Data Mining and Data Warehousing Introduction to Data Mining

Similar documents
DATA MINING TRANSACTION

CS377: Database Systems Data Warehouse and Data Mining. Li Xiong Department of Mathematics and Computer Science Emory University

Data Warehouse and Mining

Question Bank. 4) It is the source of information later delivered to data marts.

Chapter 1, Introduction

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

D B M G Data Base and Data Mining Group of Politecnico di Torino

Data mining fundamentals

DATA WAREHOUING UNIT I

Chapter 28. Outline. Definitions of Data Mining. Data Mining Concepts

SCHEME OF COURSE WORK. Data Warehousing and Data mining

Time: 3 hours. Full Marks: 70. The figures in the margin indicate full marks. Answers from all the Groups as directed. Group A.

TIM 50 - Business Information Systems

Data warehouse and Data Mining

TIM 50 - Business Information Systems

DATA WAREHOUSING IN LIBRARIES FOR MANAGING DATABASE

Data Warehouse and Data Mining

Data Warehousing ETL. Esteban Zimányi Slides by Toon Calders

COMP 465 Special Topics: Data Mining

Table Of Contents: xix Foreword to Second Edition

Data Mining & Data Warehouse

Data Mining Concepts

Tribhuvan University Institute of Science and Technology MODEL QUESTION

Database and Knowledge-Base Systems: Data Mining. Martin Ester

R07. FirstRanker. 7. a) What is text mining? Describe about basic measures for text retrieval. b) Briefly describe document cluster analysis.

Contents. Foreword to Second Edition. Acknowledgments About the Authors

Introduction to Data Mining S L I D E S B Y : S H R E E J A S W A L

Knowledge Discovery and Data Mining

Data Warehouse and Data Mining

On-Line Application Processing

Data Warehousing. Adopted from Dr. Sanjay Gunasekaran

SIDDHARTH GROUP OF INSTITUTIONS :: PUTTUR Siddharth Nagar, Narayanavanam Road QUESTION BANK (DESCRIPTIVE)

1 DATAWAREHOUSING QUESTIONS by Mausami Sawarkar

The Definitive Guide to Preparing Your Data for Tableau

Data Mining. Chapter 1: Introduction. Adapted from materials by Jiawei Han, Micheline Kamber, and Jian Pei

Data Warehouse Testing. By: Rakesh Kumar Sharma

A Star Schema Has One To Many Relationship Between A Dimension And Fact Table

Big Data Analytics: What is Big Data? Stony Brook University CSE545, Fall 2016 the inaugural edition

Applications and Trends in Data Mining

Data Mining and Warehousing

Warehousing. Data Mining

DATA MINING II - 1DL460

Dr.G.R.Damodaran College of Science

DATA WAREHOUSING AND MINING UNIT-V TWO MARK QUESTIONS WITH ANSWERS

This tutorial has been prepared for computer science graduates to help them understand the basic-to-advanced concepts related to data mining.

DATA MINING II - 1DL460

DATA MINING AND WAREHOUSING

Extended TDWI Data Modeling: An In-Depth Tutorial on Data Warehouse Design & Analysis Techniques

Applying big data analytics in practice

CHAPTER 3 Implementation of Data warehouse in Data Mining

This tutorial will help computer science graduates to understand the basic-to-advanced concepts related to data warehousing.

Oracle Database 11g: Data Warehousing Fundamentals

GUJARAT TECHNOLOGICAL UNIVERSITY MASTER OF COMPUTER APPLICATIONS (MCA) Semester: IV

Data Mining Concepts. Duen Horng (Polo) Chau Assistant Professor Associate Director, MS Analytics Georgia Tech

A Novel Approach of Data Warehouse OLTP and OLAP Technology for Supporting Management prospective

INTRODUCTION TO DATA MINING

International Journal of Advance Engineering and Research Development. A Survey on Data Mining Methods and its Applications

Lectures for the course: Data Warehousing and Data Mining (IT 60107)

INSTITUTE OF AERONAUTICAL ENGINEERING (Autonomous) Dundigal, Hyderabad

Handout 12 Data Warehousing and Analytics.

Department of Information Technology B.E/B.Tech : CSE/IT Regulation: 2013 Sub. Code / Sub. Name : CS6302 Database Management Systems

1. Inroduction to Data Mininig

Databases and Data Warehouses

Data mining overview. Data Mining. Data mining overview. Data mining overview. Data mining overview. Data mining overview 3/24/2014

UNIT -1 UNIT -II. Q. 4 Why is entity-relationship modeling technique not suitable for the data warehouse? How is dimensional modeling different?

CT75 DATA WAREHOUSING AND DATA MINING DEC 2015

Lecture 18. Business Intelligence and Data Warehousing. 1:M Normalization. M:M Normalization 11/1/2017. Topics Covered

Data Analysis and Data Science

Data Mining Course Overview

Department of Industrial Engineering. Sharif University of Technology. Operational and enterprises systems. Exciting directions in systems

Winter Semester 2009/10 Free University of Bozen, Bolzano

Processing Techniques. Chapter 7: Design and Development and Evaluation of Systems. Online Processing. Real-time Processing


Study on the Application Analysis and Future Development of Data Mining Technology

After completing this course, participants will be able to:

The General Equivalence Mappings. GEM Files Summary Sheet

Data Mining. Introduction. Hamid Beigy. Sharif University of Technology. Fall 1395

1 Dulcian, Inc., 2001 All rights reserved. Oracle9i Data Warehouse Review. Agenda

Introduction to Data Mining

Summary of Last Chapter. Course Content. Chapter 2 Objectives. Data Warehouse and OLAP Outline. Incentive for a Data Warehouse

CS423: Data Mining. Introduction. Jakramate Bootkrajang. Department of Computer Science Chiang Mai University

Data Management Lecture Outline 2 Part 2. Instructor: Trevor Nadeau

Full file at

UNIVERSITY OF BOLTON CREATIVE TECHNOLOGIES COMPUTING PATHWAY SEMESTER TWO EXAMINATION 2014/2015 ADVANCED DATABASE SYSTEMS MODULE NO: CPU6007

Data Mining. Introduction. Hamid Beigy. Sharif University of Technology. Fall 1394

IT DATA WAREHOUSING AND DATA MINING UNIT-2 BUSINESS ANALYSIS

Thanks to the advances of data processing technologies, a lot of data can be collected and stored in databases efficiently New challenges: with a

Data Warehouse and Data Mining

Jarek Szlichta

Overview. Introduction to Data Warehousing and Business Intelligence. BI Is Important. What is Business Intelligence (BI)?

No. of Printed Pages : 7 MBA - INFORMATION TECHNOLOGY MANAGEMENT (MBAITM) Term-End Examination December, 2014

UNIT -1 UNIT -II. Q. 4 Why is entity-relationship modeling technique not suitable for the data warehouse? How is dimensional modeling different?

Knowledge Modelling and Management. Part B (9)

Data Analytics at Logitech Snowflake + Tableau = #Winning

Management Information Systems MANAGING THE DIGITAL FIRM, 12 TH EDITION FOUNDATIONS OF BUSINESS INTELLIGENCE: DATABASES AND INFORMATION MANAGEMENT

Data Mining. Ryan Benton Center for Advanced Computer Studies University of Louisiana at Lafayette Lafayette, La., USA.

DATA WAREHOUSE EGCO321 DATABASE SYSTEMS KANAT POOLSAWASD DEPARTMENT OF COMPUTER ENGINEERING MAHIDOL UNIVERSITY

SAP CERTIFIED APPLICATION ASSOCIATE - SAP HANA 2.0 (SPS01)

Benefits of Automating Data Warehousing

by Prentice Hall

Transcription:

Data Mining and Data Warehousing Introduction to Data Mining Quiz Easy Q1. Which of the following is a data warehouse? a. Can be updated by end users. b. Contains numerous naming conventions and formats. c. Organized around important subject areas. d. Contains only current data. Q2. An operational system is which of the following? a. A system that is used to run the business in real time and is based on historical data. b. A system that is used to run the business in real time and is based on current data. c. A system that is used to support decision making and is based on current data. d. A system that is used to support decision making and is based on historical data. Q3. A goal of data mining includes which of the following? a. To explain some observed event or condition b. To confirm that data exists c.to analyze data for expected relationships d. To create a new data warehouse Q4. The @active data warehouse architecture includes which of the following? a. At least one data mart b. Data that can extracted from numerous internal and external sources c. near real-time updates.

Q5. Which of the following is data scrubbing? a. A process to reject data from the data warehouse and to create the necessary indexes b. A process to load the data in the data warehouse and to create the necessary indexes c. A process to upgrade the quality of data after it is moved into a data warehouse d. A process to upgrade the quality of data before it is moved into a data warehouse Q6. Which of the following types of tables is a snowflake schema? a. Fact b. Dimension c. Helper Q7. Which of the following includes the generic two-level data warehouse architecture? a. At least one data mart b. Data that can extracted from numerous internal and external sources c. Near real-time updates. Q8. Which of the following are fact tables? a. Completely de-normalized b. Partially de-normalized c. Completely normalized d. Partially normalized Q9. which of the following includes data transformation? a. A process to change data from a detailed level to a summary level b. A process to change data from a summary level to a detailed level c. Joining data from one source into various sources of data d. Separating data from one source into various sources of data

Q10. Which of the following is a reconciled data? a. Data stored in the various operational systems throughout the organization. b. Current data intended to be the single source for all decision support systems. c. Data stored in one operational system in the organization. d. Data that has been selected and formatted for end-user support applications. Q11. Which of the following is not a data mining task? a. Frequent pattern mining c. Data warehousing d. Clustering Q12. Data mining can be applied to which type of data? a. Transactional data b. Multimedia data c. Web data Medium Q1. What data mining task will be performed to clean the data? d. Frequent Pattern Mining Q2. What data mining task will be performed for grouping similar data>

d. Frequent Pattern Mining Q3. Which data mining task is performed for identifying new data based on previous data? d. Frequent Pattern Mining Q4. Which data mining task is performed for getting interesting trends in data? d. Frequent Pattern Mining Q5. Which data mining task is performed for summarizing data in graphical form? a. Data Visualization Q6. Which data mining task is performed for predicting stock market trends? a. Data Visualization Q7. Data mining is useful for? a. Data scientist b. Market trend analyst c. Both a and b d. Neither a nor b

Q8. Data mining is useful for? a. Supermarket chain b. Stock Market c. Spam detection Q9. What type of data mining task will a market analyst use for predicting future market trend? a. Pre-processing Q10. What type of data mining task will a spam detector use? a. Regression b. Pre-processing d. Clustering Q11. What type of data mining task will a data scientist use for describing the company performance? a. Data Visualization c. Regression Q12. What type of data mining task will be used by a supermarket chain to identify customer purchase trends? a. Regression b. Classification

d. Frequent Pattern Mining Hard Q1. What are the privacy issues of data mining? a. Confidentiality of data should be maintained b. User anonymity should be provided c. Controlled access of data should be performed Q2. Why is data mining required for biological data? a. To aid in medical diagnosis b. To find disease patterns c. Both a and b Q3. Why is data mining required for multimedia data? a. To help in understanding the different types of multimedia data b. To get a collection of multimedia data c. Because multimedia data has no content Q4. What are the issues in mining multimedia data? a. Size of the data is large b. Curse of dimensionality c. Both a and b Q5. What are the issues in mining biological data? a. Curse of dimensionality b. Large size of data c. Privacy issues of biological data

Q6. What are the issues in mining stream data? a. Small data size b. Dynamic in nature c. All of the above