(3) Actor: An Actor is the subject of an ACTIVITY. When we consider the ACTIVITY “to go somewhere”, we require different information between “to go from here to a station” and “from Japan to Hong Kong”. Therefore an Actor defines this difference of intention of each ACTIVITY.

(4) Application Schema (AS): Now we have to consider what an Actor see to do an ACTIVITY, and how s/he understands them. We call them an Application Schema (AS). For example, in the AS for the ACTVITY “to go from here to a station”, there are a present location, a destination, rod networks and so on. These kinds of features and feature relationships are called classes of the AS. In the example, the present location or the destination has a name, an address and so on as attributes. These are called attributes of the class.

(5) View: Each ACTIVITY has an Actor and an AS. We call a set of these three a View. Now we imaging making an urban plan. An AS of prefecture level planners and that of city are different. For example, the former deals with a prefecture as a unit. On the other hand, the later, a city. As regards road networks, the former consider national roads and prefectural roads as set, but the later, them and roads maintained by a city. Therefore, considering the difference of ASes, we should provide a data set correspond to each Actor. Especially in this sense, a View is one of the most important concepts.

(6) Dictionary: A dictionary is definition of all of information on each View. In a dictionary, it is also define that information on each aggregated ACTIVITY (refer to 3.3.3). Among Dictionaries, we define a pre-dictionary. In a Pre-Dictionary, we describe only the definition of each Actor, ACTIVITY and class of the AS. For the contents in the pre-dictionary, we make a Thesaurus.

3.3 Procedure of ADA

Figure 1. Procedure of ADA

ADA is composed of 4 steps: “Extraction and Division of ACTIVITIES”, “Extraction of AS”, “Aggregation and Generalization of ACTIVITES” and “Clarification of AS for common SI-DB”. Now we explain each step one by one.

(1)Extraction and Division of ACTIVITIES: On the first of ADA, we extract ACTIVITIES and divide them into sub-ACTIVITIES.


Figure 2. Extraction and Division of ACTIVITIES

At first we define Actors intended in the common SI-DB we are designing [Extraction of Actors]. When extraction Actors, it is helpful to refer the post name of present business system. Next is extraction of ACTIVITIES [Extraction of ACTIVITIES]. We consider what each Actor does and what is the purpose. This purpose is exactly and ACTIVITY. In the beginning, we should extract very rough ACTIVITIES. And we must not extract all of ACTIVITIES at once, because, when extracting ACTIVITIES for other Actors, we have some time to aware. At the last of this step, we divide each ACTIVITY into some sub-ACTIVITIES [division of ACTIVITES]. This division is done until to reach the required degree of detail of purpose. In this hierarchical structure among ACTIVITIIES, it is defined sub-ACTIVITIES are contained as a part of an ACTIVITY.

(2) Extraction of AS: In this step, we extract the AS of each ACTIVITY. In the former step, we obtained the target ACTIVITIES of SI. For the Result, i.e. all of ACTIVITY, we extract each AS. In this step, we are required only to extract the classes of an AS [making of Pre-Dictionary]. And then we represent the AS graphically [Illustration of AS]. We call this figure AS Diagram.


Figure 3. Actor, ACT & AS


Figure 4. AS Diagram

An AS Diagram has two functions. One is to support the matching between the AS held by actual Actors and that represented by analysts. Especially, we consider the effects will appear in the representation of topological relationships among features. Another is the role as a thesaurus. Since this AS Role has great responsibility with the next Step, we explain later. ACT

(3) Aggregation and Generalization of ACTIVITES:


Figure 5. Aggregation and Generalization of ACTIVITES

On the third, we aggregate and Integrate ACTIVITIES. We find the candidates of ACTIVITIES, which can be aggregated into one. In this super-ACT process, just we refer the class of AS to judge their similarity. Usually doing this kind of reasoning, we will use natural language understanding methodologies. These methodologies are studied for the sentences or contexts reasoning. However, there is none Figure 5 Aggregation & Generalization of ACT of sentences or contexts for ASes.