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2003 Course Advanced Databases (Elective I)

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Total No. of Questions : 12] [Total No. of Pages : 4 [3864] - 406 P 1102 B.E. (Computer Engg.) ADVANCED DATABASES (2003 Course) (Elective - I) (410445) Time : 3 Hours] [Max. Marks : 100 Instructions to the candidates: 1) Answers to the two sections should be written in separate books. 2) Neat diagrams must be drawn wherever necessary. 3) Assume suitable data, if necessary. 4) Section I : Q.1 or Q.2, Q.3 or Q.4, Q.5 or Q.6. 5) Section II : Q.7 or Q.8, Q.9 or Q.10, Q.11 or Q.12. SECTION - I Q1) a) b) c) Explain Parallel Hash Join with suitable example. Explain design issues in Parallel Database System. Explain any two Parallel Database Architectures. [5] [6] [6] OR Q2) a) b) Q3) a) b) c) Evaluate how well partitioning techniques support the following types of data access [9] i) Scanning the entire relation. ii) Locating tuple associatively. iii) Locating all tuples such that the value of given attribute lies within a specified range. Write a short note on [8] i) Cash Coherency Protocol. ii) Parallel Query Optimization. If we are to ensure atomicity, all the sites in which a transaction T executed must agree on the final outcome of the execution T must either commit at all sites, or it must abort at all sites. Describe the technique or protocol used to ensure this property in detail. [7] Write a short note on Persistent Messaging in Distributed Transaction Processing. [5] Explain Heterogeneous distributed databases. [5] OR P.T.O. Q4) a) b) c) Q5) a) b) Define semi-join. Compute semi-join r s for the relations r and s. [7] Relation r Relation s A B C C D E 1 2 3 3 4 5 4 5 6 3 6 8 1 2 4 2 3 2 5 3 2 1 4 1 8 9 7 1 2 3 Describe the voting and read-any-write-all approaches to synchronous replication. [5] Explain Optimistic methods for Distributed Concurrency Control. [5] Consider following DTD for bibliography [12] <?xml version= 1.0 encoding = UTF-8 ?> <!ELEMENT bib (book*)> <!ELEMENT book (title, (author+ | editor+), publisher, price)> <!ATTLIST book year CDATA #REQUIRED> <!ELEMENT author (last, first)> <!ELEMENT editor (last, first, affiliation)> <!ELEMENT title (#PCDATA)> <!ELEMENT last (#PCDATA)> <!ELEMENT first (#PCDATA)> <!ELEMENT affiliation (#PCDATA)> <!ELEMENT publisher (#PCDATA)> <!ELEMENT price (#PCDATA)> Create XML document, XML Schemas and solve the following queries in XQuery on the bibliography fragment. i) List books published by Addison-Wesley after 1991, including their year and title. ii) Find pairs of books that have different titles but the same set of authors (possibly in a different order). iii) For each book in the bibliography, list the title and authors, grouped inside a result element. Explain advantages and disadvantages of the Web-DBMS approach.[4] OR [3864] - 406 -2- Q6) a) b) Consider following DTD for bid <?xml version = 1.0 encoding = UTF-8 ?> <!ELEMENT bids (bid_tuple*)> <!ELEMENT bid_tuple (userid, itemno, bid, bid_date)> <!ELEMENT userid (#PCDATA)> <!ELEMENT itemno (#PCDATA)> <!ELEMENT bid (#PCDATA)> <!ELEMENT bid_date (#PCDATA)> Create XML document, XML Schemas and solve the following queries in XQuery on the bibliography fragment. i) List the item number and description of the item(s) that received the largest number of bids, and the number of bids it (or they) received. ii) List item numbers and average bids for items that have received three or more bids, in descending order by average bid. [8] Describe the various issues for efficient evaluation of XML Queries.[8] SECTION - II Q7) a) b) c) Explain Data Reduction and Data Decretization preprocessing in Data Warehouse. [5] Explain cube and rollup extended aggregation operation with suitable example. [5] Explain Kimball s nine steps design for Data Warehouse. [7] OR Q8) a) b) Q9) a) Explain different conceptual schemas design for data warehouse with suitable example. [10] Explain different indexing techniques in Data Warehouse. [7] Consider following training set [12] Outlook Temperature Humidity Wind Class Attribute Sunny Hot High False N Sunny Hot High True N Overcast Hot High False P Rain Mild High False P Rain Cool Normal False P Rain Cool Normal True N Overcast Cool Normal True P Sunny Mild High False N Sunny Cool Normal False P Rain Mild Normal False P Sunny Mild Normal True P Overcast Mild High True P Overcast Hot Normal False P Rain Mild High True N Write ID3 Classification algorithm. Construct a decision tree based on above training set using ID3. [3864] - 406 -3- b) Q10)a) b) Q11)a) b) Q12)a) b) Write a short note on Text Mining. OR [5] Consider following data set [9] Object Attribute 1 Attribute 2 Attribute 3 A 1 1 2 B 3 2 4 C 3 4 6 D 4 6 3 Write K-means clustering algorithm. Find the cluster for the objects in data set with K = 2. Consider following training data set [8] Age Income Student Credit_rating Buys_Computer <=30 high no fair no <=30 high no excellent no 31...40 high no fair yes >40 medium no fair yes >40 low yes fair yes >40 low yes excellent no 31...40 low yes excellent yes no <=30 medium no fair <=30 low yes fair yes yes >40 medium yes fair <=30 medium yes excellent yes yes 31...40 medium no excellent 31...40 high yes fair yes >40 medium no excellent no Write Na ve Bayesian Classifier algorithm. Consider Buys_Computer as a Class Attribute with values yes and no classes. Find the class label for data sample X = (age <=30, Income = medium, Student = yes Credit_rating = Fair) using Na ve Bayesian Classifier. Define Information Retrieval System. Describe how it is differ from database system. [6] Write short notes on [10] i) Signature Files. ii) Ranking Document Similarity. OR Explain any two techniques that support the evaluation of Boolean and Ranked queries. [6] Write short notes on [10] ii) Precision and Recall. i) Web Crawler. [3864] - 406 -4-

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