4. Explain in detail the Parallel & Spatial Data base.

Parallel Data Base:

Architecture of parallel data bases:

Shared-memory multiple CPU

Shared-disk multiple CPU

Shared-nothing multiple CPU

Key elements of parallel DB processing:

·         Speed-up

·         Scale-up

·         Synchronization

·         Locking

Query Parallelism:

·         I/O parallelism

·         Intra-query parallelism

·         Inter-query parallelism

·         Intra-operation parallelism

·         Inter-operation parallelism

Spatial Data Base:

Spatial DB characteristics:

Spatial Data Model:

·         Elements

·         Geometries

·         Layers

Spatial data base queries:

·         Range query

·         Nearest neighbour query or adjacency

·         Spatial joins or overlays

Techniques of Spatial DB Query:

·         R-Tree

·         Quad tree

5. Define Data mining? Briefly explain the different types of knowledge discovered during data mining.

Data Mining:

Data mining refers to the mining or discovery of new information in terms of patterns or rules from vast amount of data.

Types of knowledge discovered during Data mining:

Association Rule:

·         Apriori Algorithm

·         Sampling algorithm

·         Frequent pattern tree algorithm

·         Partition algorithm

·         Classification

·         Clustering

Approaches to other Data mining problems:

·         Discovery of sequential patterns

·         Discovery of patterns in time series

·         Regression

·         Neutral networks

·         Genetic algorithms

Applications of Data Mining:

·         Marketing

·         Finance

·         Manufacturing

·         Health care