4.12 Parallel versus Distributed Architectures
There are two main types of multiprocessor system architectures thaare commonplace:
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Shared memory (tightly coupled) architecture. Multiplprocessors share secondary (disk) storage and also share primary memory.
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Shared disk (loosely coupled) architecture. Multiple processors share secondary(disk) storage but each has their own primary memory. o These architectures enable processors to communicate without the overhead of exchanging messages over a network.4 Database management systems developed using the above types of architectures are termed parallel database management systems rather than DDBMSs, since they utilize parallel processor technology.
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Another type of multiprocessor architecture is called shared nothing architecture . In this architecture, every processor has its own primary and secondary (disk)memory, no common memory exists, and the processors communicate over a high speed interconnection network (bus or switch).
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Although the shared nothing architecture resembles a distributed database computing environment, major differences exist in the mode of operation.
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In shared nothing multiprocessor systems, there is symmetry and homogeneity of nodes; this is not true of the distributed database environment where heterogeneity of hardware and operating system at each node is very common.
 Shared nothing architecture is also considered as an environment for parallel databases.
General Architecture of Pure Distributed Databases
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In this section we discuss both the logical and component architectural models of a DDB. In Figure 25.4, which describes the generic schema architecture of a
DDB, the enterprise is presented with a consistent, unified view showing the logical structure of underlying data across all nodes.
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This view is represented by the global conceptual schema (GCS), which provides network transparency .
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To accommodate potential heterogeneity in the DDB, each node is shown as having its own local internal schema (LIS) based on physical organization details

at that particular site. The logical organization of data at each site is specified by the local conceptual schema (LCS).
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The global query compiler references the global conceptual schema from the global system catalog to verify and impose defined constraints.
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The global query optimizer references both global and local conceptual schemas and generates optimized local queries from global queries. It evaluates all candidate strategies using a cost function that estimates cost based on response time (CPU,

Some different database system architectures. (a) Shared nothing architecture.(b) A networked architecture with a centralized database at one of the sites. (c)A truly distributed database architecture.
4.13 SPATIAL DATABASE
Spatial Data Types and Models
This section briefly describes the common data types and models for storing spatial data.
Spatial data comes in three basic forms. These forms have become a de facto standard due to their wide use in commercial systems.
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Map Data 26 includes various geographic or spatial features of objects in a map, such as an object’s shape and the location of the object within the map. The three basic types of features are points, lines, and polygons (or areas).
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Points are used to represent spatial characteristics of objects whose locations correspond to a single 2-d coordinate ( x , y , or longitude/latitude) in the scale of a particular application. Depending on the scale, some examples of point objects could be buildings, cellular towers, or stationary vehicles. Moving
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Projective operators. Projective operators, such as convex hull , are used toexpress predicates about the concavity/convexity of objects as well as otherspatial relations (for example, being inside the concavity of a given object).
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Metric operators. Metric operators provide a more specific description ofthe object’s geometry. They are used to measure some global properties ofsingle objects (such as the area, relative size of an object’s parts, compactness,and symmetry), and to measure the relative position of different objects interms of distance and direction. Examples include length (arc) and distance(point, point).
Spatial Queries.
Spatial queries are requests for spatial data that require the use of spatial operations. The following categories illustrate three typical types of spatial queries:

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Range query. Finds the objects of a particular type that are within a given spatial area or within a particular distance from a given location. (For example, find all hospitals within the Metropolitan Atlanta city area, or find allambulances within five miles of an accident location.)
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Nearest neighbor query. Finds an object of a particular type that is closest to a given location. (For example, find the police car that is closest to the location of crime.)
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Spatial joins or overlays. Typically joins the objects of two types based on some spatial condition, such as theobjects intersecting or overlapping spatially or being within a certain distance of one another. (For example, find all townships located on a major highway between