OLAP performs multidimensional analysis of business data and provides the capability for complex calculations, trend analysis, and sophisticated data modeling.

4. COST AND COMPETITIVE ADVANTAGES

5. • Is an umbrella term that combines architectures, tools, databases, analytical tools,
applications and methodologies.
• The major objective is to enable interactive access (sometimes in real-time) to
data, to enable manipulation of data, and to give business managers and analysts
the ability to conduct appropriate analysis.
• By analyzing historical and current data, situations, and performances, decision
makers get valuable insights that enable them to make more informed and better
decisions.
• The process of BI is based on the transformation of data to information, then to
decisions, and finally actions.
• A BI system has four major components: A Data Warehouse, with its source data;
Business Analytics, a collection of tools for manipulating , mining, and analyzing the
data in the data warehouse; Business Performance Management (BPM), for
monitoring and analyzing performance; and a User Interface (e.g., dashboard) .

7. • OLAP is an acronym for Online Analytical Processing. OLAP performs
multidimensional analysis of business data and provides the capability for complex
calculations, trend analysis, and sophisticated data modeling.
• The term OLAP was created as slight
modification of the transaction
database term OLTP (Online
Transaction Processing).
• The term OLAP was introduced in a
PAPER entitled “Providing On-
Line Analytical Processing to User
Analysts,” by Dr. E. F. Codd, the
acknowledged father of the relational
database model, published in 1993.
(cited: 1000+)

8. Query and
Analysis
Component
Data
Integration
Component
Data
Warehouse
Operational
DBs
External
Sources
Internal
Sources
OLAP
Server
Meta
data
OLAP
Reports
Client
Tools
Data
Mining

9. # DATA WAREHOUSE (OLAP) OPERATIONAL DATABASE (OLTP)
1 Involves historical processing of information. Involves day-to-day processing.
2 OLAP systems are used by knowledge workers
such as executives, managers and analysts.
OLTP systems are used by clerks, DBAs, or
database professionals.
3 Useful in analyzing the business. Useful in running the business.
4 It focuses on Information out. It focuses on Data in.
5 Based on Star Schema, Snowflake Schema and
Fact Constellation Schema.
Based on Entity Relationship Model.
6 Contains historical data. Contains current data.
7 Provides summarized and consolidated data. Provides primitive and highly detailed data.
8 Provides summarized and multidimensional
view of data.
Provides detailed and flat relational view of
data.
9 Number or users is in hundreds. Number of users is in thousands.
10 Number of records accessed is in millions. Number of records accessed is in tens.
11 Database size is from 100 GB to 1 TB Database size is from 100 MB to 1 GB.
12 Highly flexible. Provides high performance.

10. • RELATIONAL OLAP (ROLAP)
• MULTIDIMENSIONAL OLAP (MOLAP)
• HYBRID OLAP (HOLAP) = (ROLAP+MOLAP)

11. • ROLAP servers are placed between relational back-end server and client front-end
tools.
• To store and manage the warehouse data, the relational OLAP uses relational or
extended-relational DBMS.
• ROLAP includes the following:
• Implementation of aggregation navigation logic
• Optimization for each DBMS back-end
• Additional tools and services
• ROLAP servers are highly scalable.
• ROLAP tools analyze large volumes of data across multiple dimensions.
• ROLAP tools store and analyze highly volatile and changeable data.
ROLAP ARCHITECTURE
ROLAP includes the following components:
• Database server
• ROLAP server
• Front-end tool.

12. ROLAP (ADVANTAGES & DISADVANTAGES)
Advantages:
• ROLAP servers can be easily used with existing RDBMS.
• Data can be stored efficiently, since no zero facts can be stored.
• ROLAP tools do not use pre-calculated data cubes.
• DSS server of micro-strategy adopts the ROLAP approach.
Disadvantages:
• Poor query performance.
• Some limitations of scalability depending on the technology architecture that is
utilized.

13. • MOLAP uses array-based multidimensional storage engines for multidimensional
views of data.
• With multidimensional data stores, the storage utilization may be low if the data
set is sparse.
• Therefore, many MOLAP servers use two levels of data storage representation to
handle dense and sparse data-sets.
• MOLAP tools process information with consistent response time regardless of level
of summarizing or calculations selected.
• MOLAP tools need to avoid many of the complexities of creating a relational
database to store data for analysis.
• MOLAP tools need fastest possible performance.
• MOLAP server adopts two level of storage representation to handle dense and
sparse data sets.
• Denser sub-cubes are identified and stored as array structure.
• Sparse sub-cubes employ compression technology.

14. MOLAP ARCHITECTURE
• MOLAP includes the following components:
• Database server.
• MOLAP server.
• Front-end tool.
MOLAP (ADVANTAGES & DISADVANTAGES)
Advantages:
• MOLAP allows fastest indexing to the pre-computed summarized data.
• Helps the users connected to a network who need to analyze larger, less-defined
data.
• Easier to use, therefore MOLAP is suitable for inexperienced users.
Disadvantages:
• MOLAP are not capable of containing detailed data.
• The storage utilization may be low if the data set is sparse.

15. # ROLAP MOLAP
1 Information retrieval is comparatively
slow.
Information retrieval is fast.
2 Uses relational table. Uses sparse array to store data-sets.
3 ROLAP is best suited for experienced
users.
MOLAP is best suited for inexperienced
users, since it is very easy to use.
4 It may not require space other than
available in the Data warehouse.
Maintains a separate database for data
cubes.
5 DBMS facility is strong. DBMS facility is weak.

16. • Is a combination of both ROLAP and MOLAP.
• It offers higher scalability of ROLAP and faster computation of MOLAP.
• HOLAP servers allows to store the large data volumes of detailed information.
• The aggregations are stored separately in MOLAP store.

17. • OLAP tools enable users to analyze multidimensional data interactively from
multiple perspectives. OLAP consists of four basic analytical operations:
consolidation (roll-up), drill-down, slicing and dicing and pivoting .
• Consolidation involves the aggregation of data that can be accumulated and
computed in one or more dimensions. For example, all sales offices are rolled up to
the sales department or sales division to anticipate sales trends.
• By contrast, the drill-down is a technique that allows users to navigate through the
details. For instance, users can view the sales by individual products that make up a
region’s sales.
• Slicing and dicing is a feature whereby users can take out (slicing) a specific set of
data of the OLAP cube and view (dicing) the slices from different viewpoints.
• Pivoting allows an analyst to rotate the cube in space to see its various faces. For
example, cities could be arranged vertically and products horizontally while viewing
data for a particular quarter.

18. ROLL UP: Move in one dimension from a lower granularity to a higher one
• Store city
• Cities Country
• Product Product Type.
• Quarter Semester
DRILL DOWN:
• Inverse operation
• Move in one dimension from a higher granularity to a lower one.
• City Store
• Country Cities
• Product Type Product.
• Semester Quarter
• Drill-through:
• go back to the original, individual data records

19. ROLL UP AND DRILL DOWN

20. SLICE:
• Selects one particular dimension from a given cube and provides a new sub-
cube.
• Here Slice is performed for the dimension "time" using the criterion time =
"Q1".
• It will form a new sub-cube by selecting one or more dimensions.

21. DICE:
• Dice selects two or more dimensions from a given cube and provides a new
sub-cube.
• The dice operation on the cube based on the following selection criteria
involves three dimensions.
• (location = "Toronto" or "Vancouver")
• (time = "Q1" or "Q2")
• (item =" Mobile" or "Modem")

22. PIVOT:
• Change the dimensions that are “displayed”; select a cross-tab.
• look at the cross-table for product-date
• display cross-table for date-customer
• Change the dimensions that are “displayed”; select a cross-tab.
• look at the cross-table for product-date
• display cross-table for date-customer

23. • Typically, OLAP queries are executed over a separate copy of the working data
• Over data warehouse
• Data warehouse is periodically updated, e.g., overnight
• OLAP queries tolerate such out-of-date gaps
• Why run OLAP queries over data warehouse??
• Warehouse collects and combines data from multiple sources
• Warehouse may organize the data in certain formats to support OLAP
queries
• OLAP queries are complex and touch large amounts of data
• They may lock the database for long periods of time
• Negatively affects all other OLTP transactions

24. Query and
Analysis
Component
Data
Integration
Component
Data
Warehouse
Operational
DBs
External
Sources
Internal
Sources
OLAP
Server
Meta
data
OLAP
Reports
Client
Tools
Data
Mining

25. DATA MINING: Data Mining is a combination of discovering
techniques + prediction techniques
OLAP: Provides you with a very good view of what is happening,
but can not predict what will happen in the future or why it is
happening

30. • Worcester Polytechnic Institute – US, 1865.
• DATA WAREHOUSE AND OLAP (Concepts, Architectures and Solutions) – Robert
Wrembel & Christian Koncilia. 2007
• E. Turban, J. E. Aronson, “Decision Support And Business Intelligence Systems”, 8th
edition
• Ponniah “data warehousing fundamentals for IT professionals”, 2nd Edition
• https://www.google.com/trends
• https://olap.com/olap-definition
• https://en.wikipedia.org/wiki/Business_analytics
• Tutorialspoints.com/dwh
• https://en.wikipedia.org/wiki/OLAP