Approach 1:
The system performs preliminary standardization and validation of data when it is first entered into the common database, rather than requiring manual processing later. Data is transformed into the unified schema format in advance, and consistency checks are performed before data is distributed to participants. This preliminary action eliminates the need for time-consuming manual re-formatting and reduces errors by establishing data quality standards upfront.
3Reliability
If a common database schema is enforced on all participants, then data consistency improves, but ease of operation deteriorates as users must learn and adhere to the common schema
Solution Approach 1:
The system segments the database interaction into two distinct layers: a common database layer that enforces unified schemas for data consistency, and local participant layers that can use their own familiar schemas. The translation and transformation functions act as intermediaries between these layers, allowing participants to interact with their local schemas while the common database maintains data consistency through automated schema mapping and transformation processes.
Data Source
AI summary
A method for providing access to a database for product life cycle support (PLCS database), the method comprising receiving from a client apparatus an input data item including heading data identifying the input data item; correlating said heading data with a data field used in the PLCS database amongst all data fields used in the PLCS database thereby identifying a correlated entry in the PLCS database to be associated with the input data item; searching the PLCS database using said correlated entry; retrieving one or more data elements from the PLCS database according to said search; outputting to the client apparatus a return data item which includes said heading data identifying the return data item and which includes said retrieved data elements(s).