MOM Data Warehouse Context Extension for Uniform Source Analysis
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Solution Overview
Problem
MOM data warehouses face limitations in analysis capabilities due to differing data models among sources, where not all performance parameters are linked to the same set of context indicators, restricting users' ability to apply consistent contextualization across all measures.
Innovation Solution
A data extensor module processes data from sources to add missing context identifiers, generating augmented data compatible with the basic data model, allowing for extended analysis without redefining context links, and enabling integration of external and legacy systems through configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data from multiple MOM data sources with different data models are collected into a MOM data warehouse, then the quantity of data sources is increased, but the complexity of data model integration increases
Solution Approach 1:
The patent introduces a data extensor module as an intermediary component between the data source and the MOM data warehouse. This module automatically adds missing context identifiers to source performance parameters based on the basic data model, serving as a mediator that handles data model differences without requiring manual intervention or complex integration logic in the main system
Solution Approach 2:
The patent segments the data integration process into distinct functional components: the data source, the data extensor module, and the MOM data warehouse. The data extensor module specifically handles the transformation task of adding context identifiers, separating the complexity of data model adaptation from the core data warehouse functionality
2Reliability
If a basic data model with comprehensive context indicators is enforced, then the consistency of analysis is improved, but the difficulty of integrating legacy data sources increases
Solution Approach 1:
The data extensor module implements self-service by automatically identifying and adding missing context identifiers to source performance parameters. The module autonomously queries the basic data model to determine which context identifiers are missing and adds them without requiring manual configuration or intervention, thereby maintaining analysis consistency while simplifying legacy system integration
Solution Approach 2:
The patent applies preliminary action by pre-defining the basic data model with all necessary context identifiers before data integration occurs. The data extensor module uses this pre-established model to proactively add missing context identifiers to incoming data, ensuring consistency is built-in from the start rather than requiring post-processing adjustments
3Measurement precision
If manual configuration is required to add context identifiers, then the precision of data contextualization is improved, but the loss of time for data preparation increases
Solution Approach 1:
The data extensor module eliminates manual configuration by implementing self-service functionality that automatically adds missing context identifiers to source performance parameters. The module autonomously queries the basic data model, identifies gaps in contextualization, and performs the addition without human intervention, thereby maintaining precision while eliminating time loss
Solution Approach 2:
The system implements feedback by continuously querying the basic data model to determine which context identifiers are missing from source performance parameters. This feedback mechanism guides the automatic addition process, ensuring that only the necessary context identifiers are added with high precision while minimizing unnecessary processing time
Data Source
AI summary
A process and a system collect data from a data-source into a manufacturing operation management (MOM) data warehouse. The data in the MOM data-warehouse are exposed according to a basic data model in which a performance parameter is linked to a basic set of context identifiers for MOM analysis purposes. The data in the data source are exposed according to a source data model in which a source performance parameter is linked to a source set of context identifiers. A data extensor module is provided for processing the data received from the data source to add, upon need, a context identifier linked to the source performance parameter. Whereby the added context identifier is present in the basic set but it is not present in the source set. The data extensor module processing data is received from the data source to obtain augmented data stored in the MOM data warehouse.