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CS511 Advanced Database Systems
Fall 2009, Prof. Chang
Department of Computer Science
University of Illinois at Urbana-Champaign
Midterm Examination 2
November 18, 2009
Time Limit: 75 minutes
• Print your name and NetID below. In addition, print your NetID in the upper right
corner of every page.
Name: NetID:
• Including this cover page, this exam booklet contains 7 pages. Check if you have
missing pages.
• The exam is open book and open notes (any and all books/notes). Scientific calculators
of any kinds are allowed. No other electronic devices are permitted. Any form of
cheating on the examination will result in a zero grade.
• Please write your solutions in the spaces provided on the exam. You may use the blank
areas and backs of the exam pages for additional space or scratch work.
• Please make your answers clear and succinct; you will lose credit for verbose, convo-
luted, or confusing answers. Simplicity does count!
• Each problem has different weight. You should look through the entire exam before
getting started, to plan your strategy.
• Problems that are related to homework or study-guide problems (e.g., in terms of
concepts covered) are marked with [HW ].
Problem 1 2 3 Total
Points 52 20 28 100
Score
Grader
Problem 1 (52 points) Misc. Concepts
For each of the following statements:
- for true/false choices, indicate whether it is TRUE or FALSE by circling your choice, and provide an explanation to justify;
- for short answer questions, provide a brief answer with clear explanation.
You will get 4 point for each correct answer with correct explanations, and no penalty (of negative points) for wrong answers.
(1) T rue F alse [HW ] Predicate calculus is more high level than relational algebra. ⇒ Explain:
(2) T rue F alse [HW ] When deleting a node from an R-tree, reinsertion is chosen as a way to deal with “orphaned entries” because merging (as in B-tree) is infeasible for R-tree. ⇒ Explain:
(3) T rue F alse We can use R-tree to index multiple-attribute data items like (salary, age), but the indexing will not be effective. ⇒ Explain:
(4) T rue F alse [HW ] If a transaction releases its read lock before the end of the transaction, there is a danger of cascading rollback. ⇒ Explain:
(5) T rue F alse [HW ] Precision and recall as two major IR metrics were coined in the SMART project in 1960’s. ⇒ Explain:
(6) T rue F alse [HW ] In the discrimination value model, the value of an index term is based on its ”discrimination value”—which is predicted by its IDF. ⇒ Explain:
Problem 2 (20 points) PageRank [HW ]
This problem will exercise your insight for the notion of PageRank. Consider each of the following graphs representing the Web, where nodes represent pages and directed edges hyperlinks. For simplicity, let’s use the same simplified PageRank definition as given in class.
Our question will be based on the following graph, which we call Circular, as the starting point, upon which we will make some changes.
a
b
c d
e
f
Part 1(8 points)
For the following Web graph, which is a slight change to Circular, speculate what would be the relative PageRank for each page by identifying pages with non-zero PageRank and their rank ratios, and explain why. Note that, we ask you to only speculate intuitively– without performing iterative fixpoint computation. If there is no clear intuition to speculate, state so and explain why.
a
b
c d
e
f
Part 2(12 points)
Can you change the graph Circular, so that some nodes have twice the PageRank of others? Is this possible? If so, propose minimal change to achieve this. Explain why.
Part 2(18 points)
Let’s continue such generalization for different types of data. Consider data type S as a set of integers, e.g., s1 = {1, 2, 6, 8}, s2 = {-1, 4, 15}, s3 = { 20 }, s4 = {48, 60, 102}.
Sketch, concisely, your design of an index tree for such data, by further generalizing the concepts of R-tree. Describe your design clearly—what types of queries are reasonable to support, what each node means, how to split a node, and how to perform search for a query.
