CHETTINADENGINEERINGCOLLEGE

QUESTION BANK

SEMESTER : VI

SUBJECT NAME : DATA WAREHOUSING AND MINING

UNIT-1

PART-A

  1. WHY DO YOU NEED DATA WAREHOUSE LIFE CYCLE PROCESS?

Data warehouse life cycle approach is essential because it ensures that the project pieces are brought together in the right order and at the right time.

2. MERITS OF DATA WAREHOUSE.

  • Ability to make effective decisions from database
  • Better analysis of data and decision support
  • Discover trends and correlations that benefits business
  • Handle huge amount of data.

3. WHAT ARE THE CHARACTERISTICS OF DATA WAREHOUSE?

  • Separate
  • Available
  • Integrated
  • Subject Oriented
  • Not Dynamic
  • Consistency
  • Iterative Development
  • Aggregation Performance

4. LIST SOME OF THE DATA WAREHOUSE TOOLS?

  • OLAP(OnLine Analytic Processing)
  • ROLAP(Relational OLAP)
  • End User Data Access tool
  • Ad Hoc Query tool

Data Transformation services

5. WHY IS DATA QUALITY SO IMPORTANT IN A DATA WAREHOUSE ENVIRONMENT?

Data quality is important in a data warehouse environment to facilitate decision-making. In order to support decision-making, the stored data should provide information from a historical perspective and in a summarized manner.

6. EXPLAIN OLAP?

The general activity of querying and presenting text and number data from DataWarehouses, as well as a specifically dimensional style of querying and presenting that is exemplified by a number of “OLAP Vendours” .The OLAP vendors technology is nonrelational and is almost always biased on an explicit multidimensional cube of data.OLAP databases are also known as multidimensional cube of databases.

7. EXPLAIN ROLAP?

ROLAP is a set of user interfaces and applications that give a relational database a dimensional flavour.ROLAP stands for Relational Online Analytic Processing.

8. WHAT IS ACTIVE META DATA?

Active Meta data is Meta data that drives a process rather than documents.

9. WHAT IS MEAT DATA CATALOGUE?

Meat data catalogue is a single common storage point for information that drives the entire warehouse process.

10. EXPLAIN THE VARIOUS OLAP OPERATIONS.

a) Roll-up: The roll-up operation performs aggregation on a data cube, either by climbing up a concept hierarchy for a dimension.

b)Drill-down: It is the reverse of roll-up. It navigates from less detailed data to more detailed data.

c)Slice: Performs a selection on one dimension of the given cube, resulting in a subcube.

11. WHAT IS DATA WAREHOUSE PERFORMANCE ISSUE?

The performance of a data warehouse is largely a function of the quantity and type of data stored within a database and the query/data loading work load placed upon the system.

12. DEFINE A DATA MART?

Data mart is a pragmatic collection of related facts, but does not have to be exhaustive or exclusive. A datamart is both a kind of subject area and an application. Data mart is a collection of numeric facts.

13. WHAT IS BACK ROOM META DATA?

Back room Meta data is process related, and it guides the extraction, cleaning and loading process.

14. WHAT IS FRONT ROOM META DATA?

It is more descriptive and it helps query tools and report writers function smoothly.

15. MENTION THE THREE TIERS OF DATA WAREHOUSE ARCHITECTURE?

Bottom tier: Data warehouse server

Middle tier: OLAP server

Top tier: Front-end tools

16. WHAT ARE THE APPLICATIONS OF DATA WAREHOUSE?

  1. Information processing
  2. Analytical processing
  3. Data mining

17. WHAT IS MEANT BY SLICE AND DICE?

Slice: The slice operation performs a selection on one dimension of the given cube, resulting in a subcube.

Dice: The dice operation defines a subcube by performing a selection on two or more dimensions.

18.WHAT IS MEANT BY ROLL-UP AND DRILL-DOWN?

Roll-up: The roll-up operation performs aggregation on a data cube, either by climbing up a concept hierarchy for a dimension or by dimension reduction.

Drill-down: Drill-down is the reverse of roll-up. It navigates from less detailed data to more detailed data. Drill-down can be realized by either stepping down a concept hierarchy for a dimension or introducing additional dimensions.

19.WHAT IS MEANT BY PIVOT?

Pivot is also called as rotate. Pivot is a visualization operation that rotates the data axes in view in order to provide an alternative presentation of the data.

20. WHAT ARE THE SPECIFICATIONS OF SOURCE SYSTEM META DATA?

- Repositories

- Source Scheme

- Copy Books

- Spread Sheet Sources

- Lotus notes database

21. DEFINE DATA MINING?

It refers to extracting or “mining” knowledge from large amount of data. Data

mining is a process of discovering interesting knowledge from large amounts of data

stored either, in database, data warehouse, or other information repositories.

22.GIVE SOME ALTERNATIVE TERMS FOR DATA MINING ?

• Knowledge mining

• Knowledge extraction

• Data/pattern analysis.

• Data Archaeology

• Data dredging

23. WHAT IS KDD?

KDD-Knowledge Discovery in Databases.

24.WHAT ARE THE STEPS INVOLVED IN KDD PROCESS.

• Data cleaning

• Data Mining

• Pattern Evaluation

• Knowledge Presentation

• Data Integration

• Data Selection

• Data Transformation

25. WHAT IS THE USE OF THE KNOWLEDGE BASE?

Knowledge base is domain knowledge that is used to guide search or evaluate the

interestingness of resulting pattern. Such knowledge can include concept hierarchies used

to organize attribute /attribute values in to different levels of abstraction.

26. MENTION SOME OF THE DATA MINING TECHNIQUES?

  • Statistics
  • Machine learning
  • Decision Tree
  • Hidden markov models
  • Artificial Intelligence
  • Genetic Algorithm
  • Meta learning

27. GIVE FEW STATISTICAL TECHNIQUES.

• Point Estimation

• Data Summarization

• Bayesian Techniques

• Testing Hypothesis

• Correlation

• Regression

28. WHAT IS META LEARNING?

Concept of combining the predictions made from multiple models of data

mining and analyzing those predictions to formulate a new and previously unknown prediction.

29. DEFINE GENETIC ALGORITHM.

  • Search algorithm.
  • Enables us to locate optimal binary string by processing an initial random population of binary strings by performing operations such as artificial mutation , crossover and selection.

30. WHAT IS THE PURPOSE OF DATA MINING TECHNIQUE?

It provides a way to use various data mining tasks.

31. DEFINE PREDICTIVE MODEL?

It is used to predict the values of data by making use of known results from a different set of sample data.

32. DATA MINING TASKS THAT ARE BELONGS TO PREDICTIVE MODEL

  • Classification
  • Regression
  • Time series analysis

33. DEFINE DESCRIPTIVE MODEL

  • It is used to determine the patterns and relationships in a sample data. Datamining tasks that belongs to descriptive model:
  • Clustering
  • Summarization
  • Association rules
  • Sequence discovery

34. DEFINE THE TERM SUMMARIZATION

The summarization of a large chunk of data contained in a web page or a

document.

Summarization = characterization=generalization

35. LIST OUT THE ADVANCED DATABASE SYSTEMS.

  • Extended-relational databases
  • Object-oriented databases
  • Deductive databases
  • Spatial databases
  • Temporal databases
  • Multimedia databases
  • Active databases
  • Scientific databases
  • Knowledge databases

36. DESCRIBE CHALLENGES TO DATA MINING REGARDING DATA MINING METHODOLOGY AND USER INTERACTION ISSUES.

  • Mining different kinds of knowledge in databases
  • Interactive mining of knowledge at multiple levels of abstraction
  • Incorporation of background knowledge
  • Data mining query languages and ad hoc data mining
  • Presentation and visualization of data mining results
  • Handling noisy or incomplete data
  • Pattern evaluation

37. DESCRIBE CHALLENGES TO DATA MINING REGARDING PERFORMANCE ISSUES.

• Efficiency and scalability of data mining algorithms

• Parallel, distributed, and incremental mining algorithms

38. DESCRIBE ISSUES RELATING TO THE DIVERSITY OF DATABASE TYPES.

• Handling of relational and complex types of data

• Mining information from heterogeneous databases and global information systems

39. WHAT IS MEANT BY PATTERN?

Pattern represents knowledge if it is easily understood by humans; valid on test data with some degree of certainty; and potentially useful, novel,or validates a hunch about which the used was curious. Measures of pattern interestingness, either objective or subjective, can be used to guide the discovery process.

40. HOW IS A DATA WAREHOUSE DIFFERENT FROM A DATABASE?

Data warehouse is a repository of multiple heterogeneous data sources, organized under a unified schema at a single site in order to facilitate management decision-making.

Database consists of a collection of interrelated data.

PART-B

1. EXPLAIN THE ARCHITECTURE OF DATA WAREHOUSE.

--steps for the design and construction of DW

Top-down view- data source view-data warehouse view-business

query view

--3tier DW architecture

  1. EXPLAIN INDEXING TECHNIQUES OF OLAP DATA, WITH EXAMPLE.

Bitmap indexing and join indexing

  1. EXPLAIN THE OLAP OPERATIONS IN MULTIDIMENSIONAL MODEL?

Roll-up , Drill-down, Slice and dice, Pivot and other OLAP operations.

  1. EXPLAIN STEPS FOR THE DESIGN AND CONSTRUCTION OF DATA WAREHOUSE?
  1. The Design of a Data Warehouse: A Business Analysis Framework
  2. The Process of Data Warehouse Design
  3. A Three-Tier Data Warehouse Architecture
  4. Metadata Repository
  5. Types of OLAP Servers
  1. EXPLAIN IN DETAIL ABOUT DATA WAREHOUSE IMPLEMENTATION
  1. Efficient Computation of Data Cubes
  2. Indexing OLAP Data
  3. Efficient processing of OLAP queries
  1. EXPLAIN FROM DATA WAREHOUSING TO DATA MINING
  1. Data warehouse usage
  2. From On-Line Analytical processing to On-Line Analytical Mining
  1. EXPLAIN IN DETAIL ABOUT MULTIDIMENSIONAL DATA MODEL
  1. From Tables and Spreadsheets to Data Cubes
  2. Stars, Snowflakes, and Fact Constellations
  3. Examples
  4. Measures
  5. Concept hierarchies
  6. OLAP Operations in the Multidimensional Data Model
  7. A Starnet Query Model for Querying Multidimensional Databases

8. EXPLAIN THE VARIOUS DATAMINING ISSUES?

Explain about

  • Knowledge Mining
  • User interaction
  • Performance
  • Diversity in datatypes

9. EXPLAIN THE DATAMINING FUNCTIONALITIES?

The datamining functionalities are:

  • Concept class description
  • Association analysis
  • Classification and prediction
  • Cluster Analysis
  • Outlier Analysis

10. EXPLAIN THE DIFFERENT TYPES OF DATA REPOSITORIES ON WHICH MINING CAN BE PERFORMED?

The different types of data repositories on which mining can be performed

Are:

  • Relational Databases
  • Data Warehouses
  • Transactional Databases
  • Advanced Databases
  • Flat files
  • World Wide Web

11.WHAT IS DATA MINING? EXPLAIN THE STEPS IN KNOWLEDGE DISCOVERY?

Datamining refers to extracting or mining knowledge from large amount of data. The steps in knowledge discovery are:

  • Data cleaning
  • Data integration
  • Data selection
  • Data transformation
  • Data mining
  • Pattern Evolution
  • Knowledge Discovery.

12. EXPLAIN THE EVOLUTION OF DATABASE TECHNOLOGY?

  • Data collection and Database creation
  • Database management systems
  • Advanced database systems
  • Data warehousing and Data Mining
  • Web-based Database systems
  • New generation of Integrated information systems

13.EXPLAIN THE STEPS OF KNOWLEDGE DISCOVERY IN DATABASES?

  • Data cleaning
  • Data integration
  • Data selection
  • Data transformation
  • Data mining
  • Pattern evaluation
  • Knowledge presentation

14. EXPLAIN THE ARCHITECTURE OF DATA MINING SYSTEM?

  • Database, data warehouse, or other information repository
  • Database or data warehouse server
  • Knowledge base
  • Data mining engine
  • Pattern evaluation module
  • Graphical user interface

15. EXPLAIN VARIOUS TASKS IN DATA MINING?

(Or)

EXPLAIN THE TAXONOMY OF DATA MINING TASKS?

_ PREDICTIVE MODELING

• Classification

• Regression

• Time series analysis

_ DESCRIPTIVE MODELING

• Clustering

• Summarization

• Association rules

• Sequence discovery

16. EXPLAIN VARIOUS TECHNIQUES IN DATA MINING?

_ Statistics (or) Statistical perspectives

_ Point estimation

• Data summarization

• Bayesian techniques

• Hypothesis testing

• Correlation

_ Regression

_ Machine learning

_ Decision trees

_ Hidden markov models

_ Artificial neural networks

_ Genetic algorithms

_ Meta learning

UNIT-II

PART-A (2 MARKS)

1. DEFINE RELATIONAL DATABASES.

A relational database is a collection of tables, each of which is assigned a unique name. Each table consists of a set of attributes (columns or fields) and usually stores a large set of tuples(records or rows).Each tuple in a relational table represents an object identified by a unique key and described by a set of attribute values.

2. DEFINE TRANSACTIONAL DATABASES.

A transactional database consists of a file where each record represents a transaction. A transaction typically includes a unique transaction identity number(trans_ID), and a list of the items making up the transaction.

3. WHAT ARE THE STEPS IN THE DATA MINING PROCESS?

a. Data cleaning

b. Data integration

c. Data selection

d. Data transformation

e. Data mining

f. Pattern evaluation

g. Knowledge representation

4. DEFINE DATA CLEANING

Data cleaning means removing the inconsistent data or noise and collecting necessary information

5. DEFINE DATA MINING

Data mining is a process of extracting or mining knowledge from huge amount of data.

6. DEFINE PATTERN EVALUATION

Pattern evaluation is used to identify the truly interesting patterns representing knowledge based on some interesting measures.

7. DEFINE KNOWLEDGE REPRESENTATION

Knowledge representation techniques are used to present the mined knowledge to the user.

8. WHAT IS VISUALIZATION?

Visualization is for depiction of data and to gain intuition about data being observed. It assists the analysts in selecting display formats, viewer perspectives and data representation schema

9. WHAT IS DATA GENERALIZATION?

It is process that abstracts a large set of task-relevant data in a database from a relatively low conceptual to higher conceptual levels

2 approaches for Generalization

1) Data cube approach

2) Attribute-oriented induction approach

10. WHAT IS DESCRIPTIVE AND PREDICTIVE DATA MINING?

Descriptive data mining describes the data set in a concise and summarative manner and presents interesting general properties of the data.

Predictive data mining analyzes the data in order to construct one or set of models and attempts to predict the behavior of new data sets.

11. DEFINE ATTRIBUTE ORIENTED INDUCTION

These method collets the task-relevant data using a relational database query and then perform generalization based on the examination in the relevant set of data.

12. WHAT IS LINEAR REGRESSION?

In linear regression data are modeled using a straight line. Linear regression is the simplest form of regression. Bivariate linear regression models a random variable Y called response variable as a linear function of another random variable X, called a predictor variable.

Y = a + b X

13. STATE THE TYPES OF LINEAR MODEL AND STATE ITS USE?

Generalized linear model represent the theoretical foundation on which linear regression can be applied to the modeling of categorical response variables. The types of generalized linear model are

(i) Logistic regression

(ii) Poisson regression

14. NAME SOME ADVANCED DATABASE SYSTEMS.

Object-oriented databases, Object-relational databases.

15. WHAT IS LEGACY DATABASE?

A Legacy database is a group of heterogeneous databases that combines different kinds of data systems, such as relational or object-oriented databases, hierarchical databases, network databases, spread sheets, multimedia databases or file systems.

16. EXPLAIN AD HOC QUERY TOOL?

A specific kind of end user data access tool that invites the user to form their own queries by directly manipulating relational tables and their joins. Ad Hoc query tools, as powerful as they are, can only be effectively used and understood by about 10% of all the potential end users of a data warehouse.

17. DATA TRANSFORMATION

In Data Transformation, the data are transformed or consolidated into forms appropriate for mining. Data transformation can be involve the following:

  1. Smoothing
  2. Aggregation
  3. Generalization
  4. Normalization
  5. Attribute construction

18. LIST SOME OF THE STRATEGIES FOR DATA REDUCTION

1. Data cube aggregation

2. Attribute subset selection

3. Dimensionality reduction

4. Numerosity reduction

5. Discretization and concept hierarchy generation

19. WHAT IS CLUSTERING?

Clustering is the process of grouping the data into classes or clusters so that objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters.

20. WRITE THE PREPROCESSING STEPS THAT MAY BE APPLIED TO THE DATA FOR CLASSIFICATION AND PREDICTION.

a. Data Cleaning

b. Relevance Analysis

c. Data Transformation

PART-B (16 Marks Questions)

1. EXPLAIN THE DATA PRE-PROCESSING TECHNIQUES IN DETAIL?

Ans:

The data preprocessing techniques are:

  • Data Cleaning
  • Data integration
  • Data transformation
  • Data reduction

2. EXPLAIN THE SMOOTHING TECHNIQUES?

Ans:

  • Binning
  • Clustering
  • Regression

3. EXPLAIN DATA TRANSFORMATION IN DETAIL?

Ans:

  • Smoothing
  • Aggregation
  • Generalisation
  • Normalisation
  • Attribute Construction

4. EXPLAIN NORMALIZATION IN DETAIL?

Ans:

  • Min Max Normalisation
  • Z-Score Normalisation
  • Normalisation by decimal scaling

5. EXPLAIN DATA REDUCTION?

Ans:

  • Data cube Aggretion
  • Attribute subset Selection
  • Dimensional reduction
  • Numerosity reduction

6. EXPLAIN DATAMINING PRIMITIVES?

Ans:

There are 5 Data mining Primitives.They are:

  • Task relevant data
  • Kinds of knowledge to be mined
  • Concept Hierarchies
  • Interesting Measures
  • Knowledge Presentation and Visualization Technique to be used for Discovery patterns

7. EXPLAIN PARAMETRIC METHODS AND NON-PARAMETRIC METHODS OF REDUCTION?

Ans:

Parametric Methods:

  • Regression Model
  • Log linear Model

Non-Parametric Methods

  • Sampling
  • Histogram
  • Clustering

8. EXPLAIN DATA DISCRETIZATION AND CONCEPT HIERARCHY GENERATION?

Ans:

Discretization and concept hierarchy generation for numerical data:

  • Segmentation by natural partitioning
  • Binning
  • Histogram Analysis
  • Cluster Analysis

UNIT-III

PART-A (2 MARKS)

1. WHAT IS ASSOCIATION RULE?

Association rule finds interesting association or correlation relationships among a large set of data items which is used for decision-making processes. Association rules analyzes buying patterns that are frequently associated or purchased together.

2. DEFINE SUPPORT.

Support is the ratio of the number of transactions that include all items in the antecedent and consequent parts of the rule to the total number of transactions. Support is an association rule interestingness measure.

3. DEFINE CONFIDENCE.

Confidence is the ratio of the number of transactions that include all items in the consequent as well as antecedent to the number of transactions that include all items in antecedent. Confidence is an association rule interestingness measure.

4. HOW IS ASSOCIATION RULES MINED FROM LARGE DATABASES?

Association rule mining is a two-step process.

  • Find all frequent itemsets.
  • Generate strong association rules from the frequent itemsets.

5. WHAT IS THE CLASSIFICATION OF ASSOCIATION RULES BASED ON VARIOUS CRITERIA?

1. Based on the types of values handled in the rule.

a. Boolean Association rule.

b. Quantitative Association rule.

2. Based on the dimensions of data involved in the rule.