Method for modeling, storing, and transferring data in neutral form

Abstract

The present invention simplifies the data modeling process and enables its full dynamic versioning by employing a non-hierarchical non-integrated structure to the organization of information. This is achieved by expressing data modeling, storage and transfer in a particular non-hierarchical, non-integrated neutral form. The neutral form of the present invention enables complete parallel processing of both data storage and data transfer operations. It also enables the direct integration of separate but related data models and their data without remodeling or reloading. Finally, the present invention enables direct transfer of neutral form information in a manner that includes all of the properties required to independently understand and interpret each transferred data value.

Claims

We claim:

1. A method of organizing and storing a set of information in neutral form in a computer based environment comprising the steps of:

a) organizing the set of information into instance data sets;

b) defining a time basis for the collection of instance data sets;

c) organizing each instance data set into an instance cluster comprised of data instance nodes;

d) assigning to each data instance node in an instance cluster a distinguishing structural tag comprising the following three components:

a data reference;

a data type;

a data organization;

e) defining the components of the structural tag for each data value in each instance cluster;

f) assigning properties of the data value to each structural tag;

g) storing the names of all of the structural tag elements together with their respective definitions and properties in a suitable format;

h) combining each data value and its respective structural tag to form a neutral form expression of the data; and

i) storing the resultant neutral form expression of the data value.

2. The method of claim 1, wherein the data instance nodes in the cluster are organized into groups and subgroups for display purposes.

3. The method of claim 1, further including the steps of:

a) identifying at least one prime data set during said step of organizing the set of information;

b) dividing the data instance nodes into a plurality of sub-clusters; and

c) designating one of the prime data sets to appear in any data entry form for any sub-cluster that does not naturally contain it;

wherein data entry occurs separately at a sub-cluster level without loss of relationship to its proper instance cluster.

4. The method of claim 1 further including the steps of:

a) identifying at least one prime data set;

b) forming and assigning a new cluster;

c) identifying data instance nodes within the existing cluster and moving them to the new cluster;

d) excluding those members of one prime data set during said step of identifying data instance nodes;

e) copying nodes of the excluded prime data set in the existing cluster into the new cluster; and

f) changing the designation of the copied prime data set in the new cluster to a distinct data set;

wherein data entry occurs multiple times in the new cluster without redundant data entry in the remaining portion of the existing cluster.

5. The method of claim 1, wherein said step of forming and assigning a new cluster is done on the same time basis as an existing cluster.

6. The method of claim 1, wherein said step of forming and assigning a new cluster is done on an event time basis.

7. The method of claim 4, further including the step of repeating such steps of claim 4 when the newly formed cluster itself contains a one-to-many relationship.

8. The method of claim 1, further including the steps of:

a) identifying at least one prime data set;

b) forming and assigning a new cluster on either the same time basis as existing cluster or on an event time basis;

c) identifying a selected collection of data instance nodes within the existing cluster moving them to the new cluster;

d) excluding those members of one prime data set during said step of identifying data instance nodes;

e) copying nodes of the excluded prime data set in the existing cluster into the new cluster;

f) retaining the designation of the prime data set as a prime data set;

wherein data entry occurs independently in the new cluster and the remaining existing cluster.

9. The method of claim 1, wherein existing components of structural tag elements stored in the environment are used wherever their definitions and properties match those required for a data value.

10. The method of claim 1, wherein the definition of a new or an existing component of a structural tag element which is stored in the environment includes an indication of its equivalency to another existing component of the structural tag element stored in the environment.

11. A method of transferring data in electronic form from a computer comprising the steps of:

a) organizing and storing the data in neutral form that is to be transferred;

b) organizing and storing the names, definitions and properties of the structural tags used to express the data in neutral form; and

c) transferring the data expressed in neutral form along with the names, definitions and properties of the structural tags that make up that neutral form data.

12. The method of claim 11, wherein the names, definitions and properties of the structural tags used to express the data in neutral form are themselves treated as data and expressed in neutral form.

13. The method of claim 11, further including the steps of:

a) adopting a compatible system of data typing;

b) using the system to express in neutral form both the data values of a set of information being transferred and the names, definitions, and properties of their associated structural tags; and

c) combining and transferring both the data values and the names, definitions and properties of the structural tags of the data values in a single neutral form transfer file.

14. A method of incorporating neutral form data values and the names, definitions and properties of their associated structural tags into an existing computer environment comprising the steps of:

a) comparing the names, definitions and properties of the components of the structural tags of the data values with those present in the existing environment;

b) entering a data value structural tag component name, definition and properties into the dictionary system of the existing environment if it is not already present; and

c) recording equivalency where a structural tag component in the dictionary system of the existing environment is found to be different but equivalent;

d) thereafter, adding the data values into the neutral form file of the existing environment.

15. The method of claim 14, wherein the neutral form data values are new data values.

16. The method of claim 14, wherein the neutral form data values are transferred data values.

17. The method of claim 14, further including the step of incorporating a unique authoring designator of the originating environment during the naming of components of structural tags to insure a lack of overlap between the structural components of a data value and those in the existing environment.

18. A method of identifying cluster instances of data stored in a computer based environment which are dynamically linked comprising the steps of:

a) selecting a cluster defined in the environment's dictionary system;

b) determining the prime data sets in the selected cluster;

c) identifying other clusters defined in the environment's dictionary system that contain sets of instance data nodes which are the same as those of any of the prime data sets in the selected cluster; and

d) identifying those instance clusters in the environment which contain data values for a prime data set that match the data values of the same prime data set and have a compatible time basis for an instance of the selected cluster.

Description

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 their 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 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 these integrated structures. These have provided limited mapped segments that can be stored and retrieved in parallel. These segments have tended to be short relative to the overall retrieval process, prompting numerous mergings and analysis of intermediate results in order to determine the next correct stage of the storage or retrieval. All of this effort has consumed processor capacity, reducing the amount of computer capacity available for direct data storage pr retrieval work. The more parallel paths that were created, the more complex were the paralleling, merging and analysis efforts. The net effect was rapidly rising complexity and a declining percentage of useful storage or retrieval work produced from each parallel storage or retrieval path that was added. As a result of such inherent diminishing returns, the prior art was able to achieve only limited gains in storage or retrieval performance through parallel processing. The limited gains that were possible came only with major increases in the complexity of overall database storage, retrieval and transfer operations.

MODELED DATA--NEUTRAL FORM FILES

The storage, retrieval and transfer limitations imposed by database program dependent data hierarchies and structures has been particularly evident in large, complex databases involving diverse interactions. Such complexities were common to the engineering and manufacturing life cycle where there has been a great need to share data across applications, across vendor platforms and between contractors, suppliers and customers. As a result, efforts have been made to define a neutral form for data modeling, storage and transfer which would be independent of both the application program from which it was taken and of any application to which it would be applied. After considerable years of effort, the International Standards Organization (ISO) published ISO 10303, Product Data Representation and Exchange. This set a standard for both a neutral form (ISO STEP Neutral Form) and a modeling language (EXPRESS) through which data was to be organized for incorporation into the neutral form for storage and transfer.

Severe practical limitations have so far led to minimal implementation of ISO 10303. ISO STEP and EXPRESS do nothing to reduce the complexity inherent in prior art data modeling and storage. In fact, they have seemingly increased the complexity by adding a layer of requirements on top of those which have already existed. The overhead associated with constructing an ISO STEP neutral form was high, typically between 10 and 20 times the size of the raw data file. Furthermore, there was no technique for exchanging EXPRESS modeling information together with a neutral form file so that the neutral form information could be immediately interpreted upon receipt of the transfer. While eliminating direct application program dependence, the construction and interpretation of the neutral form file has also still been hierarchically dependent on the EXPRESS modeling form of hierarchical representation.

ISO 10303 could possibly offer potential for the effective use of parallel processing of ISO compliant data. The ISO STEP neutral form took the form of groups of the data and their related information, each of which was organized into a separate and independent record in the neutral form file. Because there was no dependency between groups of neutral form records, each could be processed completely in parallel.

U.S. Pat. No. 4,864,497 attempted to address the issue of practical neutral or common data structures for the storage and access of information by multiple application programs. However, the approach taken preserved data hierarchy and integration as the premise for data modeling. It also adopted a convention for neutral file definition that did not apparently reduce the complexity or rigidity present in prior art data structure technology.

ACCOUNTING FOR RELATIONSHIPS AMONG DATA

Prior art methods for addressing and accounting for relationships among data have been inherently complex, rigid, and highly application program dependent. As shown in FIG. 2, a hierarchy and integration of structures consistently have been employed in prior art data structure technology for the storage, retrieval and transfer of information having any significant complexity. At sufficiently low levels of complexity, non-integrated, non-hierarchical methods have been employed, but they have been limited to application dependent program formats or low level formats such as ASCII, the limitations of which have already been discussed above.

Hierarchy and integration in data structure, including all known forms of relational and object oriented technology, has involved the creation of a top-down logical network to describe all of the inter-relationships and precedence orders associated with the flow of information to be data modeled. The information described by this network has then been used to construct data models which have defined integrated structures for data storage. Since there was a direct relationship between the logical network, its corresponding data model, and integrated structure for the storage of actual data, changes in the logical network necessarily changed the data model, and in turn the integrated storage of the data. Since the networks involved were typically complex, single changes in the network could precipitate extensive changes in data modeling and storage. Any errors in logic introduced during a change, either in the logic network or data model itself or in the protocols used for storage, could result in faulty storage which produced errors in data retrieval and transfer. Any changes in the data model or data storage protocols made after actual data storage had begun created significant problems for future access to such data.

Prior art technology has sought first to define the largest possible universe within which to construct its information network and second to preserve the resulting information network and its related data model and data structure for as long as possible once it has been established. It has been forced to do so because of various factors. These have included: the complexity and interlocking relationships between the hierarchy and integrated structure of the data model; the corresponding complex structure of data storage and the difficulty in changing data hierarchy and structure once it has been established; the inability to first create and then to merge a series of separate more localized information networks, data models and data structures into one final composite or universal set. See for example Teorey, "Database Modeling & Design: The Fundamental Principles," Chapter 3.

In effect, large amounts of time, often man-months to man-years, have had to be spent gathering information to define a single internally consistent universal information network . This has then been translated into a single rigid fixed element data model and in turn into a rigid, fixed element data storage structure. Once the resulting database was placed in service, there was an almost unyielding resistance to change it. Since the work environment was actually changing daily, the result was that a data model and data storage structure were well out-of-date the day they went into service. However, they remained in service unchanged for extended periods of time because of the great complexity and cost associated with changing them.

U.S. Pat. No. 5,303,367 represented an apparent effort to overcome at least a portion of the severe limitations in the prior art technology. However, the concept of hierarchical and integrated networking, modeling and storage of information was retained completely. The complexity underlying the limitations of prior art data structure technology were thus maintained. This complexity was increased by introducing the concept of a networking structure inversion, which had to be carried out each time a new element of structure was added to the system. In addition, a format for data storage that provided all of the information necessary for use in retrieval and transfer without a-priori knowledge of the data was apparently lacking.

Recently, the concept of data warehouses has become a topic of interest. Data warehouses are one of the mediums for the co-mingling of data from different databases. Prior art data structure technology did not permit, as far is known, the co-mingling of different data models or their associated data without creation of a new model that encompassed each of the different models and their content. Also, substantial effort or "manual cleansing" of data structures and data information was typically required for legacy or new databases in order to incorporate the information into another database. Included in cleansing operations were the rationalization of a wide variety of different but equivalent data formats or configurations. This was done so that all of the values reported in the consolidated field name or set of field names conformed to the same format and configuration. These cleansing operations represented one of the largest expenditures of time and money in the creation of data warehouses.

SUMMARY OF INVENTION

According to the present invention, the universe of a particular scope of information to be modeled in a computer can be represented as a collection consisting of any number of distinct scope segments. Each of these scope segments (referred to later as instance segments) can be modeled separately as individual sets of information.

By formulating such individual scope segment models and their corresponding sets of information in a particular and neutral form, individual segments from the same universal scope of information are automatically and dynamically linked. Through their dynamic linking these independent scope segment models and their corresponding sets of information function as the equivalent of a single model and set of information for the universe of a particular scope of information. The neutral expression of the information also provides an effective medium for the transfer of information.

By these and other means, the method of the present invention not only overcomes the limitations of the prior art but it also introduces significant capabilities heretofore not available. It establishes a method for organizing and storing sets of information which achieves:

relationships between an unlimited number of different and individually modeled instance segments of the same universe of an instance as well between certain instance segments of different instance universes without a-priori knowledge of their existence, their structure or their content and without remodeling or reloading of the data for individual instance segments.

practical form for expression of individual instance data items in the instance data sets that comprise an instance segment which

contains all of the information required to understand and interpret each data item; and

yet is independent both of the source and the ultimate application of the information;

absolute independence of the individual instance data sets of an instance segment which enables complete parallel processing of information for storage as well as for retrieval and transfer;

practical non-hierarchical and non-integrated format for the structuring of instance data sets that comprise an instance segment which

is independent of the size and complexity of a set of information; yet

accounts for all relevant relationships among data items comprising each instance data set; and

allows dynamic versioning without loss of connection to information based on a previous version of data structure;

reconciliation of independently defined instance structures and instance data sets, enabling the direct co-mingling and integration of unlimited numbers of different sets of information;

reconciliation of different legacy instance data set structures and data values with reduced needs for manual cleansing and reorganization;

Parallel processor organization and storage of individual instance data sets across diverse sets of information encompassing different instance universes and different instance segments of any particular instance universe;

parallel processor retrieval of all or any portion of an unlimited number of different but related sets of information without any requirement for a-priori knowledge of the formats, structures, or contents or relationships among the various sets of information;

transfer of any retrieved information complete with all the properties and relationships required to understand and interpret the information without any requirement for a-priori knowledge of its format, structure, or content.

The method of the present invention that accomplishes these advantages is comprised of three components: (1) the organization of information into instances; (2) expression of instances of information in neutral form; and (3) universal typing and neutral form expression of data items in an instance together with the dictionary information on all properties of those data items for their transfer.

An individual instance is characterized as one segment of the universe of that instance. Individual segments of the same universe of an instance are intrinsically and dynamically related. A computer environment is characterized as a universe of instances, typically spanning more than one instance universe. Instances from different instance universes are intrinsically dynamically linked if they share certain information in common. The neutral form expression of instance information is employed to store individual data items in a set of information in a manner which at once enables both the complete isolation and therefore parallel processing of individual instance data sets and the recognition and exploitation of intrinsic dynamic links between different instance data sets for the retrieval and transfer of targeted subsets of diverse sets of information stored in the same computer environment. The neutral form is achieved by assigning to each data item in an instance data set a generalized structural tag comprised of three elements: (1) a data type; (2) a data reference; and (3) a data organization. The meanings and properties of each of these elements for a particular data item are stored in a dictionary system whose frame of reference is the computer environment within which the data item in neutral form is stored. All structural tags employed within a specific computer environment are defined to be internally consistent and unambiguous. The properties of the structural tag and the discrete data values stored in neutral form are used to target and retrieve instance data sets from an environment. Universal data typing is defined to enable simultaneous storage of the data items from both retrieved instance data sets and from the associated records in environrment's dictionary system. Using this universal data typing, dictionary data items employed in the structural tags of retrieved data items are themselves expressed in neutral form. The combination of retrieved instance data items and their associated dictionary data items, now expressed in the same neutral form, are combined into a single file for transfer. This transfer file is completely self contained, including not only the data items of interest, but all of the information required to understand and to interpret each such data item.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic diagram illustrating various types of prior art techniques and their characteristics.

FIG. 2 is a schematic diagram of alternative forms of structuring of data relationships.

FIG. 3 is a schematic diagram of data organization according to the present invention.

FIG. 4 is another schematic diagram of data organization according to the present invention.

FIG. 5 is a schematic diagram of possible relationships between data according the present invention.

FIG. 6 is a schematic diagram of neutral form expression of data organized according to the present invention.

FIG. 7 is a further schematic diagram of neutral form data organization.

FIG. 8 is a schematic diagram of organization of data dictionaries of meanings and properties for data organization according to the present invention.

FIG. 9 is a schematic diagram of universal data typing with the present invention.

FIG. 10 is a schematic diagram in summary form of a data storage, retrieval and transfer process according to the present invention

FIGS. 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25 and 26 are more detailed illustrative diagrams of data modeling portions of the diagram of FIG. 10.

FIGS. 27, 28, 29, 30, 31, 32, 33, 34, 35, 36 and 37 are more detailed diagrams illustrative of data storage and retrieval portions of the diagram of FIG. 10.

FIG. 38 is a schematic flowsheet diagram of implementation of the present invention in a computer system.

FIG. 39, 40, 41, 42, 43 and 44 are more detailed diagrams of the flowsheet of FIG. 38.

FIG. 45, 46, 47, 48, 49, 50 and 51 are diagrams supporting examples of the preferred embodiment of the present invention.

DESCRIPTION OF PREFERRED EMBODIMENT

1.0 INTRODUCTION

According to the method of the present invention, it has been found that information can be modeled by organizing various related data values into groups called instances. Any universe of related data items, i.e., the universe of an instance, can be subdivided and organized into a series of instance segments, each having a collection of instance data sets, one for every occurrence of that instance segment. It has been further found that by modeling and storing the instance data sets of individual instance segments in a particular non-hierarchical, non-integrated neutral form, both the models and their data in neutral form can be combined without remodeling or reloading to form a larger portion of the universe of an instance. According to the present invention, this neutral form at once also gives rise to greatly simplified modeling techniques, parallel processing in both data storage and retrieval operations, and simplified transfer of all or portions of the stored data.

According to the method of the present invention, an instance segment is structured in non-hierarchical, non-integrated form as a cluster consisting of non-hierarchical data nodes, one for each data value in an instance data set. Each data value in an instance data set, also referred to as an instance cluster, is expressed in neutral form by assigning to it a generalized structural tag that is formed of three elements which, taken as a whole, contain all of the definitions and properties required to describe that data value. These definitions and properties are recorded in a dictionary system according to the method of the invention. One such dictionary system is associated with the contents of each neutral form file. Multiple instance segments in the same neutral file use the same dictionary system. The first element in the structural tag is data type, which specifies the physical form (length, format) of the data value. The second element is data reference, specifying the name and properties (definition, units, upper and lower limits, constraints, etc.) of the data value. The third element is data organization, specifying the other data references, their properties as a group, and their respective data values in relation to the tagged data value.

According to the method of the present invention, a neutral form file can contain instance segments from different instance universes. Furthermore, in the method of the present invention relationships exist between cluster instances in a neutral form file to the extent that two cluster instances share certain data references and their respective data values in common. These relationships are deemed dynamic, because they happen automatically and as a function of the cluster instances that are present at any one time in the neutral form file.

2.0 NOMENCLATURE

For ease of understanding, the terms used in the subsequent portions of the description of the present invention and their meanings are set forth alphabetically below:

    __________________________________________________________________________
    Term       Meaning
    __________________________________________________________________________
    Alias      One dictionary entry that is equivalent to another.
    Association
               A name that is closely associated with a dictionary entry
               name.
    Author     User of the method of the present invention who creates
               entries in
               the dictionary system.
    Author ID  Identification number unique to the world of users of the
               method
               of the present invention which insures discrimination of
               authors
               in all data transfers between two parties.
    Cluster    Non-hierarchical collection of data reference nodes which
               define
               an instance in the universe of an instance
    Cluster - Associated
               A cluster that has been formed from a comprehensive initial
               cluster.
    Cluster - Comprehensive
               Single cluster formed during the initial creation of the data
               model
    Initial    for a set of information which contains all of the data
               references
               associated with an instance data set for that set of
               information.
    Cluster - Group
               A heading within a cluster which groups, e.g., logically, a
               collection of data references.
    Cluster - Instance
               An instance data set
    Cluster - Nested
               Cluster inserted into another cluster in a one-to-one
               relationship
               such that
               .cndot.
                 the collection of instance data sets of the nested cluster
                 are accessible from within the other cluster; and
               .cndot.
                 one and only one of the nested instance data sets is
                 selected to function as a part of the instance data set of
                 the other cluster.
    Cluster - Node
               One data reference in a collection comprising a cluster.
    Cluster - Sub-cluster
               Collection of data references in a cluster which are to
               appear
               together in a data input form.
    Cluster - Subgroup
               A subheading within a cluster which groups, e.g., logically,
               a
               collection of data references under a cluster group heading.
    Data       One or more data values or data items
    Data Instance Node
               A node on an instance cluster corresponding to one data value
               in
               an instance data set.
    Data Item  One data value in a set of information
    Data Organization
               Combination of the cluster instance structure and the cluster
               instance data set properties associated with any data value.
    Data Organization -
               The set of data references and data values for one cluster
               instance
    Instance Data Set
               data set.
    Data Organization -
               The collection of data references and properties comprising a
    Structure  cluster to which a data item belongs.
    Data Reference
               Name, description and definition of certain properties of a
               data
               value
    Data Reference - Name
               Descriptive name for a data reference
    Data Reference --
               Discriminator of the context within which a data reference
               name
    Schema     is to be applied.
    Data Set - Distinct
               A collection of data references in a cluster whose sets of
               data
               values are taken from a unique population of data value sets
               for
               the collection but can be (or are) employed in more than one
               instance data set of the cluster in which they reside.
    Data Set - Prime
               One or more data references in a cluster whose data values
               are
               taken from a unique population of data value sets and which
               values are employed in only one instance data set for the
               cluster
               in which they reside.
    Data Set - Subordinate
               Data items comprising individual instances within the universe
               of
               an instance., i.e., segments of the universe of an instance.
    Data Type  Physical form property of a data value, e.g., numeric,
               alphanumeric, photo, drawing, sound, video, etc.
    Data Value One instance of a data reference.
    Dictionary - Conditional
               Entries made into the dictionary system of an environment
               during
    Entries    the modeling of an instance, which entries have not been
               reconciled with existing dictionary entries.
    Dictionary -
               Collection of dictionaries which list and define all of the
    Environment System
               properties of the structural tags that mark instance data sets
               stored
               in an environment.
    Domain     Collection of one or more dynamically linked models.
    Environment
               The universe of instances whose instance data sets are
               recorded in
               a single neutral form file which is governed by a single
               dictionary
               system.
    Glossary   Collection of properties for entries in a dictionary.
    Inputting  Entry of a single cub-cluster of data items or a complete
               cluster
               instance data set into a neutral form file.
    Instance - An
               A set of related data references for any defined scope of a
               subject.
    Instance - Universe of an
               The total set of related data references for a defined scope
               of a
               subject.
    Instance Data Set
               Set of data values for one occurrence of an instance.
    Instance Segment
               A set of related data references which is one part of the
               universe
               or total set of related data references for a particular scope
               of a
               subject.
    Instance Structure
               Collection of data references comprising an instance.
    Instances - Universe of
               An environment - collection of the universes of one or more
               instances.
    Kid        Identifier for a data reference name
    Lid        Identifier for author
    Linking -- Dynamic
               Two clusters where (a) one contains a prime data set; (b) the
               other
               contains either a matching prime data set or the same set of
               data
               references as a distinct data set; and (c) the two clusters
               have one
               or more sets of data values in common for these matching data
               sets.
    Linking -- Opportunistic
               Two clusters that share one or more non-prime data set data
               references and their corresponding sets of data values in
               common.
    Loading    Multiple sets of inputting performed in bulk.
    Model      One cluster or two or more non-integrated but dynamically
               linked
               clusters which define the relationships and properties of a
               set of
               information.
    Neutral Form
               Data values for an instance data set, each marked with a
               structural
               tag.
    Node       One data reference in a collection that comprises a cluster.
    Note ID    Identifier for a node in a cluster, e.g., the combination of
               Kid, Sid,
               Lid.
    Primary    The preferred one among a collection of aliases of data
               reference
               names or schema names.
    Properties Attributes of a piece of data, a group of data, or a structure
               for a
               group of data, e.g., name, ID, definition, units, etc.
    Reconciliation - Data
               Process for evaluating new data values as (a) being the same
               as;
    Values     (b) being equivalent to; or (c) being different from one of
               the
               discrete data values already in the environment for a
               particula
               data reference.
    Reconciliation -
               Process for evaluating dictionary entries external to an
    Dictionary environment as (a) matching with, or (b) being equivalent to,
               or
               (c) being uniquely different from an existing entry in an
               environment system dictionary.
    Schema     See "Data Reference - Schema"
    Set of Information
               Collection of data values comprising one or more occurrences
               of
               an instance together with sufficient properties of those data
               values
               to enable their understanding, interpretation and assignment
               to
               discrete instance data sets with an arbitrary but definable
               pattern
               to the ordering of the data values in each instance data set.
    Sid        Identifier for data reference schema name
    Structural Tag
               Three generalized attributes of a data value, viz., type,
               name, and
               organization, whose definition and properties completely
               define a
               data value within a cluster instance.
    Synonym    A name that has the same meaning as that of a dictionary
               entry
               name.
    Time Basis Frame of reference of time for either an instance or a data
               reference where null time is treated as "event".
    Transfer Form
               Combination of one or more instance data sets and their
               supporting dictionary information, all in neutral form.
    Transfer Form - Data
               One or more Instance data sets written in neutral form.
    Transfer Form -
               Dictionary entries employed by one or more instance data sets
    Dictionaries
               written in neutral form.
    Universe   The collection of domains, models and clusters contained in
               an
               environment.
    Version    Designator which allows for the discrimination of an existing
               cluster which has had one or more instance data nodes added
               or
               removed.
    __________________________________________________________________________

• Inventors
  Odom, Paul S.; Massey, Michael J.;
• Published
  Nov-24-1998
• Current US Classes:
  707/100
  707/101
  707/102
  707/202
  707/203
• Application #
  789860
• International Classes
  G06F 017/30
• Field of Search
  707/202 707/203 707/100 707/101 707/102
• Examiner
  Black; Thomas G.
• Agent
  Pravel, Hewitt & Kimball
• US Patent References:
  4864497
  5278979
  5303367
  5355493
  5357629
  5511188
  5640559
  5655121
  5680615