Data processing: database and file management or data structures – Database design – Data structure types
Reexamination Certificate
1999-07-09
2002-05-21
Coby, Frantz (Department: 2171)
Data processing: database and file management or data structures
Database design
Data structure types
C707S793000, C707S793000, C707S793000, C707S793000, C707S793000
Reexamination Certificate
active
06393426
ABSTRACT:
BACKGROUND OF INVENTION
1. Field of Invention
The present invention relates to a method of modeling, storing and transferring information in a neutral form in a computer. The present invention reduces the complexity and effort presently involved in the modeling and storage of information while creating a storage format which enables complete parallel processing of data. Furthermore it enables not only direct integration of different models and their stored data without remodeling or reloading but dynamic evolution of those models and their data after some earlier implementation.
2. Description of Prior Art
Since their introduction about 40 years ago, computers have increasingly been used as mechanisms for the storage of information.
FIG. 1
summarizes the prior art for the computer storage, retrieval and transfer of information. Despite exponentially rising demand and all of the effort that has been expended during the past several decades to develop and apply methods for data modeling storage and transfer, the bulk of organized information is still handled outside of databases. This has been due to the complexity, inflexibility and cost of modeling, storage and transfer of organized data that is inherent in prior art techniques.
Whether they employed data models or not; prior art techniques have, so far as is known been unable to achieve any practical success in data transfer without a user being intimately familiar with both the structure and the definitions of the data involved, and also with the particular application programs and interface languages employed for its storage. The result has been an inherent complexity in data modeling storage and transfer, putting data organization and access beyond the reach of many potential information workers who generate and seek to exchange, interpret and use their data.
Users have been forced to apply the skills of information technology specialists for data organization and information management where they can afford or justify the costs. Otherwise, users have been forced to limit arbitrarily the scale of their work where they cannot afford such specialists. At the current complexity and escalating costs of prior art methods, only a small portion of the generated data that exists is processed into database form.
Un-modeled Data Files
Methods available in the prior art for the storage and transfer of un-modeled collections of information have required a great deal of a-priori knowledge on the part of the user, both of the structure and definitions of the data and the application programs involved. Spreadsheets have been one common method employed for such storage. Its usefulness is restricted to collections of data of limited complexity and size. Storage and transfer has typically been required to be a complete rather than a partial spreadsheet file. Un-modeled information has been typically stored directly in an application program format, e.g., an Excel® or Lotus® spreadsheet file.
Although the information can be downloaded to ASCII or other primitive standard formats for transfer purposes, it is done with significant additional loss of any already limited data definition. Larger and more complex collections of information typically have been stored in the format of the specialized applications programs within which they are generated, e.g., accounting programs, simulation modeling programs, or engineering calculation programs. Here too, retrieval and transfer typically have been limited to complete rather than partial files.
Often, such information has been merely organized into streams of ASCII or comparable format. This has imposed severe limitations on the storage of structure and content of the data as opposed to merely the data values. Transfer of any and all such organized information, including spreadsheets thus has relied heavily upon a user's knowledge in advance of the structure and content of the information. Typically, a user has operated the same application interface program that generated the data to reduce the degree of difficulty involved in the many interpretive aspects of its transfer. The result is a severely restricted exchange of information both within and between data user organizations.
Modeled Data—Database Files
As shown in
FIG. 1
, data modeling has represented an increase in the degree of organization from a collection of un-modeled organized information. The clear advantage has been that portions of a collection of information can be transferred rather than only the entire file. However, this advantage has been achieved with a major increase in the complexity and cost of the data management.
Data modeling for a collection of information involves a combination of (a) organization of the data values; (b) some system or technique for definition of each data value; and (c) a system for structuring the relationships among various data values for storage in a manner which was capable of supporting accurate retrieval and transfer of targeted subsets of the information. Many modeling methods have been proposed, and of those only a limited number have proven useful enough to receive widespread use. Examples of proposed methods include: entity relationship (ER); Nijssen's information analysis method (NIAM); IDEFIX, a graphical language; EXPRESS, a product information model; and object oriented model (OOM). Discussions of these methods are available in many publications. Chapter 2, pp. 12-30 of Schenck “Information Modeling: The EXPRESS Way” is representative. Only ER and OOM are, so far as is known, presently in widespread use.
All of these prior art modeling methods have imposed a-priori relationships and some form of hierarchy both for the organization and the storage of data. The particulars of such a hierarchy have varied with the data modeling method and with the individual modeler. In each case, however, the hierarchy selected has been incorporated into a structure of the information for storage and has been different and specific to each major modeling-based database and its associated software products.
Thus the storage of information in relational and object oriented databases has been both program dependent and a function of the particular form of relationships and hierarchy chosen during modeling. As a result, the retrieval and transfer of database information have required a significant knowledge of both the hierarchy imposed on the data and the particular data storage structure that has been employed by the database product employed. These have been major limitations of the current technology which have become particularly evident to users involved in the retrieval and transfer of information.
Efforts have been made to reduce the severity of these problems through the development of a standard database software program interface language—standard query language (SQL). This effort has met with some success, in part because of the rapidly changing features of individual database software programs and the data itself.
Both the known data modeling techniques and the standard query language interface scheme (SQL) also have involved a complex set of rules and conventions whose understanding and practice are well beyond the reach of typical data users. See for example Date, “A Guide to the SQL Standard” Appendix A, pp 137-152. As a result, specialists in database management have been required for data modeling, which has given rise to high total database management costs and complicated communication problems between data users and data modeling specialists. The combination of these limitations has resulted in limited application of data modeling for the storage and transfer of organized data.
Each of the prior art data modeling and storage techniques has involved extensive integrated organization of the data and its structure, which has severely limited the capacity for parallel processing in both data storage and in data retrieval and transfer. Major relational and object oriented database vendors have developed complex optimization routines to guide the selection of pathways through thes
Massey Michael J.
Odom Paul S.
Coby Frantz
Miles Coe F.
Pliant Technologies, Inc.
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