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1. Overview of Database Management system (DBMS)
Definition of DBMS:
A collection of computer programs that allow storage, modification, and extraction of information
that reside in a database.
DBMS Functionality:
1) Security: controlling access to database to protect database
2) Integrity: ensuring that data is accurate or collect and consistence, minimizing
redundancy of record.
3) Concurrency control: allow multi-user to access/operate on shared database
simultaneously.
4) Reliability: Rollback database (ability to recover a previous database if the database
becomes corrupted)
5) Independence from technology: applications insulated from changes in storage
structures and access technology
6) Scalability: Ability to handle increased database.
Evolution of DBMS
File system -> Network DBMS -> Relational DBMS -> Object-Oriented DBMS -> Objectrelational DBMS
File system: File is unstructured collection of records containing logically related data. Different
application manages data different ways.
Network DBMS:
Relational DBMS: DBMS based on relational model. Relational model organizes data in table
format and define relationships between tables.
OODBMS: DMBS that support user a defined data type as a object
ORDBMS: Relational DBMS that supports user defined data type. Combination of RDBMS and
OODBMS
Spatial DBMS:
Software that can 1) work with an underlying DBMS such as OODBMS and ORDBMS, 2)
supports spatial data models (vector, raster), spatial ADTs and query language that can handle
spatial ADTs, and 3) support spatial indexing, algorithms for spatial operations, query and its
optimization
ADT: Abstract Data type is user defined data-type that consists of built in data type such
as integer, characters etc.
2. Difference between GIS and SDBMS
GIS: software that provides high level spatial analysis and visualization of geographic data.
SDBMS: computer programs that handle very large amounts of spatial data and use the
traditional DBMS functionality.
GIS access the data from a SDMBS.
3. SDBMS architecture
SD is interface between the spatial application (GIS) and DBMS
Three layers structure: interface to spatial application (GIS etc.), Core, and interface to
DBMS. Core includes spatial taxonomy, data model, and query.
4. Overview of SDBM
Spatial taxonomy: the descriptions /models for organizing space. topological, network,
directional, Euclidean
Data model (conceptual):
Object model to represent non amorphous (distinct) spatial entity
Field model to represent amorphous or continuous characteristics
4.2 & 3 Query language and processing
Single scan query: a record in the relation is scanned at most one time.
For SDB, spatial -range query or window query (if query range is rectangle)
Multi-scan query: a record in a table is scanned more than one time. e.g. join query requiring to
join more than two tables and process the query. For SDB, spatial-join query
Join vs. spatial join: non spatial join use the same type of attribute as joining attributes,
while joining attribute for spatial join is spatial and can be of different type such as point
and polygon.
Filter-refine process: two step process, first is to approximate the solution to query (by using
methods such as minimum boundary rectangles, easy to compute intersection between two
rectangles), second step is to compare candidates that are left after first step process, using exact
geometry
4.4 File organization and indices
DBMS utilizes secondary disk (access speed from main memory is very slow) because of large
dataset, and tries to minimize input/output time between main memory to disk. That is why it uses
indices. On the other hand, algorithm for GIS analysis tries to reduce computational time and
assumes data resides in main memory.
The index is numerical value and ordered.
B-tree index: hierarchical collection of nodes, each of which specifies the range of index. Good
for traditional database (one dimensional database)
R-tree index: index designed for spatial data, modified from B-tree. Hierarchical collection of
rectangle nodes that specify their location
The spatial data however is difficult to order because ordering multi dimensional data such as
spatial data is not natural. Cause loss of spatial proximity
4.5 Query optimization
Ordering query operations so that efficient strategy of each operation is attained. This is based on
a given database schema.