Lecture 06 -IIS-OLAP.pptx

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Business Intelligence Technologies
• OLAP and Data Mining differ in what they offer
the user and because of this they are
complementary technologies.
• An environment that includes a data warehouse
(or more commonly one or more data marts)
together with tools such as OLAP and /or data
mining are collectively referred to as Business
Intelligence (BI) technologies.
© Pearson Education Limited 1995, 2005

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Online Analytical Processing (OLAP)
• The dynamic synthesis, analysis, and consolidation of large volumes
of multi-dimensional data, Codd (1993).
• Describes a technology that uses a multi-dimensional view of
aggregate data to provide quick access to strategic information for
the purposes of advanced analysis.

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• Enables users to gain a deeper understanding and knowledge about
various aspects of their corporate data through fast, consistent,
interactive access to a wide variety of possible views of the data.
• Allows users to view corporate data in such a way that it is a better
model of the true dimensionality of the enterprise.

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• Can easily answer ‘who?’ and ‘what?’ questions, however, ability to
answer ‘what if?’ and ‘why?’ type questions distinguishes OLAP
from general-purpose query tools.
• Types of analysis ranges from basic navigation and browsing (slicing
and dicing) to calculations, to more complex analyses such as time
series and complex modeling.

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OLAP Applications
• JIT information is computed data that usually reflects complex
relationships and is often calculated on the fly. Also as data
relationships may not be known in advance, the data model must be
flexible.

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Examples of OLAP applications in various
functional areas

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OLAP Applications
• Although OLAP applications are found in widely divergent functional
areas, they all have the following key features:
• multi-dimensional views of data
• support for complex calculations
• time intelligence

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OLAP Applications - multi-
dimensional views of data
• Core requirement of building a ‘realistic’ business
model.
• Provides basis for analytical processing through
flexible access to corporate data.
• The underlying database design that provides the
multi-dimensional view of data should treat all
dimensions equally.

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OLAP Applications - support for
complex calculations
• Must provide a range of powerful computational methods such as
that required by sales forecasting, which uses trend algorithms such
as moving averages and percentage growth.
• Mechanisms for implementing computational methods should be
clear and non-procedural.

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OLAP Applications – time intelligence
• Key feature of almost any analytical application as performance is
almost always judged over time.
• Time hierarchy is not always used in the same manner as other
hierarchies.
• Concepts such as year-to-date and period-over-period comparisons
should be easily defined.

period-over-period
• The year-over-year growth rate compares a time period,
usually a month or a quarter, against the same time period
last year. It is a more effective way of looking at
performance. If you are doing better than last month, for
example, but worse than this time last year, then you might
be lulled into thinking you were improving when in fact you
were not. The improvement might simply be because you
always do better this time of year. For this reason,
whenever you are looking at a financial or economic
report, make sure you know whether it is being compared
to the last period (quarter, month, week, day) or year-over-
year.
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OLAP Benefits
• Increased productivity of end-users.
• Retention of organizational control over the integrity of corporate
data.
• Reduced query drag and network traffic on OLTP systems or on the
data warehouse.
• Improved potential revenue and profitability.

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Representation of Multi-dimensional
Data
• Example of two-dimensional query.
• What is the total revenue generated by property sales in each city, in each quarter of
2004?’
• Choice of representation is based on types of queries end-user may
ask.
• Compare representation - three-field relational table versus two-
dimensional matrix.

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Multi-dimensional Data as Three-field table versus Two-dimensional
Matrix

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Data
• Example of three-dimensional query.
• ‘What is the total revenue generated by property sales for each type of
property (Flat or House) in each city, in each quarter of 2004?’
• Compare representation - four-field relational table versus three-
dimensional cube.

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Multi-dimensional Data as Four-field Table
versus Three-dimensional Cube

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Data
• Cube represents data as cells in an array.
• Relational table only represents multi-dimensional data in two
dimensions.

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Data
• Use multi-dimensional structures to store data and relationships
between data.
• Multi-dimensional structures are best visualized as cubes of data,
and cubes within cubes of data. Each side of a cube is a dimension.
• A cube can be expanded to include other dimensions.

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Representation of Multi-dimensional Data
• A cube supports matrix arithmetic.
• Multi-dimensional query response time depends on how many cells
have to be added ‘on the fly’.
• As number of dimensions increases, number of the cube’s cells
increases exponentially.

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• However, majority of multi-dimensional queries use summarized,
high-level data.
• Solution is to pre-aggregate (consolidate) all logical subtotals and
totals along all dimensions.

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Data
• Pre-aggregation is valuable, as typical dimensions are hierarchical in
nature.
• (e.g. Time dimension hierarchy - years, quarters, months, weeks, and days)
• Predefined hierarchy allows logical pre-aggregation and, conversely,
allows for a logical ‘drill-down’.

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Data
• Supports common analytical operations
• Consolidation
• Drill-down
• Slicing and dicing

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• Consolidation - aggregation of data such as simple ‘roll-ups’ or
complex expressions involving inter-related data.
• Drill-Down - is the reverse of consolidation and involves displaying
the detailed data that comprises the consolidated data.

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• Slicing and Dicing - (also called pivoting) refers to the ability to look
at the data from different viewpoints.

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• Can store data in a compressed form by dynamically selecting
physical storage organizations and compression techniques that
maximize space utilization.
• Dense data (that is, data that exists for a high percentage of cells)
can be stored separately from sparse data (that is, a significant
percentage of cells are empty).

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• Ability to omit empty or repetitive cells can greatly reduce the size
of the cube and the amount of processing.
• Allows analysis of exceptionally large amounts of data.

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• In summary, pre-aggregation, dimensional hierarchy, and sparse
data management can significantly reduce the size of the cube and
the need to calculate values ‘on-the-fly’.
• Removes need for multi-table joins and provides quick and direct
access to arrays of data, thus significantly speeding up execution of
multi-dimensional queries.

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Codd’s rules for OLAP
• Dynamic sparse matrix handling
• Multi-user support
• Unrestricted cross-dimensional operations
• Intuitive data manipulation
• Flexible reporting
• Unlimited dimensions and aggregation levels

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OLAP Extensions to SQL
• Advantages of SQL include that it is easy to learn, non-procedural,
free-format, DBMS-independent, and that it is a recognized
international standard.
• However, major limitation of SQL is the inability to answer routinely
asked business queries such as computing the percentage change in
values between this month and a year ago or to compute moving
averages, cumulative sums, and other statistical functions.

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OLAP Extensions to SQL
• Answer is ANSI adopted a set of OLAP functions as an extension to
SQL to enable these calculations as well as many others that used to
be impossible or even impractical within SQL.
• IBM and Oracle jointly proposed these extensions early in 1999 and
they now form part of the current SQL standard, namely SQL: 2003.

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OLAP Extensions to SQL - RISQL
• The extensions are collectively referred to as the ‘OLAP package’
and are described as follows:
• Feature T431, ‘Extended Grouping capabilities’
• Feature T611, ‘Extended OLAP operators’

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Extended Grouping Capabilities
• Aggregation is a fundamental part of OLAP. To
improve aggregation capabilities the SQL standard
provides extensions to the GROUP BY clause such as
the ROLLUP and CUBE functions.

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• ROLLUP supports calculations using aggregations
such as SUM, COUNT, MAX, MIN, and AVG at
increasing levels of aggregation, from the most
detailed up to a grand total.
• CUBE is similar to ROLLUP, enabling a single
statement to calculate all possible combinations of
aggregations. CUBE can generate the information
needed in cross-tabulation reports with a single
query.

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• ROLLUP and CUBE extensions specify exactly the
groupings of interest in the GROUP BY clause and
produces a single result set that is equivalent to a
UNION ALL of differently grouped rows.

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• ROLLUP Extension to GROUP BY
• enables a SELECT statement to calculate multiple levels of
subtotals across a specified group of dimensions. ROLLUP
appears in the GROUP BY clause in a SELECT statement
using the following format:
SELECT ... GROUP BY ROLLUP(columnList)

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• ROLLUP creates subtotals that roll up from the most
detailed level to a grand total, following a column list
specified in the ROLLUP clause.
• ROLLUP first calculates the standard aggregate values
specified in the GROUP BY clause and then creates
progressively higher level subtotals, moving from right to
left through the column list until finally completing with
a grand total.

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• ROLLUP creates subtotals at n + 1 levels, where n is the
number of grouping columns. For instance, if a query
specifies ROLLUP on grouping columns of propertyType,
yearMonth, and city (n = 3), the result set will include
rows at 4 aggregation levels.

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Example - Using the ROLLUP Group
Function
• Show the totals for sales of flats or houses by
branch offices located in Aberdeen, Edinburgh, or
Glasgow for the months of September and October
of 2004.

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Function
SELECT propertyType, yearMonth, city, SUM(saleAmount) AS sales
FROM Branch, PropertyFor Sale, PropertySale
WHERE Branch.branchNo = PropertySale.branchNo
AND PropertyForSale.propertyNo = PropertySale.propertyNo
AND PropertySale.yearMonth IN ('2004-08', '2004-09')
AND Branch.city IN (‘Aberdeen’, ‘Edinburgh’, ‘Glasgow’)
GROUP BY ROLLUP(propertyType, yearMonth, city);

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Function

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• CUBE Extension to GROUP BY
• CUBE takes a specified set of grouping columns and
creates subtotals for all of the possible combinations.
CUBE appears in the GROUP BY clause in a SELECT
statement using the following format:
SELECT ... GROUP BY CUBE(columnList)

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• CUBE generates all the subtotals that could be
calculated for a data cube with the specified
dimensions.
• CUBE can be used in any situation requiring cross-
tabular reports. The data needed for cross-tabular
reports can be generated with a single SELECT using
CUBE. Like ROLLUP, CUBE can be helpful in generating
summary tables.

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• CUBE is typically most suitable in queries that
use columns from multiple dimensions rather
than columns representing different levels of a
single dimension.

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Example - Using the CUBE Group
Function
• Show all possible subtotals for sales of
properties by branches offices in Aberdeen,
Edinburgh, and Glasgow for the months of
September and October of 2004.

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Function
SELECT propertyType, yearMonth, city, SUM(saleAmount) AS
sales
FROM Branch, PropertyFor Sale, PropertySale
AND PropertyForSale.propertyNo =
PropertySale.propertyNo
AND PropertySale.yearMonth IN ('2004-08', '2004-09')
AND Branch.city IN (‘Aberdeen’, ‘Edinburgh’, ‘Glasgow’)
GROUP BY CUBE(propertyType, yearMonth, city);

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Function

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Elementary OLAP Operators
• Supports a variety of operations such as rankings
and window calculations.
• Ranking functions include cumulative
distributions, percent rank, and N-tiles.
• Windowing allows the calculation of cumulative
and moving aggregations using functions such as
SUM, AVG, MIN, and COUNT.

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• Ranking Functions
• Computes the rank of a record compared to other
records in the dataset based on the values of a set of
measures. There are various types of ranking
functions, including RANK and DENSE_RANK. The
syntax for each ranking function is:
RANK( ) OVER (ORDER BY columnList)
DENSE_RANK( ) OVER (ORDER BY columnList)

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• The difference between RANK and DENSE_RANK
is that DENSE_RANK leaves no gaps in the
sequential ranking sequence when there are ties
for a ranking.

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Example - Using the RANK and
DENSE_RANK Functions
• Rank the total sales of properties for branch
offices in Edinburgh.
SELECT branchNo, SUM(saleAmount) AS sales,
RANK() OVER (ORDER BY SUM(saleAmount)) DESC AS ranking,
DENSE_RANK() OVER (ORDER BY SUM(saleAmount)) DESC AS
dense_ranking
FROM Branch, PropertySale
AND Branch.city = ‘Edinburgh’
GROUP BY(branchNo);

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Example - Using the RANK and DENSE_RANK
Functions

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• Supports a variety of operations such as rankings
and window calculations.
• Ranking functions include cumulative
distributions, percent rank, and N-tiles.
• Windowing allows the calculation of cumulative
and moving aggregations using functions such as
SUM, AVG, MIN, and COUNT.

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• Windowing Calculations
• Can be used to compute cumulative, moving, and
centered aggregates. They return a value for each
row in the table, which depends on other rows in the
corresponding window.

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• Windowing Calculations
• Can be used to compute cumulative, moving, and
centered aggregates. They return a value for each
row in the table, which depends on other rows in the
corresponding window.
• These aggregate functions provide access to more
than one row of a table without a self-join and can be
used only in the SELECT and ORDER BY clauses of the
query.

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Example - Using Windowing Calculations
• Show the monthly figures and three-month
moving averages and sums for property sales at
branch office B003 for the first six months of
2004.

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SELECT yearMonth, SUM(saleAmount) AS monthlySales,
AVG(SUM(saleAmount))
OVER (ORDER BY yearMonth, ROWS 2 PRECEDING) AS 3-
month moving avg,
SUM(SUM(salesAmount)) OVER (ORDER BY yearMonth ROWS
2 PRECEDING)
AS 3-month moving sum
FROM PropertySale
WHERE branchNo = ‘B003’
AND yearMonth BETWEEN ('2004-01' AND '2004-06’)
GROUP BY yearMonth
ORDER BY yearMonth;

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