So we do not use the methodologies. Therefore, in ADA, when we search the candidates, we use Pre-Dictionaries and AS Diagrams. By AS Diagrams, we search the first candidates roughly, reasoning by the similarity of the contents (or items) in AS Diagrams. And then we reason them referring pre-Dictionaries in detail [Aggregation of ACTIVITIES]. By the same way, we also generalize ACTIVITIES [Generalization of ACTIVITIES]. On this aggregation and generalization, we aim to the following;
To extract patterns of ACTIVITIES
To reduce Ases, which we have to clarify attributes.
To complement the missing ACTIVITIES and ASes, which should be extracted in former steps.
To recognize the rough weigh of each aggregated/generalized ACTIVITIES.
After aggregation and generalization, we integrate classes, which are belonged in the aggregated/generalized ACTIVITIES. At this time, we also integrate AS Diagram. And then we extract attributes of each integrated AS [Extraction of Attributes], and make a dictionary to define ACTIVITIES, classes and attributes of ASes [Making of Dictionary].

(4) Clarification of AS for common SI-DB: The last step of ADA is clarifying AS for a common SI-DB. On former steps, we aim to define Views, especially Ass. In this sense, each AS is related with an ACTIVITY. However it is independent for each other. Therefore, on this step, we combine and integrate those ASes into an AS for a common SI-DB. On this step, basically, we do the same aggregation and generalization on the “aggregation and generalization of ACTIVITIES”. That is aggregating and generalizing classes belonged in all of ASes referring Pre-Dictionaries and AS Diagrams. As the result, we obtain an integrated AS. This is an AS for a common SI-DB.


Figure 6. Integrated AS for common SI-DB

4. Conclusion and Future Works
In this study, we developed a structure and a procedure of ADA. At the present, by ADA we are designing a prototype of National Spatial Data Infrastructure (NSDI), which is a common SI-DB as an infrastructure. Through this examination, we could make ADA strictly. The result of ADA is a maximum data set of SI-DB. That is, we can say, a data structure of an ideal SI-DB and, it is not to set then in an actual SI-DB. So we have to evaluate them by some evaluation methods. Therefore, after developing ADA, we also develop a behavioral model to simulate the change of behavior according to provided information, and a cost model to estimate product cost of information.

Reference

• Ivan S., Richard B., Tom R., 1994. Cooperative Systems Design. The Computer Journal, 37,5, 357-366.
• Toyoaki N., Testsuo T., Takashi K., Hideaki T., 1998. Management of Engineering Knowledge, Asakura syuppan (in Japanese)
• Jones P., 1988. Practical Guide to Structured System Design Second Edition. Prenetice-hall Inc.
• Sully P., 1993. Modeling the World with Objects. Prentice Hall International Ltd.
• Yourdon E., 1994. Object-Oriented System Design An Integrated Approach. Prentice-hall Inc.
• Mitsuru I., 1997. Knowledge Representation and High Speed Reasoning. Maruzen Syuppan (in Japanese)
• Tom d., 1997. Structure Analysis and system Specification. Prenetice-Hall Inc.
• Ivar J., Maria E., Agneta J., 1994. The Object-Oriented Systems. Prenetice-Hall Inc.
• Ryoichi M., Masanobu K., 1999. An Ontology of Faults – Articulation and Organization. Journal of Japanese Society for Artificial Intelligence, 14,5.
• Mitsuru I., Satoshi K., Ryoichi M., Masanobu K., 1999 Fault Diagnosis Based on Ontological consideration of Faults – Exhaustive Fault Hypotheses Generation. Journal of Japanese Society for Artificial Intelligence, 14,5.
• Percles L., Vassilios K., 1995. System Requirements Engineering. McGraw-Hall
• Mitsuru I., Haruki U., 1997 Knowledge Representation and Use. Ohmsha
• Kaname I., 1997. Approach for System Thinking. Seibundo Printing.
• Haruo s., 1995. Fuzzy Theory. Kyoritsu Syuppan.