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84. Differentiate between OLTP and OLAP.
85. What is a data mart? How is it different from a data warehouse?
86. What is metadata? How is it useful?
87. Differentiate between drill down and roll up analyses with reference to data
Differentiate between ROLAP and MOLAP. List out their relative
advantages and limitations.
89. Differentiate between star and snowflake schema models. Give an example
90. Write short notes on:
(a) Data warehousing
(b) Data mart
91. Write short notes on:
(a) OLTP versus OLAP
(b) ROLAP versus MOLAP
(d) Data mining
(c) Star versus snowflake schema
(d) Drill down versus roll up analysis
92. What is data mining? How is it useful? Explain with an example.
Data mining is defined as the nontrivial process of extracting implicit,
previously unknown, and potentially useful information from data. Explain the
meaning of the key terms used in this definition.
94. In data mining, different types of algorithms are needed to extract different
kinds of patterns. Briefly describe some of the most popular classes of algorithms
used for data mining.
Give an example each to explain the concept of classification, clustering,
association, and trend analysis used in data mining.
96. Briefly describe the following techniques used in data mining:
(d) Fuzzy sets
(b) Machine learning
(e) Neural networks (c) Database-oriented
(f) Visual exploration
97. Differentiate between data warehousing and data mining.
98. Explain how is data mining different from OLAP.
99. Write short notes on:
(a) Data mining techniques
(b) Data mining versus OLAP
(c) Classes of data mining algorithms
(d) Data mining and data warehousing
100. List out some of the commonly supported security features in a database
101. What is a client-server database system? Explain the roles of the database
server and database clients in such a system. List out some of the advantages of a
client-server database system over conventional database systems.
102. What is a web client in a client...
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This document was uploaded on 04/07/2014.
- Spring '14