Spatial View Materialization Technique by using R-Tree Reconstruction (R-tree 재구성 방법을 이용한 공간 뷰 실체화 기법)
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- The KIPS Transactions:PartD
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- v.8D no.4
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- pp.377-386
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- 2001
In spatial database system, spatial view is supported for efficient access method to spatial database and is managed by materialization and non-materialization technique. In non-materialization technique, repeated execution on the same query makes problems such as the bottle-neck effect of server-side and overloads on a network. In materialization technique, view maintenance technique is very difficult and maintenance cost is too high when the base table has been changed. In this paper, the SVMT (Spatial View Materialization Technique) is proposed by using R-tree re-construction. The SVMT is a technique which constructs a spatial index according to the distribution ratio of objects in spatial view. This ratio is computed by using a SVHR (Spatial View Height in R-tree) and SVOC (Spatial View Object Count). If the ratio is higher than the average, a spatial view is materialized and the R-tree index is re-used. In this case, the root node of this index is exchanged a node which has a MBR (Minimum Boundary Rectangle) value that can contains the whole region of spatial view at a minimum size. Otherwise, a spatial view is materialized and the R-tree is re-constructed. In this technique, the information of spatial view is managed by using a SVIT (Spatial View Information Table) and is stored on the record of this table. The proposed technique increases the speed of response time through fast query processing on a materialized view and eliminates additional costs occurred from repeatable query modification on the same query. With these advantages, it can greatly minimize the network overloads and the bottle-neck effect on the server.
Introduction: Diffusion is process by which an innovation is communicated through certain channel overtime among the members of a social system(Rogers 1983). Bass(1969) suggested the Bass model describing diffusion process. The Bass model assumes potential adopters of innovation are influenced by mass-media and word-of-mouth from communication with previous adopters. Various expansions of the Bass model have been conducted. Some of them proposed a third factor affecting diffusion. Others proposed multinational diffusion model and it stressed interactive effect on diffusion among several countries. We add a spatial factor in the Bass model as a third communication factor. Because of situation where we can not control the interaction between markets, we need to consider that diffusion within certain market can be influenced by diffusion in contiguous market. The process that certain type of retail extends is a result that particular market can be described by the retail life cycle. Diffusion of retail has pattern following three phases of spatial diffusion: adoption of innovation happens in near the diffusion center first, spreads to the vicinity of the diffusing center and then adoption of innovation is completed in peripheral areas in saturation stage. So we expect spatial effect to be important to describe diffusion of domestic discount store. We define a spatial diffusion model using multinational diffusion model and apply it to the diffusion of discount store. Modeling: In this paper, we define a spatial diffusion model and apply it to the diffusion of discount store. To define a spatial diffusion model, we expand learning model(Kumar and Krishnan 2002) and separate diffusion process in diffusion center(market A) from diffusion process in the vicinity of the diffusing center(market B). The proposed spatial diffusion model is shown in equation (1a) and (1b). Equation (1a) is the diffusion process in diffusion center and equation (1b) is one in the vicinity of the diffusing center.