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Bédard, Y., J. Pageau & C. Caron, 1992, Spatial Data Modeling: The Modul-R Formalism and CASE Technology, ISPRS Symposium, 10p., August 1-14, Washington, United-States

SPATIAL DATA MODELING: THE MODUL-R FORMALISM AND CASE TECHNOLOGY Dr. Yvan Bédard, QLS Johanne Pageau, B.Sc. Centre de recherche en géomatique Faculté de Foresterie et de Géomatique Université Laval Québec, CANADA Tél.: (418) 656-2530 Fax.: (418) 656-7411 Claude Caron, QLS, M.Sc. Département de Génie Rural École Polytechnique Fédérale de Lausanne Lausanne, SUISSE Tél.: (41-21) 693-2755 Fax.: (41-21) 693-2727 PURPOSE: The modeling of spatial data is a crucial step for the effective implementation of Geographic Information Systems (GIS). A data model determines what can be done easily, with difficulty, or not at all. This paper presents a new modeling formalism, called MODUL-R, which was developed specifically for spatial data. It extends the traditional Entity-Relationship model and relies on the use of CASE technology (Computer-Assisted System Engineering) to facilitate the link between the design and programming phases. Practical examples will illustrate the use and benefits of MODUL-R as well as the capabilities of the developed CASE software. This should allow the reader to understand the process from conceptual modeling to application programming. KEY WORDS: Conceptual data modeling, CASE, Extended Entity-Relationship, Conceptual formalisms, Spatial data, Geographic Information Systems. 1. INTRODUCTION Different data models are created during the development of spatial information systems (SIS) to define and to understand the content of the database. The first model defines the concepts that we want to manage in the database. The second model translates these concepts into a form meaningful for a given software (example, DBMS or GIS). The third model is a perfect mapping of the second model but in a computer language. For these three models, developers, programmers and users communicate their vision of the future database using formal but restricted languages (called formalisms).

In recent years, research has been done in SIS to make data modeling more efficient and semantically complete. However, some deficiencies remain, for example a lack of objectorientation, technology dependence, inefficient time modeling and complexity resulting of a large amount of entities and relationships. Unlike existing formalisms, the MODUL-R formalism proposes a unified approach to solve these specific problems. It integrates several facultative modules, geared toward specific problems, which remain compatible at the semantic, notation and grammar levels. The MODUL-R formalism is made to be used with CASE tools to simplify creation and management of models. This also facilitates the automatic translation between the different models created during the SIS development. This paper covers three parts. The first presents the basic concepts of modeling and its use in the development of SIS applications. The second presents needed improvements to model spatial data. The third part introduces the MODUL-R formalism that integrates solutions to these problems. Finally, the usefulness of CASE tools is presented with specific considerations to modeling with MODUL-R. 2. THE CONCEPT OF MODELING “A model is an abstraction of something for the purpose and understanding it before building it. Because a model omits nonessential details, it is easier to manipulate than the reality.” (Rumbaugh and al, 1991) When we talk, when we write or when we want to understand a phenomenon, we create models. Models are created to understand reality, to communicate our understanding of it and to remember it. For example, if we write the word “house”, everybody can visualize a certain type of building with windows, doors, where people live, etc. We don’t have to mention the dimensions, the color, the number of stories, the number of windows and the number of doors of the house to be understood. The modeling process represents the simplification of a reality for a specific goal by extracting only its essential elements. This modeling process always is for a specific goal, so, we can create several models from different points of view. To create models, we use a set of symbols, their signification and the rules to use these symbols. These three components correspond to words, meaning and grammar; they make a language. A formal language, restricted for a specific goal of modeling, is called a formalism. 3. DIFFERENT FORMALISMS FOR DIFFERENT NEEDS. Complexity arises during the development of large databases. First, it is difficult to understand all the aspects of the reality which are of interest to the users. Secondly, it is difficult to describe this understanding in a nonambiguous form. Thirdly, communication is difficult between users, designers and programmers because of their different backgrounds. Therefore, it is important to develop and use specific languages powerful enough to achieve the goal of each step of system development but simple enough to be understood by everyone involved in these steps. It has become commonly accepted to use three levels of models to achieve this communication: conceptual, logical and physical. First, the concept conceptual data model contains a representation of the reality as it is defined by the users. This model is technology-independent, thus not optimized for computing performance, and serves as a communication tool between the designers and the users.

Secondly, the logical data model is created to define how the conceptual model will be implemented on a specific software or a family of software. Therefore, it is technologydependent and it allows the optimization of the data structure. At this level, people use the relational, CODASYL or the newest object-oriented formalisms. Thirdly, the physical data model is the computer code of the application. So, it entirely depends on the selected software tool. This paper focuses only on the conceptual data model (Figure 1). At the moment, solutions to create conceptual models for spatial phenomena remain painful. This result in difficulties described in the following section. 4. IMPROVEMENTS NEEDED FOR BETTER CONCEPTUAL SPATIAL DATA MODELING. The Entity-Relationship (E/R) formalism (Chen, 1976) is the most widely used formalism for conceptual data modeling. Several authors have developed their own dialects of the E/R formalism (Martin and McClure, 1988; Tabourier 1986; Collongues and al., 1986) and also many adaptations have been made for specific needs (real-time, aggregation, integrity constraints). A new standard has been proposed (Spencer and al 1990) which includes new constructs from the object oriented world. Some adaptations have also been made for better modeling of spatial phenomenon by Bédard and Paquette (1989). Four important areas of improvement were identified for adapting the E/R formalism to spatial databases: 1) The adaptations of Bédard and Paquette (1989) introduced the use of spatial pictograms which overcome some problems. These extensions are used in some companies and government agencies. However, to become more efficient, more research was needed. For example, missing elements include: - Alternative geometries for a spatial entity: for example an entity type BUILDING represented by a point or a surface depending on its dimensions; - Some aspects of spatial aggregation of different geometries: for example, an entity type WATER SYSTEM represented by an aggregation of lines (water lines) and isolated points (fire hydrants); - Three dimensional entities. 2) Today’s spatial databases tend to include more temporal data. With the traditional E/R formalism, the temporal reference is difficult to represent. In data modeling, we often manage time with DATE attributes for the entities that we want to manage the time (Tabourier, 1986). Tasker (1987) and Langran (1989) propose the integration of sub-models that manage time into conceptual data models. However, these solutions increase the size of conceptual data models and decrease ease of reading and manipulating the model. Current solutions don’t allow the use of alternative temporalities for a relation or an entity (example, instantaneous or with a duration). 3) In SIS, databases are often complex (Ossher, 1987) because they contain a lot of entity types, types of relations and of attributes. This situation causes problems for the reading, the understanding, and the manipulation of the model. This becomes worst when integrating temporal elements. Certain solutions exist for the simplification of the data model, one being the generalization of entities by the creation of super entities (Bédard and Paquette, 1989).

Another solution is thematic grouping and the use of levels of details at the conceptual level (Bédard, 1990). However, certain problems remain: - Relations cannot be regrouped or simplified; - The drawing, the manipulation and the manual validation are difficult when the model is big and complex. 4) Existing formalisms used at the conceptual level are translatable at the logical level into either relational, hierarchical or network models. However, it is more difficult to create conceptual data models easily translatable into object-oriented modeling at the logical level. This difficulty depends of the separation of processes modeling and data modeling. It also depends of the lack of semantic richness of current conceptual formalisms (Meyer, 1988) (Feutchwanger, 1989). There exists other families of formalisms than the E/R family. However, similar limitations were found. Up to now, no formalism integrated solutions for the management of spatial reference modeling, temporal reference modeling, complex modeling and the combined modeling of data and processes Although, some formalisms resolve certain parts of these problems, none integrates all of these aspects. Therefore, MODUL-R has been designed to be a flexible solution for such an evolving situation where specific problems are solved by specific facultative modules. These modules can be added when required and are compatible with each other at the semantic, graphical notation and grammar levels. For the modeling of each project, the model designer will choose different modules. The richness of the model is directly proportional to the number of selected modules while the simplicity of reading is inversely proportional to it. 5. INTRODUCTION TO MODUL-R MOOUL-R is a new formalism being developed by a team of the Geomatics Research Center; version 1.0 was presented in detail by Caron (1991). This first version has been slightly improved and new additions are planned. This paper presents version 1.0. The most important characteristic of this formalism is the integrated use of a modular approach. This approach makes MODUL-R rich and flexible: designers can add or remove modules according to their needs while maintaining consistency. It is an open solution including the four areas of improvement identified in the previous section. A modular approach introduces flexibility to choose among optional semantic components to solve specific modeling problems. It also introduces the choice among possible views of the model. It is to the user of the formalism to choose the appropriate modules for each specific context. An integrated approach allows the designer to select the degree of richness of his model without worrying about the compatibility of concepts, notations and rules between modules. So far, MODUL-R is a data-oriented modeling approach based on the MERISE E/R dialect (Tardieu and al, 1986). MODUL-R adds four different modules grouping selected techniques directed toward the four areas of improvements needed when modeling the spatial reality (Figure 2). MODUL-R techniques come mostly from the new E/R standard (Spencer and al, 1990), from object oriented modeling and from solutions developed at the Geomatics Research Center. 5.1 The basic E/R module.

The E/R module is not split up into sub-modules. It contains the basic semantic components of E/R: entities, relations, cardinalities and attributes. It uses the MERISE dialect for the graphical notation (figure 3a). This module also includes interrelational constraints and functional dependencies (Figure 3b and 3c). 5.2 The Referential module. The referential module allows the reference of entities in space and the reference of entities and relations in time. It is made from spatial and temporal sub-modules. 5.2.1. The spatial reference sub-module. It allows the attachment of geometric entities to spatial entities. The spatial reference of an entity is represented by different spatial pictograms: punctual, linear, polygonal or three-dimensional (Figure 3d). An entity that doesn’t have a spatial pictogram in the conceptual schema won’t have a cartographic representation. In the model, we identify the shape that we choose to manage for each class of objects of the reality (which can be different from its real shape). For example, we can choose to manage road segments with linear or polygonal pictograms depending on the users’ needs. We can also use alternative pictograms for the spatial reference of an entity. This is needed when some occurrences of a spatial entity type are represented by one form and other occurrences of the same spatial entity type are represented by another form. For example, an occurrence of the entity type HOUSE can be represented by a polygon if its area is larger than a specified value or by a point otherwise, but not both (figure 3d). 5.2.2 The temporal reference sub-module. It allows the description of the temporal behavior of entities and relations. The types of existence (continuous or discontinuous) and evolution (instantaneous, gradual) of entities and relations are described as well as the need to manage past, future or/and present states. For the graphical notation of this temporal reference, MODUL-R uses temporal pictograms the same way as spatial pictograms (Figure 3e). 5.3 The aggregation module. The aggregation and the action modules make conceptual modeling more complete and compatible for a translation to an object-oriented model at the logical level. This module allows the use of semantic aggregation of entities and spatial aggregation of geometric entities. 5.3.1 The semantic aggregation sub-module. It allows the creation of complex entities. A semantic aggregation uses an intuitive graphical notation to represent the relation HAS between entities and uses inheritance concepts (partial or complete). This relation has a semantic meaning, not a spatial meaning. For example, an HYDROLOGY NETWORK has LAKES and RIVERS (Figure 3f). 5.3.2. The spatial aggregation sub-module. It allows the representation of an entity having at the same time many types of forms. This is needed when each occurrence of an entity type is represented by a set of geometric entities. For example, each occurrence of the entity type

WATER SYSTEM is represented at the same time by both lines (water lines) and isolated points (fire hydrants) (Figure 3g). 5.4 The action module. Before the object-oriented approach, data and processes used to be modeled separately. The action module goal is to facilitate this link between modeling the data and the processes. MODUL-R creates this link by the inclusion into the conceptual data model of the activities generating processes (Figure 3h). Sub activities and processes are modelized into a data flow diagram (DFD) (Gane and Sarson, 1979) which graphical notation has been slightly changed to be closer to the data model notation (Figure 3i). This module still is at an early stage and improvements will come from future research, specially from object-oriented concepts. 5.5 The Simplification module. This module includes techniques of simplification to resolve the problem of complex models. It is composed of the sub-modules abstraction and generalization. 5.5.1 The abstraction sub-module. It proposes two techniques: the use of five levels of abstraction based on the creation of themes and the use of different views (Figure 3j). The complexity of conceptual data model is managed with five levels of abstraction: summary, global, themes, detailed and integrated dictionary levels. These levels allow the presentation of the model with just a few details or with a lot of details. The use of levels of abstraction is built on thematic groupings of entities. These themes of entities allow the creation of sets of entities and relations with common characteristics. For example, a physical grouping of data, data used by a same users’ group or a same department or data within a same domain of applications. An entity located in the intersection of themes can mean duplication of data or “power struggle” while a relation between themes means possible sharing of data. Also, we tan use different views at the same level of abstraction to focus on certain aspects of the model or of a part of the model. 5.5.2 The generalization sub-module. It introduces a technique of simplification by the creation of super-entities and inheritance. A super-entity is a virtual grouping of entities created to simplify the reading of the model (Figure 3k). It often reflects a better understanding of the users’ reality and is very effective to reduce the size of the model. 6. THE USE OF CASE TOOLS FOR CONCEPTUAL MODELING FOR SIS In the conception of an information system in large organizations, it is advantageous to use structured methodology (Burns and Dennis 1985, Dubreuil 1987). These methodologies guide the designer in the choice of resources, data, processes and implementation strategies. “A methodology provides guidance in completing complex tasks as well as providing a medium for consistent communication. Methodologies are based on theory and have been tested many times, so that they’ve acquired formal rigor” (Gardner, 1991). Structured methodologies incite developers to solve the problems before the final computerization (Bédard, 1989).

Modern methodologies recommend the use of software tools specialized in the conception of information systems: CASE tools (Computer-Assisted System Engineering). CASE tools allow a faster creation and modification of all schemas, diagrams and reports suggested in a methodology. They help to increase the quality of the developed applications, of the documentation and of the maintenance of the system. A CASE tool assists the designer and the programmer in the different stages of the development of an information system. Carma McClure (1988) defines CASE technology the following way: “CASE technology is the automation of step-by-step methodologies for software and systems development from one-step planning to ongoing maintenance; it is designed to automate the drudgery of development and free the developer to solve problems.” A modern CASE is more than just a drawing tool, it stores the meaning of diagrams. “It is the meaning represented by the diagram, rather than its graphic image, which is valuable. A good CASE tool stores that meaning in a computer-processible form” (Martin, 1990). Using CASE tools increases productivity, specially when one tool can import or export results with other CASE tools used in preceding or subsequent steps. 6.1 CASE tools in SIS development. Although modern methodologies and CASE tools may be beneficial to the development of SIS, increased productivity could result from adapted methodologies and CASEs. However, at the moment, some CASE tools specially adapted for the development of SIS are emerging as new ideas (Bédard and Larrivée, 1992; Smyrnew, 1992) while others are already part of GIS tools (Intergraph 1990). At the Geomatics Research Center, seven CASE tools to assist the analyst in different steps of the SIS development are under development (Bédard and Larrivée, 1992). One of these tools is for spatial data modeling. 6.2 A CASE tool for spatial data modeling Designers using a CASE tool for conceptual modeling usually get better results because the tool enforces a more rigorous use of the formalism, including auto checking, and creates complete documentation. Such CASE tool also accelerates the translation to a logical data model and to the programming code. We are working on such a modeling tool specifically designed to support MODUL-R (which has been developed with CASE tools in mind). This tool includes the possibility to choose only the needed components (Figure 4), makes automatic checks as well as the automatic generation of the logical data schema for given GISs. A dynamic prototype of the tool has been developed. In addition to the fill-in forms, it simulates the complete behavior of the software, including the workflow and the results obtained (Figures 4, 5, 6). 7. THE TRANSLATION TO A LOGICAL DATA MODEL Once the conceptual model is completed, the results are translated into a logical data model for a given GIS. Presently, we have developed the algorithm for the translation to an objectoriented GIS (TIGRIS of Intergraph Corp.) and a relational GIS (MGE of Intergraph Corp.).

7.1 The translation to an object-oriented GIS (TIGRIS). In the TIGRIS environment, there is a CASE module called ADMINISTRATOR used to build a logical schema of the spatial database. With this module, the user defines the cartographic entities (called base features) with their geometric form and their descriptive attributes. He also defines non cartographic entities as well as semantic and spatial aggregations (called composite features). For these elements, he defines attributes and relations (called links). Rules have been developed to allow the automatic translation of the MODUL-R conceptual data model to an ADMINISTRATOR schema (Figure 7). Then, TIGRIS automatically translates the logical data model of ADMINISTRATOR into a physical data model (TIGRIS code). 7.2 The translation to a relational GIS. At the moment, we have developed an algorithm to translate the MODUL-R conceptual data model into a relational logical data model. The developed rules translate entities, relationships, attributes, geometric and temporal pictograms, etc. into file tables or specific columns. We are presently working on the translation rules for all geometric aspects. Both TIGRIS and MGE translations contain minimum optimization which must be completed by the programmer. 8. OTHER COMPONENTS OF THE CONCEPTUAL SPATIAL DATA MODELING TOOL Using MODUL-R and CASE technology allows us to have a more efficient workflow from the design of a spatial database to code generation in a GIS. Additional aspects of the developed CASE tool are worth mentioning as they also improve the overall workflow. 8.1 Recovering Information from the data inventory tool. The spatial data modeling tool will have functions to recover data types coming from another CASE tool called PHOENIX. This latter tool helps for the inventory and analysis of spatial objects, attributes, reference systems and other metadata existing on different documents (maps, plans, aerial photographs, satellite images, etc.) (Larrivée and Bédard, 1992). These metadata are imported into the conceptual spatial data modeling tool, This allows a selection or modification of objects and attributes to be kept in the final database. This capability leads to the conceptual data model faster than starting from scratch. 8.2 Generating the data dictionary. The spatial data modeling tool has the possibility to create parts of the data dictionary. Data dictionary usually contain components such as definitions of entities, lists of attributes with definitions, ranges of values, integrity constraints, etc. A dictionary extended for spatial data also contains cartographic attributes that define the exact geometry of entities, an initial symbology, the rules for spatial aggregation and for alternative representations. The CASE tool will automatically fill certain parts of such an extended dictionary.

8.3 Recover information from the source selection/data integration CASE tool. The spatial data modeling tool will also have functions to recover data coming from a source selection/data integration CASE tool under development at the Center. This latter tool uses inventory metadata to identify the best source for each type of data identified in the conceptual model. Several technical criteria are used, as well as cost, delay and human resources information. After the selection process, the information can be sent in the cartographic data dictionary. Then, the dictionary becomes a complete reference for the description of the content of the spatial database, including metadata. 9. CONCLUSION Using modeling formalisms and appropriate CASE tools in an integrated workflow are today recognized as an efficient way to deal with complex databases and rapidly deliver good results. It will become especially useful for SIS developers when specific tools will exist for spatial databases. The research presented in this paper is a contribution in this direction. We have identified needs for a new formalism to design conceptual models for spatial data. We have developed solutions that have been integrated into a new formalism called MODULR. This formalism allows conceptual models to be more complete and adaptable to different contexts in SIS design. We have been working on the conception of a CASE tool adapted to MODUL-R. One of its most important components is the capability to translate conceptual data schema into logical data schema. Other important components are the capability to import metadata from the inventory CASE, the creation of a part of the data dictionary, including geometry, and the use of the results coming from the source selection/data integration CASE tool. All these capabilities should lead to a complete workflow where specific CASE tools will be used for all steps of SIS development, from opportunity assessment to implementation and maintenance. 10. REFERENCES Bédard, Y., 1990. Création d’un nouveau formalisme pour la modélisation conceptuelle de la BNDT. Report, Géomatics research center of Laval University, Sainte-Foy, 33 p. Bédard, Y., 1989. Information Engineering for the development of Spatial Information Systems: A Research Agenda. Proceedings of the 1989 Annual Conference of the URISA, Boston, Massachusetts. pp. 43-53. Bédard, Y. and S. Larrivée, 1992. Ateliers de Génie Logiciel et développement des Systèmes d’information à Référence Spatiale. Paper presented at a seminar of the Geomatics research center, Laval University, 6 February, 16 p. Bédard, Y. and F. Paquette, 1989. Extending Entity/Relationship formalism for spatial information systems. AutoCarto 9 (2-7 April), Baltimore, pp. 816-828. Bédard, Y. and J. Prince, 1989. Information Engineering for the development of Spatial Information Systems. Proceedings of the Canadian conference on GIS, 1989, Ottawa, pp. 417428. Burns, R.N. and AS. Dennis, 1985. Selecting the Appropriate Application Development Methodology. Database, Vol. 17, no. 1, Fall pp. 19-23.

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Tabourier, Y., 1986. De l’autre côté de Merise: Système d’information et modèles d’entreprise. Les éditions de l’organisation. Paris, 241 p. Tasker, D., 1987. An Entity/Relationship View of Time. Proceedings of the Sixth International Conference on Entity/Relationship Approach, New York, pp. 211-221. N.B. The authors want to acknowledge the financial support of Intergraph for this research project. 10. FIGURES

Figure 1: Different needs for modeling in the development of SIS.

Figure 2: Modules of MODUL-R (Caron, 1991).

Figure 3: Graphical notations of MODUL-R (Caron and Bédard, 1992).

Figure 4: The modular approach of the CASE tool for conceptual spatial data modeling.

Figure 5: Example of the user-interface: Creation of an entity with its spatial pictogram.

Figure 6: Example of the user-interface: Creation of a relation.

Figure 7: Extract of an ADMINISTRATOR schema (TIGRIS, Intergraph Corp.).