DATA WAREHOUSING AND DATA MINING

Unit 1

Q1: What is Data Warehouse? Discuss the components. What are the differences between Operational Database and Data Warehouse? Q2: With a neat sketch, explain the architecture of a data warehouse. Compare two-tier architecture and three-tier client server architecture.Q3: What is OLAP? Discuss the typical OLAP operations with an example.Q4: What is Data Mart? Discuss different types of Data Marts. How data Marts are different from Data Warehouse?Q5: What is Meta Data? Why is it important? Discuss multidimensional data.Q6: What is Online Transaction Processing? Describe the evolution of OLTP. What are the critical features of OLTP systems?Q7: Compare and contrast two basic approaches to building a data warehouseQ8: What are the differences between the three main types of data warehouse usage: information processing, analytical processing and data mining? Discuss the motivation behind OLAP mining.Q9: “A data mart is a subsection of a data warehouse that deals with specific information”. List the advantages and disadvantages of using data mart over data warehouseQ10: Suppose that a data warehouse consists of three dimensions time, doctor and patient and the two measures count and charge, where the charge is the fee that a doctor charges a patient for a visit.Draw a schema diagram for the above data warehouse using Snowflake schemaQ11: Describe the similarities and dissimilarities between Database and Data warehouseQ12: Give an example where application of data mining can be crucial to the success of business.Q13: What are the responsibilities of Data Warehouse Manager? Explain Q14: What do you understand by Data Granularity? Discuss with exampleQ15: What are Schemas? Discuss various Schemas used in Data Warehouse.Q16: What do you understand By Virtual Data Warehouse? Also, discuss the concept of Distributed Data Warehouse. Q17: Discuss OLAP, ROLAP, and MOLAP.

Unit 2Q1: What is Data Mining? Describe the steps involved in data mining when viewed as a process of knowledge discovery.Q2: “Binning can be used for Data Reduction and Discretization”. Support this statement with an example.Q3: What is Noise? What techniques are used to remove noise while building a data warehouse? Explain briefly.Q4: Write different approaches to Data Transformation.Q5: What are various goals of Data Mining? Explain the various tools and techniques of Data Mining.

Q6: List and explain the five primitives for specifying a data mining task.Q7: Describe the differences between the following approaches for the integration of a data mining system with a database: no coupling, loose coupling, semi tight coupling and tight coupling. Q8: Discuss the major issues in Data Mining.Q9: Discuss the architecture of a typical Data Mining system with diagram.Q10: Explain Discretization and Concept Hierarchy with example.Q11: What do you understand by Data Reduction? Explain the various strategies involved in it.

Unit 3Q1: Explain the algorithm for constructing a decision tree from training samples.Q2: What is Bayesian Classification? How it classifies the input data? Explain Bayes theorem.Q3: What are supervised and unsupervised Learning? Why Clustering is known as unsupervised Learning?Q4: Write and explain the algorithm for mining frequent item sets without candidate generation. Give relevant example.Q5: Discuss the approaches for mining multi level association rules from the transactional databases. Give relevant example.Q6: How does Clustering different from Classification?Q7: What are Classification Rules? How they are associated with Decision Tree?Q8: Define Association Rule Mining. How market basket analysis forms the association rule?Q9: Briefly outline how to compute the dissimilarity between objects described by following types of variables:(i) Numerical Variables(ii) Ratio-scaled variables(iii) Categorical Variables(iv) Asymmetric Variables

Q10: What is Outlier Analysis? Why it is important?Q11: Explain categorization of major Clustering methods.Q12: Discuss K-MEANS and K-MEDOIDS ALGORITHMS.Q13: Numericals on Apriori Algorithm and Decision TreeQ14: Discuss the various categories of Clustering.Q15: Discuss the approaches for mining multidimensional association rule from data warehouseQ16: Write the Apriori algorithm for generating the frequent item sets.

UNIT 4Q1: What is a multimedia database? Explain the methods of mining multimedia database?Q2: Explain

(i) Mining Time Series and Sequence data

(ii) Spatial Databases(iii) Descriptive Mining of Complex Data Objects(iv) Applications in Data Mining(v) Trends in Data Mining

Q3: What kinds of Associations can be mined in Multimedia Data? Explain.Q4: Discuss the concept of Audio and Video Mining.