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6. OGIS Information Communities Model - Managing Data Heterogeneity

6.1 Introduction

The pluggable computing model for distributed geoprocessing grew out of discussions about how general models of component-based and common interface-based distributed computing could be applied to geoprocessing and geodata access. We now present a concept, the OGIS Information Communities Model, which grew out of discussions about how the notoriously difficult problem of federated databases could be partially and practically solved for the purpose of sharing information between databases that contain complex geodata with inconsistent geodata feature definitions.

Much of the stimulus for the OGIS Project comes from a need to share geographic information more effectively between individuals and organizations who not only store and manipulate geographic data in different ways on different computer systems, but who think about, talk about, and visualize geography in very different ways. The OGIS Information Communities Model helps solve the human problem of communication between communities who, by necessity or chance, describe geographic features in different ways. To an ecologist, highways are barriers with particular characteristics affecting populations of plants and animals. To a civil engineer, highways are legally bounded public properties with particular pavement structures, drainage problems, load requirements, etc. An ecologist and a civil engineer might exchange data easily because they use the same software, but they won't define highways in the same way, so exchange of information will be limited.

The OGIS Information Communities Model was devised to enable groups such as ecologists and civil engineers efficiently manage the semantics (or feature schema mismatches) of their own geodata collections and get maximum benefit from each other's geodata collections, despite semantic differences.

An Information Community is a collection of people (a government agency or group of agencies, a profession, a group of researchers in the same discipline, corporate partners cooperating on a project, etc.) who, at least part of the time, share a common digital geographic information language and share common spatial feature definitions. This implies a common world view as well as common abstractions, feature representations, and metadata. The feature collections that conform to the Information Community's standard language, definitions, and representations belong to that Information Community.

Keep in mind that the details of the OGIS Information Communities Model have not, at the time of this writing, been fully developed and approved by the OGIS Project Technical Committee for inclusion in the OGIS detailed specification. The detailed specification and DCP implementation specifications may differ from this description in significant ways.)

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6.2 Basic Assumptions

Below are the basic assumptions underlying the OGIS Project's concept of Information Communities.

  • Assumption 1: Each Information Community has one set of semantics. A set of semantics may include, but is not limited to, any or all of the following: metadata to describe the content of feature collections; feature class definitions; attribute definitions; valid feature and attribute relationships; data capture guidelines for features and attributes; symbol sets for feature representation; rule sets for feature portrayal or display; relationships and dependencies between features, attributes, metadata, etc.; methods; and behaviors. Even matters of policy such as: data management procedures; update cycles to manage feature, attribute, and metadata obsolescence; units of measure and degree of measurement precision; accuracy thresholds, and so forth contribute to the set of semantics that define an Information Community. By the very definition of Information Community, the set of semantics gives an Information Community its identity. This set of semantics sets the bounds within which an Information Community operates and sets the standard for understanding and interpretation for all users within the Information Community. Differences between sets of semantics differentiate one Information Community from another.
  • Assumption 2: Each distinct and unique feature collection is owned by exactly one Information Community. Each feature collection was created according to a set of semantics that dictate the interpretation of its content. This standard interpretation enables the feature collection to be used consistently by a body of users that has agreed to abide by the defined set of semantics. The feature collection cannot be interpreted completely and accurately by another Information Community with a different set of semantics without being subject to a semantic translation that may introduce potential loss of information. Once a feature collection is translated for use under a new set of semantics, the derivative feature collection is now a member of the second and not the original Information Community.
  • Assumption 3: Each distinct and unique logical catalog is owned by exactly one Information Community. A catalog is the means by which an Information Community advertises its holdings to the rest of the world, and it also provides a structure for the parsing of semantics. Within each catalog is a set of metadata packages that registers the existence, location, and description of many feature collections. Since these metadata packages describe individual feature collections that are specific to the Information Community, it follows that the catalog that contains them is also specific to an Information Community and is owned exclusively by that Information Community. Therefore, each catalog is unique to its own Information Community. There can be more than one catalog per Information Community. If more than one exists, there needs to be an "authoritative" catalog that serves as the master for a set of slave catalogs. (Note that the FGDC's Metadata Standard initiative provided the groundwork for this scheme, both in terms of concept and in terms of getting organizations to manage their metadata.)
  • Assumption 4: Inter-community sharing of geodata requires the existence of at least two Information Communities. A Semantic Translator is specific to a pair of Information Communities, but more than two might coordinate their Semantic Translators. An Information Community may use a Semantic Translator to filter its "view" (in the database sense) of the data in another Information Community, perhaps deciding subsequently to reduce the specificity of feature and attribute content in their geodata for the purpose of integrating their data. When data is imported or exported, we will call the Information Community with the feature and attribute deficiency the target Information Community. The Information Community that supplies the needed geodata to the target Information Community is called the source Information Community. These terms will be used in later sections to eliminate the confusion associated with the identity of Information Communities in later examples.
  • Assumption 5: Each Information Community has one Semantic Translator for each external source Information Community from which it maps feature collections. The Semantic Translator contains all of the information it needs to find and translate feature collections from the source to the target. The Semantic Translator must contain mappings from the target Information Community's own semantic constructs to those of the source Information Community to support interpretation and translation assistance when the exchange of feature collections between the two Information Communities occurs. These mappings will be created through comparison analysis of the two sets of semantics for the Information Communities involved, and this will definitely be a manual, labor-intensive process. The mappings between the manually created sets of semantics will in the future be implemented through middleware, possibly with the aid of artificial intelligence or knowledge-based software.

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    6.3 Catalogs, Traders, and Semantic Translators

    In the OGIS Information Communities Model, Information Communities rely on the use of special registries that contain manually derived semantic models which enable a mapping of terms and/or definitions from one Information Community to another.

    Individual Information Communities' datasets are bounded by a body of shared context and semantics. Foreign Information Communities'datasets have different context and semantics, but two bodies of context and semantics can be reconciled to some degree so that data sharing is possible. Each Information Community is free to negotiate relationships with one or more foreign Information Communities to share some or all of their data, as follows:

  • 1. First, an Information Community agrees to expose the contents of one or more feature collection Catalogs to a larger community. (Feature collections are discussed in Chapter 5.) The Information Community, by whatever decision-making process it deems appropriate, determines what information will be exposed and to whom it will be made available. There are various ways to accomplish this. One concrete possibility is a Trader. A Trader exists outside of the Information Community as part of the infrastructure of the distributed computing environment. (CORBA, for example, has a concept of Traders.) Exposing the existence of a catalog or catalogs through a Trader (or an ftp site or the World Wide Web) may comprise the full extent of the Information Community's effort to share its data. (See Section 7.8 for more information about Traders.)
  • 2. In addition, the Information Community may agree to work with a partner Information Community in the manual construction of one or more semantic mappings (analogous to bilingual dictionaries) which will be used to facilitate one way or two way data sharing between itself and its partner Information Community. This cooperative task is mainly a matter of coming to agreement on the precise meaning of each metadata element type, and capturing the results of this work as a series of semantic distinctions and translation rules in a Semantic Translator. After it is so configured, the Semantic Translator translates semantic information automatically during data transfers, and perhaps also produces a report describing the data's fitness, quality, limitations, etc. This repository contains the entire extent of interpretive information needed to enable information to be communicated successfully at the resolution which has been negotiated by the Information Communities themselves. In some instances this may require only that a Thesaurus of terms be constructed and shared. In other instances a comprehensive inventory of detailed schema information (data definitions, data dictionary, constraint mapping, procedures, type fonts, attribute definitions, rule sets, policy etc.), may be required to achieve the level of coherence required by the communities themselves. An Information Community may present multiple views of its information as a result of creating separate Semantic Translators with multiple partner Information Communities. Note that the construction of a Semantic Translator may involve the agreed upon modification of one or both of the schema of the two communities involved, or they may decide that some subsets of data are not worth sharing.
  • Figure 6-1 Catalogs list featurefeature collectionfeature collections held by an Information Community. Facilities such as Traders (a non-geodatageodata-specific DCPDCP service) give out-of-community data seekers a Catalog summary and a pointer to an Information Community that has geodata to shareshare. A Semantic TranslatorSemantic Translator automatically translates semanticssemantics, enabling the target Information Communitytarget Information Community to make use of the source Information Communitysource Information Community's information (to the degree that the source Information Community's semantics can be translated into the target Information Community's semantics).

    Figure 6-1 provides one picture of data integration between Information Communities. Information Communities 1 and 2 each have one (or perhaps more) Catalogs which are the basic means for geodata discovery and access within the Information Community. Catalogs are collections of entries, each of which describes and points to a feature collection which is represented here with a disk storage symbol. (See previous chapter for a discussion of feature collections.) Catalogs, like databases and database tables, provide a structured view of selected information and provide both a synopsis and a roadmap to a feature collection or a data set that an application can use. Information Communities may make Catalogs and their feature collections readable by out-of-community data seekers, perhaps advertising them in traders. (See Section 7-8.)

    By the definition of Information Community, all features contained in all of an Information Community's feature collections are consistent in terms of their semantics. (That is to say, the features conform to the same schema.) So users 1, 2, and 3 can trust data contained in Information Community 1's catalogs to conform to Information Community 1's set of semantics, and users 4, 5, and 6 can trust data contained in Information Community 2's catalog to conform to Information Community 2's set of semantics.

    User 6, seeking additional information not available with Information Community 2, uses a trader to discover that Information Community 1 may have helpful data. A look at Information Community 1's catalog confirms that the desired data is indeed available, so User 6 acquires the data, and in the process the Semantic Translator (which Information Community 2 has configured with the cooperation of Information Community 1) automatically translates the semantics. Of course, as the rest of this chapter explains, the translation is only as good as the semantic mapping configured into the Semantic Translator.

    Information Communities can also intersect, and clearly they often will, because any two groups of geodata users are highly likely to have some common feature definitions and feature collections and some different feature definitions and feature collections. Similarly, one Information Community is a subset of another Information Community if all of its feature definitions and all of its feature collections are subsets of those of a larger Information Community.

    There are now, and will undoubtedly continue to be organizations that maintain base sets of geographic information whose definitions and meaning are shared across a group of communities with otherwise distinct interests and semantics. If the USGS, for example, were principal steward of a feature collection including geodetic network, topography and hydrography for the U.S., and if a particular state's geology office were authorized to develop new geodata of these types while maintaining the USGS's strict semantic standards, the state's geology office would be an Information Community that would be a subset of the USGS Information Community for these particular types of geodata.

    For many purposes, partial data sharing and/or ad hoc data sharing will be quite adequate, and it will be common. For example, a user may find through a Trader that Information Community 1 has land use data for New York State. The Trader may be no more specific than that. If Information Community 1 exposes its semantic data and its catalogs to the general public, the out-of-community user can determine whether Information Community 1 uses acceptable semantics for land use data, and whether particular data is available. The semantics are acceptable and the data is available, so the user obtains data from Information Community 1, without negotiation or discussion, and without benefit of semantic translation. All of this could be done and is being done to a degree with the World Wide Web instead of a Trader, and without OGIS. In Technical Committee discussions, this kind of data access is called "pillaging," in contrast to data integration achieved through a Semantic Translator.

    With OGIS interfaces available in a variety of data access products, but without Semantic Translators, users will share data as they do today, except that the OGIS interfaces will make queries more powerful, make data access much faster and easier, and make it possible for heterogeneous applications to access data held in heterogeneous databases. OGIS interfaces will even discipline and facilitate "data fusion" methods that convert, for example, some of the information in remote sensing images into GIS thematic map layers that conform to a Base Information Definition. But without Semantic Translators, semantic mismatch will need to by addressed by metadata standards and inter-group coordination alone. Metadata standards and intergroup coordination are an essential beginning, and work of this kind done now will make it easier to configure Semantic Translators later.

    It is frankly difficult to predict what the Information Communities picture will look like ten years from now. It may happen (because the Net will be so big, because Information Communities will be so complex and fragmented, and because there will be so many sources of data) that groups that recognize themselves as Information Communities will publish their geodata offerings in Traders, but the primary way in which others will get that data will be "pillaging." That is, in this scenario data will usually be acquired from a source Information Community on an ad hoc basis with little use of well-tuned Semantic Translators developed and maintained through communication and cooperation. More optimistically, it may happen that the maintenance and use of Semantic Translators will become an essential part of a new global culture, one of the ways in humans will constructively employ automation while interacting professionally to organize a world that seems likely without such cooperative efforts to become increasingly chaotic. The Information Communities concept may even be applied to systems for supporting intergroup communication in non-geographic information circles.

    In the next section, we look at some of the reasons for the chaos of the current geodata semantics situation, to give potential OGIS developers an appreciation of the scope of the problem. Virtually all the experienced geoprocessing software developers who learn about OGIS believe that it is our best hope for solving the problem, but none of them expect it to be easy. Clearly the cooperative manual process of building feature dictionaries and Semantic Translators needs to be understood and promoted, and the following discussion illuminates that process. Chapter 7 goes into greater detail about Catalogs, Traders, and Semantic Translators in an explanation of the OGIS Services Model.

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    6.4 The Information Community Concept - Like Human Language

    This section presents six levels of semantic mismatch. Semantic translators will be designed which will indicate to the user how much data is being lost or questionably placed, according to which of these levels applies at the time of attempted data integration. Semantic Translators will not hide the tough decisions from users, even though they will make the data conversion effortless.

    To begin to understand the reasons for the complexity of the problem of data sharing, it is useful to consider the close analogy between human language and spatial/temporal computing.

    Individuals sharing a context such as a professional society or an institutional culture usually use a common human language to describe that context and to adhere to a similar frame of reference in regard to it. They see the world through the same eyes and characterize it using shared descriptors. Standardization of meanings facilitates unimpeded, accurate communication.

    For instance, doctors are likely to communicate more effectively with one another than with petrochemical engineers or physicists who each rely on their own highly stylized language to convey specialized information. Within the community of doctors, subsets of specialists share vocabulary and skills which are not found across the community of doctors at large. Communication within the sub-groups is specialized and efficient in regard to the area of specialization that defines the group.

    The complexity of this picture deepens as we see how each individual and institution belongs to multiple groups. Consider two ways in which the federal bureaucracy can be diagrammed: One model might be a department drawn with a single box for each agency and a box for each division within each agency and so on. The top level box representing the federal government constitutes a "root" Information Community representing a corporate culture that is ubiquitous within the federal government and which is common to all the participants. As the model is traversed from top to bottom an increasing number of Information Communities emerge, sharing an increasingly specialized body of information and semantics.

    Taking a lateral view of the same diagram, based on function rather than organization affiliation, consider the subset of all federal employees who are responsible for accounting related to travel. Under this functional view there is a particular set of concerns and tasks which is shared by a majority of this group in regard to administering travel. In fact, in many respects a travel administrator in the USDA might be more closely linked, in terms of information and semantics, to a travel administrator in the EPA than to a colleague in the next office and the same agency whose primary job is inventory control.

    Common language, common conceptual model, and common meaning create semantic integrity which in turn makes effective, unambiguous communication possible. These are the factors which combine to define individual Information Communities, and which create the semantic separation between Information Communities.

    In perfect information sharing, information is exchanged with no degradation or loss of meaning. Within a community that shares a language, a common set of definitions, and a consistent conceptual model, lossless transmission of data can occur. However, as the language, the definitions, and/or the conceptual model diverge between groups, information sharing is imperfect and information is lost unless specific steps are taken to control the process.

    Logically, there are at least three distinct cases in which information may be lost when communicating between different language groups, and by analogy, between Information Communities:

    1. 1) In the first case, definitions and concepts are shared but there is no common language between the two groups, or the groups share a common language but use dramatically different dialects. This problem is corrected through simple translation using a bi-directional mapping between the two languages. As long as the languages themselves are stable and there is a 1:1 relationship between relevant terms this mapping solution supports effective communication. For instance, if A and B want to talk about logistics and the overland transportation of goods and A refers to trucking and B knows the large vehicles as lorries, they can agree that the mapping of "truck" = "lorry" will be used to communicate this concept.
    2. 2) In the second, somewhat more abstract case, a stable base of definitions for terms is not shared between the communities. Correcting for this requires a direct mapping of shared definitions plus a set of interpretations for terms that can't be mapped. Where there is a 1:M mapping of definitions between communities, generalization and a consequent loss of information will occur when mapping multiple, specific definitions to one more general definition. For example, an Inuit using an Inuit language which may have more than a dozen nouns that refer to different kinds of snow will not be able to convey subtle yet important distinctions about snow when speaking with someone whose language has only one word for snow.
    3. 3) Finally, there is the case in which basic concepts are not shared between the communities. For instance, communicating in regard to snow or mass transportation technology would be difficult if both parties didn't have at least some notion or concept of these things. In the event that such a basic prerequisite for communication is not in place it is very difficult, if not impossible, to share information.

    When applying these insights to geodata sharing it is important to keep in mind that geographic feature definitions become more specialized as we focus more finely on narrow applications. For instance, we all can agree on a general definition of a road, but four different GIS Information Communities will see four distinctly different phenomena carrying with them very different sets of information. To the traffic network analyst the road is a vector with a series of defining attributes which might include width, impedance, lane numbers, sidewalks, signals, and intersections. To the remote sensing specialist the road represents a particular spectral reflectance value that guarantees that it is neither a wetland nor a cultivated agricultural area. To the cartographer the road represents a mapping feature which must be characterized using a specific combination of color, style and label in order to conform with a rigid rule base established for roads. To the civil engineer, the road has compacted soil, gravel base, storm drains, and paving material, as well as cadastral boundaries and many other characteristics.

    It is precisely these kinds of semantic anomalies and conceptual inconsistencies that the OGIS Information Communities Model addresses.

    What the Geodata Model, by itself, Doesn't Do -Two Examples

    The following examples illustrate the need for the Information Community Model:

    Paved with Good Intentions -One Example of Open Geodata Model's Limitations

    A cul-de-sac (a usually short road segment terminating in an enlarged turn-around area) instance, expressed using the Open Geodata Model, clearly exposes the geometry that establishes the path of the road on the face of the Earth. There may be additional information in the form of attribute values attached to attribute names that are exposed, such as:

    Attribute Name Attribute value

    surface material #2 grade asphalt

    width (meters) 10

    Maintainer Park and Recreation Dept.

    Last maintained 10/12/92

    In this example, the meaning of the attribute values is made clear, it appears at first glance, through the use of careful attribute names. However, additional explanation is required to ensure that precise meaning has been conveyed.

    The attribute value "#2 grade asphalt" assumes the existence and accessibility of a reference or authority that maintains exact definitions and conformance tests for paving materials. Further, this assumes a technology that enables such definitions to be published, managed and supported in accordance with regularities or standards that are known within a community. (OGIS must provide a way to allow the community that published the road segment information to refer to the authority that establishes such standards).

    The attribute value "10" is more subtle. There are several issues: Is the value "10" rounded up or down? What are the allowed values of the widths of roads within the community that exposed this feature? Is the value "10" chosen from a "domain of valid widths," such as {6, 8, 10, 12, 14, 18, greater than 20}? Is the number "10" an integer, or is it floating point, or is it ASCII? Would it be correct to infer that the width of the road is 10 meters plus or minus one millimeter? Does the value 10 meters refer to the width of the pavement, the width of the right-of-way, the width of the narrowest spot along the segment, the average width, the width between the curbs, or the width of the largest object that can move along the road? OGIS must provide a technology that allows the community that exposed the road segment to explain, in structured and/or natural language, the semantic meaning of the value "10," and the meaning of the attribute name "width (meters)."

    The attribute value "Park and Recreation Dept." also needs explanation. What does "maintainer" mean, exactly? How does one interact with the "Park and Recreation Dept."? OGIS will support communication of such explanations.

    The date 10/12/92 could refer to the maintenance of the road itself, the maintenance of some subset of the road, or the maintenance of some aspect of the feature collection in which the road segment is represented. OGIS will provide a way to allow the provider of the feature to expose sufficient semantics to resolve such questions.

    There are additional ambiguities in the road segment cul-de-sac instance. For example: the "dead" end of the segment may be represented in different ways:

    There are many other possible representations.

    Furthermore, there is an even deeper question: what is the threshold for collection of a cul-de-sac instance (used during the creation of the feature collection)? Are instances of very short "dead end" road segments (say 10 feet long) represented as cul-de-sacs in the feature collection? What are the capture criteria? If a feature collection contains one cul-de-sac instance, can one assume all such instances are present? What are the completeness characteristics of the feature collection?

    OGIS must provide a technology that allows these conventions and characteristics to be exposed and explained. Without such explanation, information sharing cannot be trusted because the receiver of the Information Cannot know fully what the sender intended. Part of the problem can be addressed by metadata, but the real answer is to annotate the attributes of features with descriptions to encourage the use of numbers with physical units attached as a basic type when referring to physical measurements.

    A Bridge too Far -Another Example of the Open Geodata Model's Limitations

    A user may be interested in bridges and their lengths. However, a feature collection with no "bridge" feature type may contain instances of the following feature types: viaduct, overpass, trestle, catwalk, culvert, underpass, tunnel, and causeway. Moreover, each of these may have an attribute called length. The user may need to know how length is measured in each instance: from what structural member to what other structural member, and to what accuracy.

    OGIS must provide a technology that make is possible for Information Communities to codify all possible elements of such varied sets. OGIS technology can then be used to expose the attributes of these elements programmatically and to structure ways in which the attributes can be presented and explained to the user.

    Data Sharing Examples

    In this section a series of examples are presented which illuminate the assumptions developed in the previous section and attempt to convey the diversity which is inherent in the way geographic information is perceived and used.

    Water, water everywhere: a hydrologic example

    A system of feature class definitions within a distinct Information Community is usually described in terms of a feature/attribution schema. The following examples of inter-community feature/attribute schema translations illustrate the conceptual interaction of two Information Communities which coexist within the same discipline.

    Hydrography, according to the Department of Defense Glossary of Mapping, Charting, and Geodetic Terms, is "the science which deals with the measurements and description of the physical features of the oceans, seas, and lakes, and their adjoining coastal areas, with particular reference to their use for navigational purposes." Hydrographic data has an important geospatial component. Two well-known systems of feature class definitions used to describe features within the hydrographic discipline are the S-57 Object Catalogue, and the Feature and Attribute Coding Catalog (FACC). The S-57 Object Catalogue is part of the IHO Transfer Standard For Digital Hydrographic Data developed by the International Hydrographic Organization. The FACC is part of the Digital Geographic Exchange Standard (DIGEST) developed through an international cooperative effort by the member nations of the Digital Geographic Information Working Group (DGIWG).

    Both of these schemes have a robust ontology of hydrographic features and attributes to support geospatial use of these data, though each is used for a slightly different purpose. The S-57 Object Catalogue is primarily intended to support the visual display component of electronic charts on board commercial sea-going ships, and is used in the United States by the Department of Commerce in producing digital hydrographic charts for the Electronic Chart Display Information System (ECDIS). The FACC, as part of DIGEST, was developed to support broadly applicable geospatial analysis requirements, and is used in the United States primarily by the Department of Defense in the generation of the Defense Mapping Agency's Vector Product Format (VPF) products, including the Digital Nautical Chart (DNC). Semantic translation between the two schemes may be necessary to support the exchange of hydrographic data under international exchange agreements aimed at updating and improving the hydrographic charts and safety of navigation information produced and maintained by both communities.

    In translating between two feature class definition schemes, there are at least six different results that can occur. Let's look at some examples of these results in terms of translations that might take place between feature classes in the S-57 Object Catalogue and the FACC. (These schemes are used in these examples for illustrative purposes only and an endorsement or critique of either scheme should not be inferred.)

  • Result 1: There can be an exact match of the meanings of the definitions between the two feature classes, with no loss of information in the translation. For instance, an "AQ070 Ferry Crossing" in the FACC is the same as a "FERYRT Ferry route" in the S-57 Object Catalogue.
  • Result 2: Though there may not be a direct semantic match between definitions in the two feature class definition schemes, an exact translation can be achieved through the use of information carried in the attributes of one or both feature definitions. For example, a "LITFLT Light float" in the S-57 Object Catalogue is not an exact match for the FACC feature class "BC040 Light", but through the use of the FACC attribute "BTC Beacon / Buoy Type Category", the Information Can be recovered without loss when an attribute value of "BTC006 Light Float" is used.
  • Result 3: Feature aggregation is required to achieve translation between feature classes. For example, the FACC feature classes "BJ040 Ice Cliff", ÔBJ065 Ice Shelf", "BJ070 Pack Ice", "BJ080 Polar Ice", and "BJ100 Snow Field / Ice Field" must be aggregated to the feature class "ICEARE Ice area" in the S-57 Object Catalogue. Once this aggregation takes place, it would not be possible to implicitly determine whether an "ICEARE Ice area" had been originally captured as an ice shelf, an area of pack ice, or something else. Thus, feature content would be lost in the translation and would not be recoverable without the attachment of some kind of caveat to the feature to describe the translation process and maintain an accurate lineage of the feature.
  • Result 4: Feature decomposition is required to achieve translation between feature classes. This case is the reverse of Result 3, with more ominous implications. Using the feature classes from the previous example, a direct and accurate semantic translation of an "ICEARE Ice area" feature from a dataset based on the S-57 Object Catalogue to one based on the FACC is not possible. To support such a translation, a modification to the FACC schema would be required to create a new Ice Area feature class. If schema modification is not feasible, a verification process, probably accomplished through some type of source data evaluation, image analysis, or ground truth reconnaissance process, would be required to properly classify the Ice Area as one of the existing snow-and-ice-related feature classes supported in the FACC. Both of these alternatives may take weeks or even months to achieve, so the only option that remains may be for the "ICEARE Ice area" to be classified in the FACC, incorrectly and misleadingly, as one of the snow-and-ice-related feature classes described previously. In this case it would be necessary to include a caveat with the feature to capture the discontinuities in the translation process and to maintain an accurate lineage of the feature.
  • Result 5: A match between the meaning of two comparable features in different Information Communities is probable, but further clarification on the definition of the supporting feature classes is required before the match can be verified, or the translation may be dependent on the representation of the feature in its respective Information Communities. For instance, a "BA020 Foreshore" in the FACC may be the same as an "ITDARE Intertidal area" in the S-57 Object Catalogue. Both definitions specifically refer to measurement of the shoreline, but in respect to different datums. "BA020 Foreshore" references Mean Low Water, while "ITDARE Intertidal area" references Mean High Water. A conversion is required due to the differences in datum registration, so the same instance of a shoreline may ultimately be represented very differently by the target Information Community. Again, a caveat stating the nature of the conversion performed and describing the translation process is needed to maintain an accurate lineage of the feature.
  • Result 6: No match at all between the two feature classes in differing Information Communities is possible without a complete loss of information. Such an example would be the FACC feature class "GB040 Launch Pad", which has no counterpart in the S-57 Object Catalogue.
  • These are six general cases of feature class translation results. To achieve true interoperability between the feature class definition schemes of two different Information Communities, other more complex considerations apply. Though the semantic intent of a feature may be consistent across two feature class definition schemes, the content of their supporting attribution schemes may be divergent from one another to a great degree. Even when the attribute sets for two comparable feature classes in different schemes match to a large degree, there are still opportunities for loss of attribute information. A clear example of this is the lack of support for undefined or indeterminate values in many of the coded value attributes in the S-57 Object Catalogue. Most of these attribute sets contain the same values and corresponding meanings as their counterparts in the FACC, with the exception of a code to indicate that the attribute was not, or could not be, measured. For instances where attribute information is undefined or indeterminate, a significant loss of information is incurred when translating from the FACC to the S-57 Object Catalogue in such cases.

    Other measurement-based considerations will have an impact on the semantic interoperability of feature class and attribute definition schemes. Even if the feature class and attribute definitions match exactly across two different schemes, the feature's method of capture, as well as its method of representation or portrayal, may be different, thus introducing uncertainty or loss of information into the translation. The former issue is concerned with the rules for feature data capture for input into a digital geodatabase. Guidelines that specify, for example, whether an analyst should delineate an area feature by its perimeter, by its corner points, or by some other criteria, will ultimately have an effect on the quality of the feature data integration between two Information Communities, especially if their expectations for that integration are different. The latter issue relates to rules for feature data representation in the geodatabase used by the Information Community. Guidelines that specify whether a feature will be stored as a point, line, or area, or some other complex geometric construct, also have an effect on the quality of feature data integration between the two Information Communities. If an area feature from one Information Community is translated for use in another Information Community that only supports a point representation for that feature, the detailed areal extent information is not recoverable in the target Information Community without some kind of lineage metadata to accompany the exchange.

    These are all significant challenges to achieving semantic interoperability between the feature class and attribute definition schemes used by a diverse collection of geospatial Information Communities. OGIS mechanisms must address how these challenges are met to ensure efficient and accurate mediation of distributed, heterogeneous geospatial metadata and feature class schema.

    A Federal Agency Example

    The structure of the federal bureaucracy provides many examples of individual Information Communities which use geodata and geoprocessing technology to help them fulfill their mission. In the following discussion we examine an example developed using the Bureau of Land Management as a sample Information Community.

    The BLM's Automated Land & Minerals Record System (ALMRS) project is transitioning a pen & paper based records system into a database & GIS system. The ALMRS land status subprogram tracks ownership, management, rights, and limitations (known as segregations) on U.S. land. The spatial descriptions include a grab-bag of metes & bounds, township/range/section rectangles, and modern surveys. "Land Status" could be considered an IC. Other Information Communities that might be interested in land status data could be mineral exploration companies, real estate developers, or other government land agencies (like the U.S. Forest Service). We will limit our discussion of this hypothetical Information Community to describing the attribute and geodata they use, and what information other Information Communities might want to know about it.

    Attribute Information That Might Populate the ALMRS Semantic Translator

    Land status tracks the surface & subsurface rights and segregations on land. This data includes vast domains of possible values. The allowed domains would be good candidates for the Semantic Translator. For example, some of the 112 possible rights are "CALCIUM", "CALICHE", "CINNABAR", "CLAY", "COAL", "OIL & GAS", "PUMICE", "SALMON", "TIMBER", "TIMBER IN PERPETUITY", etc. Some surface segregations are "CLOSED TO AGRICULTURAL LAWS", "CLOSED - DESERT LAND ENTRY"; subsurface segregations include "ASPHALTIC MATERIAL", "GEOTHERMAL", "SUBJECT TO SEC. 24 OF FPA". "Surface Management Agency" has about 2400 possible values.

    The system must track the dates of acquisitions, releases, terminations, withdrawals, etc. to derive the cumulative rights, etc. To derive the cumulative values for land status, the system follows complex rules to determine which rights supersede others, and which areas they apply to. These rules might be good candidates for the Semantic Translator.

    Also, given the dynamic nature of ownership, rights, etc., warnings that the data gets re-derived on a regular basis should be in the Semantic Translator. Other Information Communities accessing this information need to know how and when to update their information.

    If rights or segregations on nominally located parcels (explained below) cannot be resolved through the rules, they are flagged as "COMPLEX". Any default values in the domain like "COMPLEX" should be explained in the Semantic Translator, that is, "This is not a real world value, but an indication of ...".

    Spatial Information that Might Populate the ALMRS Semantic Translator

    The initial system can only resolve rectangular "PLSS" locations, also known as township/range/section/aliquot descriptions. Assumptions about this geodata should be included in the Semantic Translator. For example, the smallest aliquot division possible determines the maximum resolution of the data.

    Non-rectangular data, like special surveys, must be "nominally" located to one or more rectangular PLSS parcels. Assumptions about these nominal locations should be stated in the Semantic Translator. For example, special mineral surveys are nominally located at a maximum resolution of a 1/4,1/4 section. Therefore, the actual location of a survey cannot be known with any greater precision.

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    6.5 Conclusions

    Semantic differences between geographic features and attributes used by different groups impose the most difficult obstacle to data sharing, as the foregoing examples and discussion indicate. The OGIS Information Communities Model offers a path toward overcoming this obstacle. New technologies restructure our behavior, for better or worse, often without much planning. Members of the OGIS Project are thinking carefully about the system of human communications they propose to rationalize and leverage with technology. Information Community technology, if developed and delivered correctly, will structure both data access and collaborative metadata conformance efforts. Both kinds of activity will become easier, more efficient, and more comprehensive. Metadata conformance achieved through configuring Semantic Translators will be particularly rewarding because the painstaking efforts of the participants will be highly leveraged, in terms of future benefit deriving from the automated systems, by the configured software mechanisms.

    In the next section we look at the OGIS Services Architecture, in which OGIS distributed computing services critical to Information Communities are described.

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    7. The OGIS Services Model

    The OGIS Services Model is a set of interoperable component services that developers will use to build applications that have a geospatial component: The range of geographic applications encompasses all problems for which one wishes to model the Earth and then use the model to solve these problems. As developers implement products with OGIS interfaces, interoperable geographic applications will be composed of components from the OGIS Services Model and other supporting and compatible information services. OGIS Services Model components provide:

    The specific types developed for the OGIS Services Model do not comprise a model of real world facts (an essential model) but a specification model of software intended to provide the above capabilities.

    The following sections outline each of these component types and their relationships. In a specific implementation the processes and/or code objects of the system being implemented may not match in a one-to-one manner with the components outlined in the specification model. Finally, the reader should note that the specific capabilities listed are not meant to be a complete and final set. Services may be extended, added, or removed in the course of OGIS Project specification writing.

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    7.1 Features, Their Schema, and The Feature Registry

    In Chapter 5 we discussed the relationship between features and their constituent property sets. Basically, features are made up of their properties, which can be geometric, semantic, or descriptive in nature. Figure 7-1 shows the relationship between an instance (called "aFeature") of the Feature type and a schema instance (called "Feature") of the type FeatureSchema that describes the property set for that feature type. Note that every element of the schema defines an element in the property set and that there is a one-to-one correspondence. If a property set does not conform exactly to a schema, then the feature is not properly that feature type. All features that conform exactly are of the feature type defined by the schema. Features provide access to their schema. Feature schemas define all aspects of a feature type including geometric components (including associated spatial temporal reference systems), semantic components, and metadata (an element of a Base Information Definition). Lastly, note that the Feature instance has an identity (this topic is covered in section 5.4.1). In the figure we have chosen to depict the value of this identity as a string that refers to an Internet node ("nsdi.gov"), a database name ("BigDB"), and a hexadecimal number that uniquely identifies this particular feature within the storage system being utilized (0x0c5f).

    Figure 7-1 Features, Schemas, and the Feature Registry

    Feature schemas are stored in a registry (called a Feature Registry), so that they can be shared and reused. This aspect is especially important in the context of an Information Community, where the definitions of features have been agreed to as a component of standardization for interoperability. The entries in the Feature Registry contain a name and a schema. Although not shown in the diagram, feature registries are factories for features. This means that, once a feature schema has been selected and an input set of properties that correspond to the elements of the schema are provided, the registry can be used to create an instance of a feature.

    Although not shown, a single FeatureSchema can be defined in many registries and many registries can exist in one Information Community. If multiple feature registries exist within an Information Community there should be some notion of an authoritative feature registry. As a matter of fact, this issue comes up over and over again with all indexing structures within an Information Community. Thus registries are referenced within an Information Community (see section 7.2 for the details).

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    7.2 Access to Geodata via the Catalog

    In Chapter 5 we pointed out that features are the central concept of the Open Geodata Model. The granularity of access to data under database management control is at the level of features. Somehow the geodata in databases must be exposed (as a Feature) to OGIS-based applications or OGIS-based application environments. This is achieved by means of the indexing system known as a Catalog. Another catalog may also be referenced as a Catalog Entry (an element of a catalog). Catalogs can be thought of as "buckets" that contain features and other buckets. An abstract example, is depicted in Figure 7-2. Notice that, for every level of depth, the items enclosed in ellipses are catalog entries for the level above.

    Figure 7-2 An abstract Catalog example

    Each catalog entry has a schema that conforms, at least partially, to a schema that is maintained as part of the catalog (this schema is called the Catalog Entry Schema). That is, each catalog entry must have properties that match the schema elements contained in the Catalog Entry Schema as part of its property set. An application developer (or a user of a Base Information Definition configuration tool) configures a Catalog Entry Schema to refer to whatever information is considered necessary by the application developer or by the Information Community or Communities in which the data is being shared. The Catalog Entry Schema can be thought to contain the metadata required for the contained catalogs and features to conform to a Catalog. In section 7.9 we will show how this schema can be used to build valid catalog queries.

    Figure 7-3 provides an example of the above. The Catalog named "superFeatureCatalog" has the CatalogEntrySchema called "FeatureCatalog". "FeatureCatalog" defines a schema having two elements (we don't really care what they are for this example). The Catalog called "superFeatureCatalog" has two catalog entries, a feature called "aFeature" and a catalog called "subCatalog".

    "aFeature" has a schema (called "Feature") that has two elements that correspond to "FeatureCatalog" and a property set that has elements exactly matching the elements defined in "Feature" and thus has two elements that also match "FeatureCatalog". "aFeature" is said to conform to the catalog entry schema of "superFeatureCatalog" because it has elements that completely correspond to "FeatureCatalog". "subCatalog" has exactly the same explanation with differing names. Notice that in both cases the property sets of the contained entries must conform to the catalog entry schema of the containing catalog.

    Figure 7-3 Relationships between Catalogs, Catalog Entry Schemas, and Catalog Entries.

    Many catalogs can exist within an application context and/or information community. Using catalogs, application developers can control which features are exposed within an Information Community. Catalogs are created by a factory method of a Catalog Registry (exactly analogous to the Feature Registry capability). Figure 7-4 depicts the situation using the relevant parts of Figure 7-3.

    Figure 7-4 Catalog Registry

    Here we see that a catalog registry contains named entries, each of which points to a valid catalog entry schema. Providing the registry factory method the name of a catalog entry schema, a catalog schema, and a set of properties that exactly matches the catalog schema will result in successful Catalog creation. Once the catalog has been created it may be inserted into an appropriate containing catalog (as long as its catalog schema conforms to the catalog entry schema of the containing catalog). Features created using the feature registry factory method can also be inserted into a containing catalog (as long as the features schema conform to the catalog entry schema.

    Here it is appropriate to deal with the issue of authoritative indexing mentioned in section 7.1. Registries (of all kinds) are referenced by catalogs (as alluded to in Figure 7-4, so that they can be accessed by interacting with a catalog.

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    7.3 Master Catalogs and Bootstrapping

    Up to this point we have seen two registries (one for feature schemas and one for catalog entry schemas) and the catalog. In application use these entities must be locatable and, if more than one exists, they must either be tied to some authoritative source or made accessible via some other mechanism. As we mentioned in the last paragraph, the registries are locatable via the catalogs. However, we still have the issue of authoritative catalogs and bootstrapping. This issue must be dealt with at the level of implementation, but here we provide two options:

    One or both of these mechanisms might become standardized within the OGIS Project at some point in the future, but not until considerable application experience can be brought to bear on the issues.

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    7.4 Spatial/Temporal Reference System Access and Translation

    Spatial/temporal reference systems used within an information community must be made accessible for transformations and for understanding geometries. Transformations will be needed when:

    This requirement means that spatial/temporal reference systems must be defined in a common way (the exact methodology is beyond the scope of this book, but can be found in OGIS) and these definitions must be made available via some mechanism. This section defines the mechanism used to register spatial/temporal reference systems and the mechanism to transform features to create otherwise identical features with different spatial/temporal reference systems.

    7.4.1 Spatial/Temporal Reference System Access

    Figure 7-5 details the linkages between a spatial/temporal reference system, its definition, and a registry used to index spatial/temporal reference system definitions. The spatial/temporal reference system registry is referenced (like feature and catalog registries) in catalogs containing features that use the spatial/temporal reference system definitions contained in the registry.

    Figure 7-5 Spatial/Temporal Reference System Access

    7.4.2 Spatial/Temporal Reference System Transformation

    In addition to providing a common registry of spatial/temporal reference systems, OGIS must also make available, as a common resource, a collection of transforms that generate features with different spatial/temporal reference systems. Figure 7-6 depicts both the mechanism used to access spatial/temporal reference system transformations and an example transformation. The four types in the center of the figure detail the access mechanism. A Reference System Transform Registry stores all of the parts needed to transform a feature with geometric properties associated to a spatial/temporal reference system (in this case "LocalFrame") to a feature with exactly the same properties except the geometric properties that have been transformed and now are associated with the new spatial/temporal reference system (in this case "WGS84"). Notice that the transform registry maintains a reference to the transformation "code" (called a Reference System Transform), the source and target spatial/temporal reference systems, and a transform schema. The transform schema defines the properties of a transformation (usually transformation parameters), providing a flexible mechanism for implementation of more capable and generic transforms.

    When a transform is invoked, it is provided with a set of transformation properties (that match the Transform Schema) and a source Feature (in our case "aFeature). The transform can then construct the new feature (in this case "aNewFeature" at lower left).

    Figure 7-6 Spatial/Temporal Reference System Translation

    Spatial/temporal reference system transformation registries are referenced by spatial/temporal reference system registries themselves. When inter- or intra- Information Community transformations are needed, then the appropriate transforms should be developed or purchased and installed by entering them in the appropriate spatial/temporal reference system registry (in other words, the registry in the target environment). Transforms that are available in the source environment may also be used.

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    7.5 Semantic Translation

    The Semantic Translator is a set of types providing a mechanism for the translation of features from Information Community to Information Community. Chapter 6 describes the intended use and the need for such a mechanism. The types presented here, like all the others presented so far in this book, represent a consensus reached after considerable discussion in the Technical Committee. But unlike geodata modeling and distributed geodata access, automated semantic translation has not been part of the mainstream academic or commercial geoprocessing research agenda. So, we expect early implementation of the types and associations described below to lead to more discussion and perhaps to a revision of the model.

    7.5.1 Overview

    As we stated in Chapter 6, an Information Community may agree to work with a partner Information Community in the manual construction of one or more semantic mappings which will be used to facilitate one way or two way data sharing between itself and its partner Information Community. This cooperative task is mainly a matter of coming to agreement on the precise meaning of each Feature, schema element by schema element, and capturing the results of this work as a series of Feature Translators.

    Within an integrating Information Community, a registry of Feature Translators (called the SemanticTranslatorRegistry) will exist, as shown in Figure 7-7. A SemanticTranslatorRegistry contains a set of definitions for translating Features from a source Information Community Catalog to a target Information Community Catalog. Thus a SemanticTranslatorRegistry contains many SemanticTranslatorDefinitions that map features in a source Catalog to features in a target Catalog by defining all of the necessary FeatureTranslators. FeatureTranslators provide the ability to translate a single Feature from a source FeatureSchema to a target FeatureSchema.

    Figure 7-7 Semantic Translator

    Figure 7-8 is an OMT specification model diagram which illustrates the relationships among SemanticTranslatorRegistry, SemanticTranslatorDefinitions, and FeatureTranslators.

    Figure 7-8 Semantic Translator and Registry Model

    7.5.2 The FeatureTranslator Type

    The FeatureTranslator type encapsulates the functionality required to translate a Feature from one feature schema to another feature schema. The interface includes:

    The translateFeature() operation must contain all of the necessary rules, calculations, mappings, or whatever else it takes to perform the translation. In this way very simple to very complex translations can be handled via the same interface. It is anticipated that additional (value-added) services will be implemented in products that automate, to a greater or lesser degree, the job of developing FeatureTranslators.

    7.5.3 The SemanticTranslatorRegistry

    A semantic translator registry is a repository for named semantic translator definitions. It functions as a clearinghouse for feature translation capabilities in Information Communities that are, in whole or in part, an integration of other Information Communities. A SemanticTranslatorDefinition contains a set of FeatureTranslations for the integration of features from a single source Information Community to a target Information Community.

    The SemanticTranslatorRegistry supports the following functionality:

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    7.6 Operation Registry

    7.6.1 Overview

    Now that we have addressed automated geodata access, spatial/temporal reference system conversion, and semantic translation, we look directly at how OGIS supports operations on geographical data in distributed environments. Distributed geoprocessing operations can be part of a Database Management System (DBMS) or separate from a DBMS. For instance:

    End users, however, do not want to know where an operation is defined. In addition, a geoprocessing operation might call another without knowing whether it has been built into the system or defined by the user.

    Most object models make the definition of operations part of the definition of objects. But that makes it hard to define operations intended to work on existing data. Those models also choose an operation based on the class of one object, but many geoprocessing operations depend on the classes of more than one object. For example, intersecting a circle and a line is different from intersecting a polyhedron and a line, which is different from intersecting a point and a fractal cloud, which is different from intersecting an arc and a chain. In the worse case, introducing a new kind of geometry requires defining how it intersects with each of the other geometries. But in a distributed system, it is not even possible to know all the kinds of geometries, since there might be many new kinds of geometries being defined simultaneously.

    To solve these problems, OGIS supports an object model in which operations are not necessarily part of objects and can be selected based on the classes of more than one object. This is a multi-operation object model, and it uses a type system similar to that developed by Chambers and Leavens (see Bibliography). Concepts

    Types in OGIS are defined as a set of related operations. They are different from the implementation of an object, which is a Class.

    Each type has a unique identifier. (It is a subtype of Identity). A type can be the subtype of one or more other types, but the subtype graph is acyclic: that is, there can be no circular or recursive associations from child back to parent. The union of two types is their common supertype; the intersection of two types is their common subtype.

    The type system defines a set of interfaces. Each interface has a set of operations and attributes. Attributes are basically simplified definitions of operations. An attribute definition is a simplified way to define a pair of accessor operationsone that allows read access and one that allows write access to the attribute value. A read-only attribute defines an operation that is a read-only accessor for the attribute value. Each operation has a name, a return type, and a sequence of parameter types. Operations do not have to have unique names.

    For example, the following interface (expressed in OMG IDL, for convenience):

    interface ExampleInterface {
            attribute float att_a;
            readonly attribute boolean is_ok;
            long count_item(in AnotherInterface input);

    defines four operations. These operations are (also expressed in OMG IDL):

    float get_att_a(in ExampleInterface o);
    void set_att_a(in ExampleInterface o, in float a);
    boolean get_is_ok(in ExampleInterface o);
    long count_item(in ExampleInterface o, in AnotherInterface input);

    There are usually many operations with the same name. A operation t is compatible with a operation s if s and t have different names, if the return type of t is a subtype of the return type of s, or if one of the parameter types of s is not a subtype of the corresponding parameter type in t. A set of operations is compatible if each operation in the set is compatible with every other operation in the set.

    For example, suppose Geometry has subtype Curve, which has subtype LineString. The following set of operations would be compatible:

    Geometry intersect(in Geometry a, in Geometry b)
    LineString intersect(in LineString a, in LineString b)
    LineString intersect(in LineString a, in Curve b)
    Curve intersect(in Curve a, in Curve b)

    Each program has a current set of operations. An operation call is legal only if there is a operation in the current set with the same name and whose parameter types are supertypes of the types of the actual arguments of the operation call.

    The implementation of these operations and the connection between operations and their implementations are discussed in the section on the OperationRegistry.

    Operations can be defined in DBMS's, be predefined by the implementation of OGIS, or be defined by the developer. But there must be a central OperationRegistry that can decide which operation to select for a particular set of arguments. The OperationRegistry not only registers operations, it also registers implementations. It invokes services and can define new services or add to existing services.

    The OperationRegistry must ensure the consistency of its information. To this purpose, the OperationRegistry must certify that all registered operations are compatible.

    The OperationRegistry is responsible for invoking operations. The OperationRegistry services an invocation's request in the following manner: Operation Registry Model

    This section outlines the use model for the Operation Registry. Figure 7-9 depicts the components of interest in a single Operation Registry interaction. They are the client of the interaction, the Operation Registry itself, and an object that implements the operation to be performed. The use model has four steps (some of which may be repeated many times in a single interaction). They are:

    1. Query the Operation Registry to return the operation(s) that match some client-defined criteria. The criteria will initially be limited to the name of the operation the client wishes to invoke.
    2. The Operation Registry returns matches to the client for selection.
    3. Once the client has selected the operation, the client calls the create_operation interface on the operation Registry to create an Operation object.
    4. The Operation Registry creates the Operation object and returns the reference to the client for subsequent request and response interaction.

    The client may then:

    Figure 7-9 Use model for Operation Registry interaction

    Figure 7-10 illustrates the relationships among Operation, OperationSchema, OperationEntry, and OperationRegistry.

    Figure 7-10 Operation and Operation Registry Specification Model

    7.6.2 The OperationRegistry Type

    An operation registry is a repository for named operation definitions (each called an OperationEntry). It functions as a clearinghouse for the operations available within Information Communities. A OperationEntry defines a set of attributes that are shared in common with all instances (like class variables in the C++ sense). These attributes include:

    The OperationRegistry type supports the following functionality:

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    7.7 Type Registry

    7.7.1 Overview

    As with any distributed object system, metadata about types must be accessible and updatable so that developers can create applications which portably interoperate with each other and with core components. Here we refer the reader to the CORBA specification (specifically, the Interface Repository) and to the COM specification and the Type Library portion of OLE Automation.

    The following types are meant to abstract these mechanisms so that implementation independence can be gained at the OGIS level.

    The Type Registry enables users to install new types in the shared environment, and it enables other users to see what new types have been implemented and to create an instance of a new type that has become available in the shared environment. We anticipate that users will be able to buy components from different vendors and install them into an environment where they can be immediately useful without recompilation. The Type Registry is very much like the operation registry, except that the only new operation enabled when a TypeFactory instance is created is the creation of an instance of that type. Once the capability to create a new type becomes available through the Type Registry, then the Operation Registry support discussed in the previous section will also be available.

    This section outlines the use model for the Type Registry. Figure 7-11 depicts the components of interest in a single Type Registry interaction. These components are: the client of the interaction, the Type Registry itself, and an object that implements a factory for the needed Type. The use model has four steps (some of which may be repeated many times in a single interaction). They are:

    1. Query the Type Registry to return the type factory that matches some client-defined criteria. The criteria are initially limited to the name of the type the client seeks to create.
    2. The Type Registry returns the matching Type Entry to the client.
    3. The client calls the create_type_factory interface on the Type Registry to create a TypeFactory object.
    4. The Type Registry creates the Type Factory object and
    5. returns the reference to the client.
    6. The client calls the create_instance interface of the TypeFactory object to create an instance of the type.
    7. The TypeFactory object creates the instance and
    8. returns the reference to the client.

    Figure 7-11 Use model Type Registry interaction

    Figure 7-12 illustrates the relationships among TypeFactory, TypeSchema, TypeEntry, and TypeRegistry.

    Figure 7-12 TypeFactory and Type Registry Specification Model

    7.7.2 The TypeFactory Type

    The TypeFactory type supports the following functionality:

    7.7.3 The TypeRegistry Type

    A type registry is a repository for named type definitions (each called a TypeEntry). It functions as a clearinghouse for the types available within Information Communities. A TypeEntry defines a set of attributes that are shared in common with all instances (like class variables in the C++ sense). The attributes include:

    The TypeRegistry supports the following functionality:

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    7.8 Traders

    A trading service, or Trader, provides a mechanism for locating items of interest, such as Catalogs and their contents. Traders mediate import and export offers between Catalogs and their potential users. Catalog descriptions are "exported" to a Trader which can then mediate between the Catalogs and potential Catalog clients (who want to "import" a reference to a Catalog that suits their requirements).

    Like Catalogs and Features, Traders also have associations with PropertySets that function to describe the properties of the import and export offers within the Trader. A TraderSchema imposes the required structure on the PropertySets used by the Trader to establish a template for the Trader. In other words, the Properties contained in the TraderSchema must exist in a PropertySet that supports the Trader. A Trader also has a query facility which allows the selection of services based on the service type and/or other properties of the services.

    The notion of trading services is an area of widespread interest within and among other Information Technology disciplines (in other words, outside of the OGIS Project). Standards for trading services are emerging from ongoing work by other standards organizations. The OGIS project will leverage these standards when appropriate and will attempt to influence the requirements for trading services through liaison with other standards bodies. We introduce the concept here, because we feel it is a valuable future capability, especially in terms of inter-Information Community "discovery:"

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    7.9 Queries

    Queries in the OGIS environment are supplied by a set of types collectively known as the OGIS Query Service. The query service types are the basic mechanisms for retrieving information from feature collections and catalogs.

    The OGIS Query Service is modeled closely after the OMG Query Service (see Object Management group 1995, OMG Tech Comm Doc 95-1-1, Object Query Service Specification). The primary types are:

    Figure 7-14 depicts the Collection and Iterator types and their relationship. The types are defined in detail below (see sections 7.9.1 and 7.9.2, respectively). A Collection may have any number of Iterators that are actively iterating over the collection, but these iterators depend on the existence of the Collection. In fact, if the collection changes, then the iterator becomes invalid.

    Figure 7-14 The Collection Specification ModelSpecification Model

    7.9.1 The Collection Type

    The Collection type defines operations to:

    The element type of a collection can be any type of value: primitive, constructed, and object data types. Note that a given Collection may have multiple iterators defined on it.

    7.9.2 The Iterator Type

    The Collection type can create an iterator called an Iterator. The Iterator type defines operations to:

    7.9.3 The QueryEvaluator Type

    The QueryEvaluator type defines operations for evaluating queries. It also returns the type(s) of query language(s) it supports, including its default query language. The QueryEvaluator manages an implicit collection of persistent objects. The query evaluator has an operation that returns a list of the data types which will be returned by the query. In other words, if the query were to be executed and an iterator were to be created on the resulting collection, then an invocation of the next operation on the iterator would return a sequence of values. The describe operation tells what actual data types would appear in the sequence for that particular query. The evaluate operation evaluates the query, performs the required query processing, and returns a collection containing the results.

    7.9.4 The Query Type

    The Query type defines four operations that can be performed on an instance of a query:

    The Query type can identify the QueryManager which created it.

    7.9.5 The QueryManager Type

    The QueryManager is a more powerful form of QueryEvaluator which enables creation and direct interaction with a Query object.

    The QueryManager is a Query factory that provides for defining the input query. If the query language type is not specified, a default query language is assumed. If the query language type is specified it must be supported by the QueryManager, otherwise an error is flagged. If the query syntax or semantics are incorrect or if the input parameter list is incorrect, an error is reported. The parameters to the Query factory provide the values for any dynamic variables appearing in the query string. If these values are provided for the factory then any values provided for the prepare and execute operations of Query type will be ignored.

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    7.10 The Query Model

    We have explained in detail the components of the Query Service, but we need to describe the model so that a better understanding of the actual process of executing a query is understood. OGIS supports two models, a simple one-step operation and a more complex, but powerful multi-step operation.

    Figure 7-15 depicts both of these operations. For the simple process only steps 0 and 12 are needed. For the complex model, the client is assumed to take the role of the Query Evaluator in the figure and steps 1 through 11 are needed. The process is as follows:

    1. A client tasked to perform a query prepares an appropriate query string in a selected query language and invokes the evaluate operation on the Query Evaluator.
    2. The QueryEvaluator then finds a QueryManager that can handle the query language of the query string passed by the client and invokes the create_query operation. If the complex process is in use, then the client has performed these tasks.
    3. The QueryManager creates a Query object.
    4. The QueryManager returns the Query object handle to the QueryEvaluator (or to the query client if the complex process is in use).
    5. The QueryEvaluator (or query client) invokes the prepare operation to ready the Query. Note that this step is only necessary if the invoking entity has not provided parameters for queries containing variables.
    6. The Query object returns control to the QueryEvaluator (a query client).
    7. The QueryEvaluator (or query client) invokes the execute operation on the Query object, specifying details of how to perform the query (such as whether the query is to be executed synchronously or asynchronously, etc.).
    8. The Query object immediately returns control to the QueryEvaluator (a query client) if the query is asynchronous, else it completes the query execution and returns control at that point.
    9. If the query was performed asynchronously, the QueryEvaluator can check the status of the query during execution to determine whether the query is complete or has failed for some reason. If the query is performed synchronously, then the status of the query can be checked using the get_status operation.
    10. The get_status operation will return either an indication of success or an indication of failure and the reason(s) for failure.
    11. If the query has been successfully executed, then the get_result operation can be invoked.
    12. The get_result operation returns the Collection of results to the QueryEvaluatorQueryEvaluator (or the query client).
    13. If the simple model is used the QueryEvaluatorQueryEvaluator returns either the Collection of results or a failure indication along with the reason(s) for failure.

    Figure 7-15 Query Model

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    7.11 Query Service Issues

    7.11.1 Query Languages

    In general, the properties of a feature can be classified into four categories:

    It is necessary to be able to use any or all of these categories in query conditions.

    The only requirement that is unique for OGIS for a query language is:

    Any operation on any type will be available from within the query language.

    The spatial extent of a feature is normally a Geometry. For example, query conditions on the spatial extent will typically need to use the following Boolean operations as defined from the types for coordinate geometry and geometry:

    Query conditions will also need to use the BufferZone(Geometry, Distance) operation, or perhaps an extended set of Boolean operations:

    The temporal extent of a feature is normally a TemporalObject, which can be defined as a finite set of non-intersecting time intervals (where an interval may be unbounded on either or both ends). Query conditions on the temporal extent will typically need to use boolean intersects, contains and equals operations.

    Query conditions on structured attributes will typically need to use the types of operations which are supported by such well known query languages as SQL or OQL.

    Query conditions on textual attributes will typically involve "appears_in(keyword, text)" or "keyword_of(keyword, text)" operations of the sort which lie at the heart of full-text indexing/searching systems.

    Thus, a query language for querying feature collections should support spatial, temporal and textual object types, as well structured attributes, and should support the operations described above for these extended data types, as well as the usual operations for structured attributes.

    The language OQL defined in the Object Database Standard (ODMG-93) already supports these extended data types and operations, in the sense that these data types and operations can be defined in the object definition language ODL and then used in OQL queries. However, in order to achieve acceptable performance when querying over large feature sets, it will be necessary to have data managers which have built-in knowledge of these data types and operations and which provide support for them, in the sense of providing efficient indexing mechanisms for executing queries involving them, rather than just having these data types and operations defined at the application level.

    In its present form the language SQL does not support these extended data types and operations.

    Thus, there is a need for extensions to SQL and OQL which explicitly include these extended data types and operations, and there is a need for enhanced relational and object data managers which support them. Hopefully, these features will soon be included in the standards for SQL and OQL.

    In the meantime, implementations of query services may have to support only limited forms of such extended data types and operations and may have to impose restrictions on the ways in which they can appear in query expressions. Such limited forms of support are preferable to no support at all. One example of such a limited form of support would be a query manager which supports only rectangular extents, all in a common spatial/temporal reference system, and which requires that the "where" clause of a query must be a conjunction of terms, each of which contains only one type of condition (spatial, temporal, attribute, or textual).

    7.11.2 Query Schema

    Ideally, the object types which appear in the query schema for a feature collection would match the data types of the properties in the features of the feature collection. However, this is not always possible. For example, the data types of the properties in a feature may be arbitrarily complex, and SQL supports only a fairly limited set of data types. One can represent very complex data types with the relational data model, but the result is a representation, not an exact match of data types.

    The query schema for each feature collection should be accessible through the query manager itself, using the standard query facilities. In other words, the query schema should be made available as data, using a standard representation for schema information. This is common practice for relational databases, and in fact the SQL standard specifies how the schema information should be represented in relational tables. A similar facility should be provided for an OQL implementation.

    Copyright 1996, Open GIS Consortium, Inc.