System and Method for Planning and Generating Queries for Multi-Dimensional Analysis using Domain Models and Data Federation

Abstract

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.

Description

FIELD OF THE INVENTION [0001] The present invention relates to data integration and data analysis using computing equipment, software as well as hardware, and, more particularly, to 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. BACKGROUND OF THE INVENTION [0002] In recent years, industry trends toward mergers and acquisitions has forced most OEMs to re-think their processes to account for the multitude of data sources used by the various corporate divisions within the enterprise for storing and manipulating product data, including the product bill of materials, parts catalog, diagnostic procedures and warranty claims as a partial list of the kinds of product data that exists. [0003] Government regulations, rising production cost, shorter time-to-market are yet other reasons why co...

AI Technical Summary

Benefits of technology

This approach enhances data integration and analysis capabilities, reducing costs and improving speed and accuracy in predicting and addressing product design weaknesses, while maintaining data integrity and adaptability to changing business needs.

Problems solved by technology

The complexity of the IT environment in the industrial manufacturing sector has grown exponentially over the years creating problems.

The increasingly complex nature of the product itself as measured by the number of components that goes into the making of a product and the number of configurations for each product.

The lack of visibility across the product life cycle due to the disparity of data management systems used in the different stages of the design, development, manufacturing process and services after sales.

A large number of heterogeneous systems that are in use throughout the extended enterprise create an artificial barrier for information sharing.

The product design is often organized in silos around specific product assemblies (in the case of automobiles, e.g., wing design, engine design, body structure design, interior design, etc) making it difficult to integrate data across multiple divisions.

While the project team is often composed of a multi-disciplinary group of engineers, the IT tools remain too fragmented and specifically tailored to the organization or division that uses them the most.

In this environment, traditional approaches for data integration using data warehousing does not always scale well.

Data warehouses are usually designed to meet the requirements of one specific application and are not easily extensible without tearing down and rebuilding the table schema.

They provide a fixed view on the data and are not easily adapted to changing business needs such as when new suppliers are integrated into the value chain.

Furthermore, product data tends to be deeply hierarchical in nature and has associated semantics and access control procedures that are encapsulated within the data management system that hosts the information and cannot be easily exposed to external applications.

This approach tends to be slow and costly as multiple iterations may be required to provide the information that is needed.

The approach leads to data redundancy where multiple replicas of the same work product could exist within the extended enterprise and requires additional complexity for managing the life-cycle of the exchanged information.

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