February 23, 2016Data Mining: Concepts and Techniques 1 Data Mining: Concepts and Techniques UNIT...

May 5, 2023Data Mining: Concepts and

Techniques 1

Data Mining: Concepts and Techniques

— UNIT IV & V —

Nitin Sharma

Asstt. Profffesor in Department of CS & IET, Alwar

Books Recommended: M.H. Dunham, “ Data Mining: Introductory &

Advanced Topics” Pearson Education Jiawei Han, Micheline Kamber, “ Data Mining

Concepts & Techniques” Elsevier Sam Anahory, Denniss Murray,” data

warehousing in the Real World: A Practical Guide fro Building Decision Support Systems, “ Pearson Education

Mallach,” Data Warehousing System”, TMH


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UNIT IV: Data Warehousing and OLAP Technology: An Overview

What is a data warehouse? A multi-dimensional data model Data warehouse architecture Data warehouse implementation From data warehousing to data mining


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What is Data Warehouse? Defined in many different ways, but not rigorously.

A decision support database that is maintained separately from the organization’s operational database

Support information processing by providing a solid platform of consolidated, historical data for analysis.

“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

Data warehousing: The process of constructing and using data warehouses


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Data Warehouse—Subject-Oriented Organized around major subjects, such as customer, product, sales Focusing on the modeling and analysis of data for decision makers, not

on daily operations or transaction processing Provide a simple and concise view around particular subject issues by

excluding data that are not useful in the decision support process Data Warehouse—Integrated Constructed by integrating multiple, heterogeneous data sources

relational databases, flat files, on-line transaction records Data cleaning and data integration techniques are applied.

Ensure consistency in naming conventions, encoding structures, attribute measures, etc. among different data sources

E.g., Hotel price: currency, tax, breakfast covered, etc. When data is moved to the warehouse, it is converted.


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Data Warehouse—Time VariantThe time horizon for the data warehouse is significantly longer than that of operational systems

Operational database: current value dataData warehouse data: provide information from a historical perspective (e.g., past 5-10 years)

Every key structure in the data warehouseContains an element of time, explicitly or implicitlyBut the key of operational data may or may not contain “time element”Data Warehouse—Nonvolatile

A physically separate store of data transformed from the operational environmentOperational update of data does not occur in the data warehouse environment

Does not require transaction processing, recovery, and concurrency control mechanismsRequires only two operations in data accessing:

initial loading of data and access of data


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Data Warehouse vs. Heterogeneous DBMS

Traditional heterogeneous DB integration: A query driven approach Build wrappers/mediators on top of heterogeneous databases When a query is posed to a client site, a meta-dictionary is used

to translate the query into queries appropriate for individual heterogeneous sites involved, and the results are integrated into a global answer set

Complex information filtering, compete for resources Data warehouse: update-driven, high performance

Information from heterogeneous sources is integrated in advance and stored in warehouses for direct query and analysis


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Data Warehouse vs. Operational DBMS

OLTP (on-line transaction processing) Major task of traditional relational DBMS Day-to-day operations: purchasing, inventory, banking, manufacturing,

payroll, registration, accounting, etc. OLAP (on-line analytical processing)

Major task of data warehouse system Data analysis and decision making

Distinct features (OLTP vs. OLAP): User and system orientation: customer vs. market Data contents: current, detailed vs. historical, consolidated Database design: ER + application vs. star + subject View: current, local vs. evolutionary, integrated Access patterns: update vs. read-only but complex queries


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OLTP vs. OLAP OLTP OLAP users clerk, IT professional knowledge worker function day to day operations decision support DB design application-oriented subject-oriented data current, up-to-date

detailed, flat relational isolated

historical, summarized, multidimensional integrated, consolidated

usage repetitive ad-hoc access read/write

index/hash on prim. key lots of scans

unit of work short, simple transaction complex query # records accessed tens millions #users thousands hundreds DB size 100MB-GB 100GB-TB metric transaction throughput query throughput, response


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Why Separate Data Warehouse? High performance for both systems

DBMS— tuned for OLTP: access methods, indexing, concurrency control, recovery

Warehouse—tuned for OLAP: complex OLAP queries, multidimensional view, consolidation

Different functions and different data: missing data: Decision support requires historical data which

operational DBs do not typically maintain data consolidation: DS requires consolidation (aggregation,

summarization) of data from heterogeneous sources data quality: different sources typically use inconsistent data

representations, codes and formats which have to be reconciled Note: There are more and more systems which perform OLAP

analysis directly on relational databases


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Characteristics of Data WarehouseA DWH can be viewed as an information system with the

following attributes:1. It is a database designed for analytical tasks, using data

from multiple applications.2. It supports a relatively small no. of users with relatively long

interactions.3. Its usage is read-intensive.4. Its content is periodically updated (mostly additions).5. It contains current and historical data to provide a historical

perspective of information.6. It contains a few large tables.7. Each query frequently results in a large result set and

involves frequent full table scan and multi-table joins.8. DWH is an environment, not a product. It is an architectural

construct of information systems that provides users with current and historical decision support information.

9. It is blend of technologies aimed at the effective integration of operational database into an environment.


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Need of DWH1. Decisions need to be made quickly and correctly

using all available data.2. Users are business domain experts, not computer

professionals.3. The amount of data doubles every 18 months

which affects response time & the sheer ability to comprehend its content.

4. Competition is heating up in the area of business intelligence and added information value.

5. The DWH is designed to address the incompatibility of informational and operational transactional systems.

6. The IT infrastructure is changing( such as price of computer, speed, storage, N/W bandwidth, heterogeneous workplace)


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Benefits of Datawarehousing Tangible Benefits:1. Product inventory turnover is improved.2. Cost of product introduction are decreased with improved

selection of target markets.3. More cost-effective decision making is enabled by

separating query processing from running against operational databases.

4. Better business intelligence is enabled by increased quality & flexibility of market analysis available.

Intangible Benefits:1. Improved productivity2. Reduced redundant processing, support and S/W to support

overlapping decision support application.3. Enhanced customer relations4. Enabling business process reengineering DWH can provide

useful insights into the work processes themselves.


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The Data Warehouse Delivery processIT Strategy

Education

Build the Vision

History Load

Ad-hoc Query

Automation

Extending Scope

Business Requirements

Business Case Analysis

Technical BlueprintRequirem

ents Evolution


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The Data Warehouse Delivery process

IT Strategy : DWH project must include IT strategy for procuring and retaining funding.

Business Case Analysis : It is necessary to understand the level of investment that can be justified and to identify the projected business benefits that should be derived from using DWH.

Education & Prototyping : Organizations will experiment with the concept of data analysis and educate themselves on the value of DWH. This is valuable and should be considered if this is the organization’s first exposure to the benefits of DS information. The prototyping activity can progress the growth of education. It is better than working models. Prototyping require business requirement, technical blueprint ,architecture.

Business Requirement : It includes such as The logical model for information within the DWH. The source system that provide this data( mapping rules) The business rules to be applied to data. The query profiles for the immediate requirement


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Technical Blueprint : It must identify :The overall system architectureThe server & data mart architecture for both data & applications.The essential components of the data base design.The data retention strategyThe backup & recovery strategyThe capacity plan fro H/w and infrastructure

Building the vision : It is the stage where the first production deliverable is produced. This stage will probably build the major infrastructure components for extracting and loading data, but limit them to the extraction & load of data sources. History Load : In most cases, the next phase is one where the remainder of the required history is loaded into the DWH. This means that new entities would not be added to the DWH, but additional physical tables would probably be created to store the increased data volumes.AD-Hoc Query : In this process we configure an ad-hoc query tool to operate against the DWH. These end-user access tools are capable of automatically generating the database query that answer any question posed by the user.


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Automation : The automation phase is where many of the operational management processes are fully automated within the DWH. These would include:

Extracting & loading the data from a variety of sources systems Transforming the data into a form suitable fro analysis Backing up, restoring & archiving data Generating aggregations from predefined definitions within DWH Monitoring query profiles & determining the appropriate aggregations to

maintain system performance. Extending Scope : In this phase, the scope of DWH is extended to address a

new set of business requirements. This involves the loading of additional data sources into the DWH i.e. introduction of new data marts .

Requirements evolution : This is most important aspect of the delivery process is that the requirements are never static. Business requirements will constantly change during the life of the DWH, so it is imperative that the process support this, and allows these changes to be reflected within the system.


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Chapter 3: Data Warehousing and OLAP Technology: An Overview



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From Tables and Spreadsheets to Data Cubes

A data warehouse is based on a multidimensional data model which views data in the form of a data cube

A data cube, such as sales, allows data to be modeled and viewed in multiple dimensions

Dimension tables, such as item (item_name, brand, type), or time(day, week, month, quarter, year)

Fact table contains measures (such as dollars_sold) and keys to each of the related dimension tables

In data warehousing literature, an n-D base cube is called a base cuboid. The top most 0-D cuboid, which holds the highest-level of summarization, is called the apex cuboid. The lattice of cuboids forms a data cube.


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Cube: A Lattice of Cuboids

time,item

time,item,location

time, item, location, supplier

all

time item location supplier

time,location

time,supplier

item,location

item,supplier

location,supplier

time,item,supplier

time,location,supplier

item,location,supplier

0-D(apex) cuboid

1-D cuboids

2-D cuboids

3-D cuboids

4-D(base) cuboid


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Conceptual Modeling of Data Warehouses

Modeling data warehouses: dimensions & measures Star schema: A fact table in the middle connected

to a set of dimension tables Snowflake schema: A refinement of star schema

where some dimensional hierarchy is normalized into a set of smaller dimension tables, forming a shape similar to snowflake

Fact constellations: Multiple fact tables share dimension tables, viewed as a collection of stars, therefore called galaxy schema or fact constellation


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Example of Star Schema

time_keydayday_of_the_weekmonthquarteryear

time

location_keystreetcitystate_or_provincecountry

location

Sales Fact Table

time_key

item_key

branch_key

location_key

units_sold

dollars_sold

avg_salesMeasures

item_keyitem_namebrandtypesupplier_type

item

branch_keybranch_namebranch_type

branch


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Example of Snowflake Schema


time

location_keystreetcity_key

location

Sales Fact Table

time_key

item_key

branch_key

location_key

units_sold

dollars_sold

avg_sales

Measures

item_keyitem_namebrandtypesupplier_key

item


branch

supplier_keysupplier_type

supplier

city_keycitystate_or_provincecountry

city


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Example of Fact Constellation


time

location_keystreetcityprovince_or_statecountry

location

Sales Fact Table

time_key

item_key

branch_key

location_key

units_sold

dollars_sold

avg_salesMeasures

item_keyitem_namebrandtypesupplier_type

item


branch

Shipping Fact Table

time_key

item_key

shipper_key

from_location

to_location

dollars_cost

units_shipped

shipper_keyshipper_namelocation_keyshipper_type

shipper


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Cube Definition Syntax (BNF) in DMQL

Cube Definition (Fact Table)define cube <cube_name> [<dimension_list>]:

<measure_list> Dimension Definition (Dimension Table)

define dimension <dimension_name> as (<attribute_or_subdimension_list>)

Special Case (Shared Dimension Tables) First time as “cube definition” define dimension <dimension_name> as

<dimension_name_first_time> in cube <cube_name_first_time>


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Defining Star Schema in DMQL

define cube sales_star [time, item, branch, location]:dollars_sold = sum(sales_in_dollars), avg_sales

= avg(sales_in_dollars), units_sold = count(*)define dimension time as (time_key, day,

day_of_week, month, quarter, year)define dimension item as (item_key, item_name,

brand, type, supplier_type)define dimension branch as (branch_key,

branch_name, branch_type)define dimension location as (location_key, street,

city, province_or_state, country)


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Defining Snowflake Schema in DMQL

define cube sales_snowflake [time, item, branch, location]:dollars_sold = sum(sales_in_dollars), avg_sales =

avg(sales_in_dollars), units_sold = count(*)define dimension time as (time_key, day, day_of_week, month,

quarter, year)define dimension item as (item_key, item_name, brand, type,

supplier(supplier_key, supplier_type))define dimension branch as (branch_key, branch_name,

branch_type)define dimension location as (location_key, street,

city(city_key, province_or_state, country))


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Defining Fact Constellation in DMQL

define cube sales [time, item, branch, location]:dollars_sold = sum(sales_in_dollars), avg_sales =

avg(sales_in_dollars), units_sold = count(*)define dimension time as (time_key, day, day_of_week, month, quarter,

year)define dimension item as (item_key, item_name, brand, type,

supplier_type)define dimension branch as (branch_key, branch_name, branch_type)define dimension location as (location_key, street, city, province_or_state,

country)define cube shipping [time, item, shipper, from_location, to_location]:

dollar_cost = sum(cost_in_dollars), unit_shipped = count(*)define dimension time as time in cube salesdefine dimension item as item in cube salesdefine dimension shipper as (shipper_key, shipper_name, location as

location in cube sales, shipper_type)define dimension from_location as location in cube salesdefine dimension to_location as location in cube sales


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Measures of Data Cube: Three Categories

Distributive: if the result derived by applying the function to n aggregate values is the same as that derived by applying the function on all the data without partitioning

E.g., count(), sum(), min(), max() Algebraic: if it can be computed by an algebraic function

with M arguments (where M is a bounded integer), each of which is obtained by applying a distributive aggregate function

E.g., avg(), min_N(), standard_deviation() Holistic: if there is no constant bound on the storage size

needed to describe a subaggregate. E.g., median(), mode(), rank()


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A Concept Hierarchy: Dimension (location)

all

Europe North_America

MexicoCanadaSpainGermany

Vancouver

M. WindL. Chan

...

......

... ...

...

all

region

office

country

TorontoFrankfurtcity


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View of Warehouses and Hierarchies

Specification of hierarchies Schema hierarchy

day < {month < quarter; week} < year

Set_grouping hierarchy{1..10} < inexpensive


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Multidimensional Data Sales volume as a function of product,

month, and region

Prod

uct

Region

Month

Dimensions: Product, Location, TimeHierarchical summarization paths

Industry Region Year

Category Country Quarter

Product City Month Week

Office Day


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A Sample Data CubeTotal annual salesof TV in U.S.A.Date

Produ

ct

Cou

ntrysum

sum TV

VCRPC

1Qtr 2Qtr 3Qtr 4Qtr

U.S.A

Canada

Mexico

sum


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Cuboids Corresponding to the Cube

all

product date country

product,date product,country date, country

product, date, country

0-D(apex) cuboid

1-D cuboids

2-D cuboids

3-D(base) cuboid


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Browsing a Data Cube

Visualization OLAP capabilities Interactive manipulation


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Typical OLAP Operations Roll up (drill-up): summarize data

by climbing up hierarchy or by dimension reduction Drill down (roll down): reverse of roll-up

from higher level summary to lower level summary or detailed data, or introducing new dimensions

Slice and dice: project and select Pivot (rotate):

reorient the cube, visualization, 3D to series of 2D planes

Other operations drill across: involving (across) more than one fact table drill through: through the bottom level of the cube to

its back-end relational tables (using SQL)


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Fig. 3.10 Typical OLAP Operations


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A Star-Net Query Model Shipping Method

AIR-EXPRESS

TRUCKORDER

Customer Orders

CONTRACTS

Customer

Product

PRODUCT GROUP

PRODUCT LINE

PRODUCT ITEM

SALES PERSON

DISTRICT

DIVISION

OrganizationPromotion

CITY

COUNTRY

REGION

Location

DAILYQTRLYANNUALYTime

Each circle is called a footprint


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Design of Data Warehouse: A Business Analysis Framework

Four views regarding the design of a data warehouse Top-down view

allows selection of the relevant information necessary for the data warehouse

Data source view exposes the information being captured, stored, and

managed by operational systems Data warehouse view

consists of fact tables and dimension tables Business query view

sees the perspectives of data in the warehouse from the view of end-user


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Data Warehouse Design Process

Top-down, bottom-up approaches or a combination of both Top-down: Starts with overall design and planning (mature). It is useful in cases where the

technology is mature and well known, and where the business problems that must be solved are clear and well understood.

Bottom-up: Starts with experiments and prototypes (rapid). This is useful intheearly stage of business modeling and technology development. It allows an organization to move forward at considerably less expense and to evaluate the benefits of the technology before making significant commitments.

From software engineering point of view Waterfall : structured and systematic analysis at each step before proceeding to the next Spiral : rapid generation of increasingly functional systems, short turn around time, quick

turn around Typical data warehouse design process

Choose a business process to model, e.g., orders, invoices, etc. Choose the grain (atomic level of data) of the business process Choose the dimensions that will apply to each fact table record Choose the measure that will populate each fact table record


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Process Architecture

Operational data

External data

Load Manager

Query Manager

Detailed Information

Meta data

Summaery Info

Warehouse Manger

Data Information Data

OLAP tools

Data dippers


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Data Warehouse: A Multi-Tiered ArchitectureData Warehouse: A Multi-Tiered Architecture

DataWarehouse

ExtractTransformLoadRefresh

OLAP Engine

AnalysisQueryReportsData mining

Monitor&

IntegratorMetadata

Data Sources Front-End Tools

Serve

Data Marts

Operational DBs

Othersources

Data Storage

OLAP Server


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Data Warehouse: A Multi-Tiered Data Warehouse: A Multi-Tiered ArchitectureArchitecture

1. The transaction or other operational database(s) from which the data warehouse is populated.

2. A process to extract data from this database or these databases, and bring it into the DWH. This process must often transform the data into the database structure & internal formats of DWH.

3. A process to cleanse the data to make sure it is of sufficient quality for the decision making purposes for which it will be used.

4. A process to load the cleansed data into DWH database. The four processes from extraction through loading are often referred to collectively as data staging.

5. A process to create any desired summaries of the data: pre-calculated totals, averages, and the like, which are expected to be requested often.

6. Metadata “ data about data”. It is useful to have a central information repository to tell users What is in DWH where it came from, who is in-charge of it and more.

7. The DWH database itself .This database contains the detailed and summary data of the DWH.

8. Querry tools usually include and end-user interface for posing questions to the database, in a process called OLAP. They may also include automated tools for uncovering patterns in the data, often reffered to as data mining

9. The users or users for whom the data warehouse exists and without whom it would be useless.


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DWH Components1. Data sourcing, cleanup, transformation &

migration tools.2. Metadata Repository3. DWH database Technology4. Datamarts5. Data query, reporting, analysis and mining

tools6. DWH administration & management7. Information delivery system8. Operational data store.


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Building a DWH Business considerations(a) Approach(b) Organizational Issues Design considerations(a) Data control(b) Meta data(c) Data Distribution(d) Tools(e) Performance considerations(f) Nine decisions in the design of DWH


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Building a DWH Technical considerations1. H/W Platforms2. DWH & DBMS specialization3. Communication Infrastructure Implementation considerations1. Access Tools2. Data extraction, Cleanup, transformation &

Migration.3. Data Placement Strategies4. Metadata5. User sophistication levels.


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Three Data Warehouse Models Enterprise warehouse

collects all of the information about subjects spanning the entire organization

Data Mart a subset of corporate-wide data that is of value to a

specific groups of users. Its scope is confined to specific, selected groups, such as marketing data mart Independent vs. dependent (directly from warehouse) data

mart Virtual warehouse

A set of views over operational databases Only some of the possible summary views may be

materialized


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Data Warehouse Development: A

Recommended Approach

Define a high-level corporate data model

Data Mart

Data Mart

Distributed Data Marts

Multi-Tier Data Warehouse

Enterprise Data Warehouse

Model refinementModel refinement


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Data Warehouse Back-End Tools and Utilities

Data extraction get data from multiple, heterogeneous, and external

sources Data cleaning

detect errors in the data and rectify them when possible

Data transformation convert data from legacy or host format to warehouse

format Load

sort, summarize, consolidate, compute views, check integrity, and build indicies and partitions

Refresh propagate the updates from the data sources to the

warehouse


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Metadata Repository Meta data is the data defining warehouse objects. It stores: Description of the structure of the data warehouse

schema, view, dimensions, hierarchies, derived data defn, data mart locations and contents

Operational meta-data data lineage (history of migrated data and transformation path),

currency of data (active, archived, or purged), monitoring information (warehouse usage statistics, error reports, audit trails)

The algorithms used for summarization The mapping from operational environment to the data

warehouse Data related to system performance

warehouse schema, view and derived data definitions Business data

business terms and definitions, ownership of data, charging policies


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Data Marting A data mart is a subset of an organizational data store, usually oriented

to a specific purpose or major data subject, that may be distributed to support business needs. Data marts are analytical data stores designed to focus on specific business functions for a specific community within an organization. Data marts are often derived from subsets of data in a data warehouse, though in the bottom-up data warehouse design methodology the data warehouse is created from the union of organizational data marts.

A data mart is a repository of data gathered from operational data and other sources that is designed to serve a particular community of knowledge workers

Data Marts are created for the following reasons: To speed up queries by reducing the volume of data to be scanned. To structure data in a form suitable for a user access tool. To partition data in order to impose access control strategies. To segment data into different hardware platforms.


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Data Marting Situation for creation of Data Marts to identify whether

There is a natural functional split within the organization. There is a natural split of the data. The proposed user access tool uses its own database

structures. Any infrastructure issues predicate the use of data marts. There are any access control issues that require data marts to

provide Chinese walls. Costs of Data Marting : data Marting strategies incur costs in

the following areas: Hardware and software Network Access Time-window constraints


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Aggregations Data aggregation is an essential component of decision support

DWH. It allows us to provide cost-effective query performance by avoiding the need for substantial.

Aggregation strategies rely on the fact that most common queries will analyze either a subset or an aggregation of the detailed data.

We can see that the appropriate aggregation will substantially reduce the processing time required to run a query, at the cost of processing and storing the intermediate results.

The aggregations make trends more apparent, by providing an overview of the whole picture rather than small parts of the picture.


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Aggregations Designing Summary Tables : The primary purpose of using

summary tables is to cut down the time it takes to execute queries. The objective of using summary tables is to minimize the volume of data being scanned, by storing as many partial results as possible.

The summary tables are designed using following steps: Determine which dimensions are aggregated. Determine the aggregation of multiple values. Aggregate multiple facts into the summary table. Determine the level of aggregation. Determine the extent of embedding dimension data in the

summary. Design time into the summary table. Index the summary table.


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OLAP Server Architectures Relational OLAP (ROLAP)

Use relational or extended-relational DBMS to store and manage warehouse data and OLAP middle ware

Include optimization of DBMS backend, implementation of aggregation navigation logic, and additional tools and services

Greater scalability Multidimensional OLAP (MOLAP)

Sparse array-based multidimensional storage engine Fast indexing to pre-computed summarized data

Hybrid OLAP (HOLAP) (e.g., Microsoft SQLServer) Flexibility, e.g., low level: relational, high-level: array

Specialized SQL servers (e.g., Redbricks) Specialized support for SQL queries over star/snowflake

schemas


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OLAP Guidelines (Dr. E.F. Codd Rule)

Dr. E.F. Codd the “father” of the relational model has formulated a list of 12 guidelines and requirements as the basis for selecting OLAP systems:

1. Multidimensional conceptual view2. Transparency3. Accessibility4. Consistent reporting performance5. Client / Server Architecture6. Generic dimensionality7. Dynamic sparse matrix handling8. Multiuser support9. Unrestricted cross-dimensional operations10. Intuitive data manipulation11. Flexible reporting12. Unlimited dimensions & aggregation levels


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MOLAP In the OLAP world, there are mainly two different types: Multidimensional OLAP (MOLAP)

and Relational OLAP (ROLAP). Hybrid OLAP (HOLAP) refers to technologies that combine MOLAP and ROLAP.

MOLAPExcellent performance- this is the more traditional way of OLAP analysis. In MOLAP, data is stored in a multidimensional cube. The storage is not in the relational database, but in proprietary formats.

Advantages:MOLAP cubes are built for fast data retrieval, and are optimal for slicing and dicing operations.

They can also perform complex calculations. All calculations have been pre-generated when the cube is created. Hence, complex calculations are not only doable, but they return quickly.

Excellent performance: MOLAP cubes are built for fast data retrieval, and is optimal for slicing and dicing operations.

Disadvantages:It is limited in the amount of data it can handle. Because all calculations are performed when the cube is built, it is not possible to include a large amount of data in the cube itself. This is not to say that the data in the cube cannot be derived from a large amount of data. Indeed, this is possible. But in this case, only summary-level information will be included in the cube itself.

It requires an additional investment. Cube technology are often proprietary and do not already exist in the organization.


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ROLAPThis methodology relies on manipulating the data stored in the relational database to give the appearance of traditional OLAP's slicing and dicing functionality. In essence, each action of slicing and dicing is equivalent to adding a "WHERE" clause in the SQL statement.

Advantages:It can handle large amounts of data. The data size limitation of ROLAP technology is the limitation on data size of the underlying relational database. In other words, ROLAP itself places no limitation on data amount.

It can leverage functionalities inherent in the relational database. Often, relational database already comes with a host of functionalities. ROLAP technologies, since they sit on top of the relational database, can therefore leverage these functionalities.

Disadvantages:Performance can be slow. Because each ROLAP report is essentially a SQL query (or multiple SQL queries) in the relational database, the query time can be long if the underlying data size is large.

It has limited by SQL functionalities. Because ROLAP technology mainly relies on generating SQL statements to query the relational database, and SQL statements do not fit all needs (for example, it is difficult to perform complex calculations using SQL), ROLAP technologies are therefore traditionally limited by what SQL can do. ROLAP vendors have mitigated this risk by building into the tool out-of-the-box complex functions as well as the ability to allow users to define their own functions.

HOLAPHOLAP technologies attempt to combine the advantages of MOLAP and ROLAP. For summary-type information, HOLAP leverages cube technology for faster performance. When detail information is needed, HOLAP can "drill through" from the cube into the underlying relational data


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The FASMI test FAST means that the system is targeted to deliver most responses to users within about five seconds, with the

simplest analyses taking no more than one second and very few taking more than 20 seconds. Independent research in The Netherlands has shown that end-users assume that a process has failed if results are not received with 30 seconds, and they are apt to hit ‘Alt+Ctrl+Delete’ unless the system warns them that the report will take longer. Even if they have been warned that it will take significantly longer, users are likely to get distracted and lose their chain of thought, so the quality of analysis suffers. This speed is not easy to achieve with large amounts of data, particularly if on-the-fly and ad hoc calculations are required. Vendors resort to a wide variety of techniques to achieve this goal, including specialized forms of data storage, extensive pre-calculations and specific hardware requirements, but we do not think any products are yet fully optimized, so we expect this to be an area of developing technology. In particular, the full pre-calculation approach fails with very large, sparse applications as the databases simply get too large (the database explosion problem), whereas doing everything on-the-fly is much too slow with large databases, even if exotic hardware is used. Even though it may seem miraculous at first if reports that previously took days now take only minutes, users soon get bored of waiting, and the project will be much less successful than if it had delivered a near instantaneous response, even at the cost of less detailed analysis. The OLAP Survey has found that slow query response is consistently the most often-cited technical problem with OLAP products, so too many deployments are clearly still failing to pass this test.

ANALYSIS means that the system can cope with any business logic and statistical analysis that is relevant for the application and the user, and keep it easy enough for the target user. Although some pre-programming may be needed, we do not think it acceptable if all application definitions have to be done using a professional 4GL. It is certainly necessary to allow the user to define new ad hoc calculations as part of the analysis and to report on the data in any desired way, without having to program, so we exclude products (like Oracle Discoverer) that do not allow adequate end-user oriented calculation flexibility. We do not mind whether this analysis is done in the vendor's own tools or in a linked external product such as a spreadsheet, simply that all the required analysis functionality be provided in an intuitive manner for the target users. This could include specific features like time series analysis, cost allocations, currency translation, goal seeking, ad hoc multidimensional structural changes, non-procedural modeling, exception alerting, data mining and other application dependent features. These capabilities differ widely between products, depending on their target markets.

http://www.business-intelligence.co.uk/reports/olapsurvey/default.asp


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The FASMI test SHARED means that the system implements all the security requirements for

confidentiality (possibly down to cell level) and, if multiple write access is needed, concurrent update locking at an appropriate level. Not all applications need users to write data back, but for the growing number that do, the system should be able to handle multiple updates in a timely, secure manner. This is a major area of weakness in many OLAP products, which tend to assume that all OLAP applications will be read-only, with simplistic security controls. Even products with multi-user read-write often have crude security models; an example is Microsoft OLAP Services.

MULTIDIMENSIONAL is our key requirement. If we had to pick a one-word definition of OLAP, this is it. The system must provide a multidimensional conceptual view of the data, including full support for hierarchies and multiple hierarchies, as this is certainly the most logical way to analyze businesses and organizations. We are not setting up a specific minimum number of dimensions that must be handled as it is too application dependent and most products seem to have enough for their target markets. Again, we do not specify what underlying database technology should be used providing that the user gets a truly multidimensional conceptual view.

INFORMATION is all of the data and derived information needed, wherever it is and however much is relevant for the application. We are measuring the capacity of various products in terms of how much input data they can handle, not how many Gigabytes they take to store it. The capacities of the products differ greatly — the largest OLAP products can hold at least a thousand times as much data as the smallest. There are many considerations here, including data duplication, RAM required, disk space utilization, performance, integration with data warehouses and the like.


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Cube Operation Cube definition and computation in DMQL

define cube sales[item, city, year]: sum(sales_in_dollars)

compute cube sales Transform it into a SQL-like language (with a new operator

cube by, introduced by Gray et al.’96)SELECT item, city, year, SUM (amount)FROM SALESCUBE BY item, city, year

Need compute the following Group-Bys (date, product, customer),(date,product),(date, customer), (product, customer),(date), (product), (customer)()

(item)(city)

()

(year)

(city, item) (city, year) (item, year)

(city, item, year)


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Efficient Processing OLAP Queries Determine which operations should be performed on the available cuboids

Transform drill, roll, etc. into corresponding SQL and/or OLAP operations, e.g., dice = selection + projection

Determine which materialized cuboid(s) should be selected for OLAP op. Let the query to be processed be on {brand, province_or_state} with the

condition “year = 2004”, and there are 4 materialized cuboids available:1) {year, item_name, city} 2) {year, brand, country}3) {year, brand, province_or_state}4) {item_name, province_or_state} where year = 2004Which should be selected to process the query?

Explore indexing structures and compressed vs. dense array structs in MOLAP


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Data Warehouse Usage Three kinds of data warehouse applications

Information processing supports querying, basic statistical analysis, and reporting

using crosstabs, tables, charts and graphs Analytical processing

multidimensional analysis of data warehouse data supports basic OLAP operations, slice-dice, drilling,

pivoting Data mining

knowledge discovery from hidden patterns supports associations, constructing analytical models,

performing classification and prediction, and presenting the mining results using visualization tools


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From On-Line Analytical Processing (OLAP)

to On Line Analytical Mining (OLAM) Why online analytical mining?

High quality of data in data warehouses DW contains integrated, consistent, cleaned data

Available information processing structure surrounding data warehouses

ODBC, OLEDB, Web accessing, service facilities, reporting and OLAP tools

OLAP-based exploratory data analysis Mining with drilling, dicing, pivoting, etc.

On-line selection of data mining functions Integration and swapping of multiple mining

functions, algorithms, and tasks


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An OLAM System Architecture

Data Warehouse

Meta Data

MDDB

OLAMEngine

OLAPEngine

User GUI API

Data Cube API

Database API

Data cleaning

Data integration

Layer3

OLAP/OLAM

Layer2

MDDB

Layer1

Data Repository

Layer4

User Interface

Filtering&Integration Filtering

Databases

Mining query Mining result


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Data Warehouse Security Responsibility and Confidentiality : The Data Warehouse contains

confidential and sensitive University data. In order to use its data, you must have proper authorization. Your authorization means that you have the authority to use the data and the responsibility to share stewardship of the data with the other users of the collection.

Once authorized, you can access the data that you need to do your job. All authorized users are cautioned, however, that they are entrusted to use the data they retrieve from the Warehouse with care. Confidential data should not be released to others except for those with a "legitimate need to know."

Please remember that you should never share Business Objects queries with other users with the data intact -- send the query without the data. More information about sending and saving Business Objects documents.

Querying Data with Security Restrictions : If you execute a query requesting data that you are not authorized to access, you will get results which may be incomplete because they are missing the data you are not allowed to access.

If your authorization is limited to a specific set of data, be sure when querying the data that your record selection conditions include your security restrictions. For example, if you are authorized to access just data for a particular department, one of your record selection conditions should state something like "If Organization= 'My Organization'," where My Organization is the code of your department. This will document why the query gets the results it does, and will also help your query run faster.


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SECURITY A DWH by nature is an open accessible system. The aim of DWH is generally to make

large amounts of data easily accessible to the users, thereby enabling the users to extract information about the business as a whole.

It is important to establish early any security and audit requirements that will be placed on the DWH.

Clearly, adding security will affect performance because further checks require CPU cycles and time to perform.

Requirement :Security can affect many different parts of the DWH such as : User access – can be done by

Data classification By Level of security required By Data sensitivity By job Function

User Classification Top-down company hierarchy (department , section, group) By Role

Data load Legal Requirements : it is vital to establish any legal requirements (law) on the data

being stored. Audit Requirements : such as connections, disconnections, data access, data change. Network Requirements : (routes)


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SECURITY Data movement : Type of file to be moved & the manner in which

file has to be moved (flat file, encrypted / decrypted, summary generation, results temporary tables)

Documentation : It is important to get all the security and audit requirements clearly documented as this will be needed as part of any cost justification. This document should contain all the information gathered on:

Data classification User classification Network requirement Data movement & storage requirements All auditable actions

Query generation


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User Access Hierarchy

Detailed Sales data

Detailed Customer

DataSummarized sales dataReference Data

Senior AnalystAdministrators

Administer

Administer

MarketingSales

Data Warehouse Inc.

Analyst

Analyst

Analyst

Analyst

Analyst

AnalystAnalyst


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Backup and Recovery Backup is one of the most important regular operations carried

out on any system. It is important in the DWH environment because of the volumes

of data involved and the complexity of the system. Types of Backup

In a complete backup, the entire database is backedup at the same time. This includes all database data files, the control files and the journal files.

Partial backup is any backup that is not complete. A cold backup is a backup that is taken while the database

is completely shutdown. In a multi-instance environment, all instances of that database must be shut down.

Hot backup : any backup that is not cold is considered to be hot.

Online backup is a synonym for hot backup


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Backup and Recovery Hardware: When you are choosing a backup strategy, one of the first

decisions you will have to make is which H/W to use. The choice depend on factors such as speed at which a backup or restore can be processed, H/W connection, N/W bandwidth, backup S/W used, speed of server’s I/O subsystem and components.

Tape Technology Tape media (reliability / life / cost of tape medium/drive, scalability) Standalone tape drives (connection directly to servers, as a network-

available device, remotely to another machine) Tape stackers (a method of loading multiple tapes into a single tape

drive. Only one tape can be accessed at a time, but the stacker will automatically dismount the current tape when it has finished with it and load the next tape)

Tape silos (These are large tape storage facilities, which can store and manage thousands of tapes. These are generally sealed environments with robotic arms for manipulating the tapes.)

Disk-to-disk backups (the backup is performed to disk rather than to tape)


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Backup and Recovery Software (Omniback II, ADSM, Alexandria, Epoch, Networker)

Performance- such as degree of parallelism, I/O bottlenecks

Requirements : When considering which backup package to use it is important to check the following criteria.

What degree of parallelism is possible? How scalable is the product as tape drives are added? What platforms are supported by the package? What tape drives and tape media are supported by the

package? Does the package support easy access to information

about tape contents? Backup strategies:

Effect on database Design- such as DB partitioning strategies.

Design Strategies -main aim should be to reduce the amount of data that has to be backed up on regular basis, e.g. Read-only tablespace, automation of backup


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Backup and Recovery Recovery Strategies- depend on kind of failure and consist of a set of

failure scenarios & their resolution. Each of the following failure scenarios indicated below needs to be centred for recovery steps and must be documented:1. Instance Failure 2. Media failure3. Loss or damage of table space or data file 4. Loss or damage of a table5. Loss or damage of control file 6. Failure during data movement

The plan must be made for following data movement scenarios :1. Data load into staging tables2. Movement from staging to fact table3. Partition roll-up into larger partitions4. Creation of Aggregations.

Testing the Strategy- The backup and recovery tests need to be carried out on a regular basis, but it is advisable to avoid performing tests at busy times such as end of year and try to test to be run at low load in the business year.


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Backup and Recovery Disaster Recovery- A disaster can be defined where a major site loss

has taken place or destroyed or damaged beyond immediate repair. It is advisable to decide a criteria for making that judgment before any situation occurs, as attempting to make such a decision while in the middle of crises is likely to lead problems. Deal with following requirements for disaster recovery:

Planning Disaster recovery with minimal system required. Replacement / standby machine Sufficient tape and disk capacity Communication links to users. Communication links to data sources. Copies of all relevant pieces of software. Backup of database. Application-aware systems administration and operation staff.


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Tuning the Data WarehouseTuning the data warehouse deal with the measures such as

Average query response timesscan ratesI/O throughput ratesTime used per query (fixed or ad-hoc)

No. of users in the groupWhether they use adhoc queries frequently or occasionally at unknown intervals or at regular or predictable timesThe average / maximum size of query they tend to runThe peak time of daily usageThe more unpredictable the load, the larger the queries, or the greater the number of users the bigger the tuning task.

Memory usage per process


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Testing the data warehouse Three levels of testing

Unit testing : each development unit is tested on its own Integration testing : the separate development units that

make up a component of DWH application are tested to ensure that they work together.

System Testing :the whole DWH application is tested together. The components are tested to ensure that they work properly together, that they don’t cause system bottlenecks.

Developing the Test Plan Test Schedule – metrics for estimating the amount of time

required for testing Data Load

How will the data be generated ? Where will the data be generated ? How will the generated data be loaded ? Will the data be correctly skewed ?


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Testing the data warehouse Testing Backup Recovery: To test the recovery of a lost data file, a

data file should actually be deleted and recovered from backup. Check that the backup database is working correctly, and actually tracks what has been backed up and where it has been backed upto. If that works then check that the information can be retrieved. Check that all the backup hardware is working: tapes, tape drives, controllers etc. Each of the scenarios indicated below needs to be centred for 1. Instance Failure 2. Media failure3. Loss or damage of table space or data file4. Loss or damage of a table 5. Loss or damage of control file6. Failure during data movement

Testing the Operational Environment : Testing of the DWH operational environment is another key set o tests that will have to be performed. There are following aspects that need to be tested :

Security – document what is not allowed, disallowed operations and devising a test for each

Disk configuration – test thoroughly to identify any potential I/O bottlenecks.


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Testing the data warehouse Scheduler – Given the possibility for many of the processes in the

DHW to swamp the system resources if allowed to run at the wrong time, scheduling control of these processes is essential to the success of the DWH.

Management Tools – (event / system / configuration / backup recovery / database)

Database ManagementTesting the database : It can be broken down into three separate sets of tests:

Testing the database manager and monitoring tools (creation, running & management of the test database)

Testing database features (querying / create index / data load in parallel) Testing database performance (test queries with different aggregations,

index strategies , degree of parallel, different-sized data sets) Testing the application Logistic of the Test (DWH application code, day-to-day operational

procedures, backup recovery strategy, query performance, management & monitoring tools, scheduling software)


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What is a data warehouse? A multi-dimensional data model Data warehouse architecture Data warehouse implementation From data warehousing to data mining Summary


Techniques 80

References (I) S. Agarwal, R. Agrawal, P. M. Deshpande, A. Gupta, J. F. Naughton, R.

Ramakrishnan, and S. Sarawagi. On the computation of multidimensional aggregates. VLDB’96

D. Agrawal, A. E. Abbadi, A. Singh, and T. Yurek. Efficient view maintenance in data warehouses. SIGMOD’97

R. Agrawal, A. Gupta, and S. Sarawagi. Modeling multidimensional databases. ICDE’97

S. Chaudhuri and U. Dayal. An overview of data warehousing and OLAP technology. ACM SIGMOD Record, 26:65-74, 1997

E. F. Codd, S. B. Codd, and C. T. Salley. Beyond decision support. Computer World, 27, July 1993.

J. Gray, et al. Data cube: A relational aggregation operator generalizing group-by, cross-tab and sub-totals. Data Mining and Knowledge Discovery, 1:29-54, 1997.

A. Gupta and I. S. Mumick. Materialized Views: Techniques, Implementations, and Applications. MIT Press, 1999.

J. Han. Towards on-line analytical mining in large databases. ACM SIGMOD Record, 27:97-107, 1998.

V. Harinarayan, A. Rajaraman, and J. D. Ullman. Implementing data cubes efficiently. SIGMOD’96


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References (II) C. Imhoff, N. Galemmo, and J. G. Geiger. Mastering Data Warehouse Design:

Relational and Dimensional Techniques. John Wiley, 2003 W. H. Inmon. Building the Data Warehouse. John Wiley, 1996 R. Kimball and M. Ross. The Data Warehouse Toolkit: The Complete Guide to

Dimensional Modeling. 2ed. John Wiley, 2002 P. O'Neil and D. Quass. Improved query performance with variant indexes.

SIGMOD'97 Microsoft. OLEDB for OLAP programmer's reference version 1.0. In

http://www.microsoft.com/data/oledb/olap, 1998 A. Shoshani. OLAP and statistical databases: Similarities and differences. PODS’00. S. Sarawagi and M. Stonebraker. Efficient organization of large multidimensional

arrays. ICDE'94 OLAP council. MDAPI specification version 2.0. In

http://www.olapcouncil.org/research/apily.htm, 1998 E. Thomsen. OLAP Solutions: Building Multidimensional Information Systems. John

Wiley, 1997 P. Valduriez. Join indices. ACM Trans. Database Systems, 12:218-246, 1987. J. Widom. Research problems in data warehousing. CIKM’95.


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