Data Federation for Multi-Dimensional Analysis
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Solution Overview
Problem
The complexity of managing product data across multiple divisions in industrial manufacturing environments, where traditional data warehousing approaches fail to scale and provide real-time integration and analysis due to disparate data management systems and hierarchical product data structures, leading to inefficiencies and increased costs.
Innovation Solution
A system and method for integrating data from various sources using semantic models and data federation, allowing for human-friendly queries to be automatically translated into physical queries without building a traditional data warehouse, enabling multi-dimensional analysis across divisions and dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data warehousing approaches are used to integrate product data across divisions, then data integration is achieved, but the system complexity and costs increase exponentially and the system fails to scale
Solution Approach 1:
The patent segments the monolithic data warehousing approach into multiple virtual data marts, each serving specific divisions or functions. These virtual data marts are logically separated but coordinated through a federated query system, allowing data integration without consolidating all data into a single complex warehouse structure.
Solution Approach 2:
The patent introduces a data federation layer as an intermediary between disparate data sources and end users. This federation layer virtualizes data access, translating user queries into coordinated queries across multiple underlying data sources without requiring physical data consolidation, thus reducing system complexity while maintaining integration capabilities.
2Loss of information
If data is replicated across multiple partners to enable data exchange, then data sharing is achieved, but data redundancy increases and additional complexity is required for managing the life-cycle of exchanged information
Solution Approach 1:
The patent uses virtual copies of data through data federation rather than physical replication. The federation layer creates virtual representations of data from remote sources, allowing partners to access and share data without duplicating the actual data files. This eliminates the need to manage multiple physical replicas and their life cycles.
Solution Approach 2:
The patent implements a universal data access interface that works across multiple partners and data sources. This unified federation layer provides consistent data sharing capabilities to all partners without requiring partner-specific replication strategies, simplifying the management of data exchange across the extended enterprise.
3Ease of manufacture
If a data warehouse is designed to meet the requirements of one specific application, then that application's needs are met, but the data warehouse is not easily extensible without tearing down and rebuilding the table schema
Solution Approach 1:
The patent implements a dynamic data federation architecture where the virtual data mart structure and query routing can be modified without affecting the underlying data sources. New data sources, queries, and analysis requirements can be added dynamically by configuring the federation layer, eliminating the need to rebuild schemas when requirements change.
Solution Approach 2:
The patent adds a virtualization dimension between the physical data sources and the application layer. This federation layer provides a new dimension of flexibility, allowing applications to access data in different formats and structures without changing the underlying data warehouse schemas, thus enabling extensibility while preserving application-specific optimizations.
4Loss of information
If product data exchange through data replication is used, then data sharing between partners is enabled, but the process is slow and costly as multiple iterations may be required
Solution Approach 1:
The patent replaces the mechanical data replication process with a virtual data access system. Instead of physically copying and transferring data files between partners through multiple iterative exchanges, the federation layer translates queries into direct accesses to remote data sources, retrieving data in a single pass and eliminating the need for repeated data exchange iterations.
Data Source
AI summary
Data integration and data analysis using computing equipment, software as well as hardware, includes a system and method for integrating data from various data sources, structured and unstructured, without physically creating a data warehouse and automatically generating queries for analysis of the integrated data from a multitude of different views.