OLAP & DSS SUPPORT IN DATA WAREHOUSE By - Pooja Sinha Kaushalya Bakde

Data Warehouse “A data warehouse is a subject-oriented, integrated, time- variant, and nonvolatile collection of data in support of management’s decision-making process.”—W. H. Inmon A decision support system (DSS) is a computer program application that analyzes business data and presents it so that users can make business decisions more easily. A Data Warehouse is used for On-Line-Analytical- Processing: “Class of tools that enables the user to gain insight into data through interactive access to a wide variety of possible views of the information”

Understanding the term Data Warehousing Subject Oriented: Data that gives information about a particular subject instead of about a company's ongoing operations. Integrated: Data that is gathered into the data warehouse from a variety of sources and merged into a coherent whole. Time-variant: All data in the data warehouse is identified with a particular time period. It keeps historical data. Non-volatile Data is stable in a data warehouse. More data is added but data is never removed. This enables management to gain a consistent picture of the business.

Data Warehouse for Decision Support A data base is a collection of data organized by a database management system. A data warehouse is a read-only analytical database used for a decision support system operation. A data warehouse for decision support is often taking data from various platforms, databases, and files as source data. The use of advanced tools and specialized technologies may be necessary in the development of decision support systems, which affects tasks, deliverables, training, and project timelines.

Decision Support System in datawarehouse Information SourcesData Warehouse Server (Tier 1) OLAP Servers (Tier 2) Clients (Tier 3) Operational DB’s Semistructured Sources extract transform load refresh etc. Data Marts Data Warehouse e.g., MOLAP e.g., ROLAP serve OLAP Query/Reporting Data Mining serve

Characteristics Of DSS DSS should give well structured information. DSS attempts to combine the use of models or analytic techniques with traditional data access and retrieval functions DSS specifically focuses on features which make them easy to use by non computer people in an interactive mode DSS emphasizes flexibility and adaptability to accommodate changes in the environment and the decision making approach of the user.

Application Area

 OLAP, Online Analytical Processing, is capable of providing highest level of functionality and support for decision which is linked for analyzing large collections of historical data. The functionality of an OLAP tool is purely based on the existing / current data.  DSS, Decision Support System, helps in taking decisions for top executive professionals. Data accessing, time-series data manipulation of an enterprise’s internal / some times external data is emphasized by DSS. The manipulation is done by tailor made tools that are task specific and operators and general tools for providing additional functionality. OLAP and DSS

Introduction to OLAP OLAP(Online Analytical Processing )is computer processing that enables user to easily & selectively extract & view data from different points of view. OLAP data is stored in multidimensional databases. Present in Tier II in Data Warehouse architecture.

Data warehouse for On Line Analytical Processing (OLAP) features Complex queries that access millions of records. Contains historical data for analysis. Provides summarized and multidimensional view of data. Database size : 100 GB -TB Fast response time for interactive queries. Navigation in & out of details(drill down & roll up, slice & dice or rotation). Ability to perform complicate calculations.

Types Of OLAP Servers 1.Relational OLAP(ROLAP) :- ROLAP servers are placed between relational back-end server and client front-end tools.  Data is stored in tables in relational database or extended-relational database.  They use RDBMs to manage the warehouse data. 2.Multidimensional OLAP(MOLAP) :-  It stores data in an optimized multi- dimensional array rather than relational database.  Fast indexing to pre-computed aggregations. 3.Hybrid OLAP(HOLAP) :- Hybrid OLAP is a combination of both ROLAP and MOLAP. It offers higher scalability of ROLAP and faster computation of MOLAP.  HOLAP servers allow to store large data volumes of detailed information. The aggregations are stored separately in MOLAP store.

The list of OLAP operations: Roll-up Drill-down Slice and dice Pivot (rotate)

Common OLAP Operations 1.Roll-up: Move up the hierarchy  By dimension reduction.  When roll-up is performed, one or more dimensions from the data cube are removed.  E.g. Given total sales by city, we can roll-up to get sales by state or by country.

OLAP Operations 2.Drill-down: Move down the hierarchy  By introducing a new dimension  Lowest level can be the detail records (drill- through)  It navigates the data from less detailed data to highly detailed data.  E.g., Given total sales by state, can drill-down to get total sales by city.

Contd Slice & Dice :- Select and Project on one or more dimensions. The user can view the data from many angles.  The slice operation selects one particular dimension from a given cube  Dice selects two or more dimensions from a given cube and provides a new sub-cube. product customers store customer = “Smith”

4. Pivot(Rotate):-  Changing the dimensions.  It rotates the data axes in view in order to provide an alternative presentation of data Contd...

Applications Of OLAP  Business reporting for sales & Marketing  Management reporting  Financial Service industry (insurance, banks, etc).