Online Analytical Processing (OLAP) is a technology that allows users to analyze data from a variety of perspectives, such as by time, product, or customer. OLAP systems are typically used for business intelligence and decision-making.
There are three main types of OLAP systems:
·
Relational OLAP (ROLAP): ROLAP
systems store data in a relational database, which is a standard database
format. ROLAP systems are typically faster than other types of OLAP systems,
but they can be more difficult to use.
·
Multidimensional OLAP (MOLAP):
MOLAP systems store data in a multidimensional array, which is a data structure
that is optimized for OLAP queries. MOLAP systems are typically slower than
ROLAP systems, but they are easier to use.
·
Hybrid OLAP (HOLAP): HOLAP
systems combine the features of ROLAP and MOLAP. HOLAP systems are typically
faster than MOLAP systems, but they are not as easy to use as ROLAP systems.
Here are some examples of OLAP queries:
1.11 Data Preprocessing:
1.11.1 Data Integration:
It combines datafrom multiple sources into a coherent data store, as in data warehousing. These sourcesmay include multiple databases, data cubes, or flat files.
The data integration systems are formally defined as triple<G,S,M>
Where G: The global schema
S:Heterogeneous source of schemas
M: Mapping between the queries of source and global schema
1.11 Data Preprocessing:
1.11.1 Data
Integration:
It combines datafrom multiple sources into a coherent
data store, as in data warehousing. These sourcesmay include multiple
databases, data cubes, or flat files.
The data integration systems are
formally defined as triple<G,S,M>
Where G: The global schema
S:Heterogeneous
source of schemas
M:
Mapping between the queries of source and global schema
1.11.2 Issues
in Data integration:
1. Schema integration and object matching:
How can the data analyst or the computer be sure that
customer id in one database and customer number in another reference to the
same attribute.
2. Redundancy:
An attribute (such as annual revenue, forinstance)
may be redundant if it can be derived from another attribute or set
ofattributes. Inconsistencies in attribute or dimension naming can also cause
redundanciesin the resulting data set.
3. detection and resolution of datavalue
conflicts:
For the same real-world entity,
attribute values fromdifferent sources may differ.
1.11.3 Data
Transformation:
In data transformation, the data are transformed or
consolidated into forms appropriatefor mining.
Data transformation can involve the
following:
Smoothing,
which works to remove noise from the data. Such techniques includebinning,
regression, and clustering.
Aggregation,
where summary or aggregation operations are applied to the data. For example,
the daily sales data may be aggregated so as to compute monthly and annualtotal
amounts. This step is typically used in constructing a data cube for analysis
of the data at multiple granularities.:
1. Schema integration and object matching:
How can the data analyst or the computer be sure that customer id in one database and customer number in another reference to the same attribute.
2. Redundancy: An attribute (such as annual revenue, forinstance) may be redundant if it can be derived from another attribute or set ofattributes. Inconsistencies in attribute or dimension naming can also cause redundanciesin the resulting data set. detection and resolution of datavalue conflicts: For the same real-world entity, attribute values fromdifferent sources may differ.
Data Transformation:
In data transformation, the data are transformed or consolidated into forms appropriatefor mining.
Data transformation can involve the following:
Smoothing, which works to remove noise from the data. Such techniques includebinning, regression, and clustering.
Aggregation, where summary or aggregation operations are applied to the data. For example, the daily sales data may be aggregated so as to compute monthly and annualtotal amounts. This step is typically used in constructing a data cube for analysis of the data at multiple granularities.