CS6400 - Final Exam Complete Questions And 100% Verified Answers Graded A+

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CS6400 - Final Exam Complete
Questions And 100% Verified Answers
Graded A+


Waste of storage space - {✔✔Resolution Result Verified Answers ✔✔}-due
to NULLs and the difficulty of performing selections, aggregation operations, and
joins due to NULL values



A functional dependency (A->B) - {✔✔Resolution Result Verified Answers
✔✔}-occurs when the attribute A uniquely determines B. A = left-hand side,
B=right-hand-side.
-is a property of the semantics or meaning of the attributes.



FD or f.d. - {✔✔Resolution Result Verified Answers ✔✔}-abbreviation for
functional dependency


Candidate Key - {✔✔Resolution Result Verified Answers ✔✔}-An attribute,
or combination of attributes, that uniquely identifies a row in a relation.



legal relation states (or legal extensions) of R - {✔✔Resolution Result Verified
Answers ✔✔}-Relation extensions r(R) that satisfy the functional dependency
constraints

,FD cannot be inferred automatically - {✔✔Resolution Result Verified Answers
✔✔}-must be defined explicitly by someone who knows the semantics of the
attributes of R.

Bitmap indexing - {✔✔Resolution Result Verified Answers ✔✔}-another
important type of indexing
used for querying by multiple keys and is particularly applicable on fields with a
small number of unique values.



Grid files - {✔✔Resolution Result Verified Answers ✔✔}-organize data into
buckets along multiple dimension



The number of blocks needed for the file - {✔✔Resolution Result Verified
Answers ✔✔}-is b = ⎡(r/bfr)⎤, r - number of records, bfr - blocking factor



The reflexive rule (IR1) - {✔✔Resolution Result Verified Answers ✔✔}-if Y is
part of X, then X →Y. . can also be stated as X → X; that is, any set of attributes
functionally determines itself.



The augmentation rule (IR2) - {✔✔Resolution Result Verified Answers ✔✔}-
adding the same set of attributes to both the left- and right-hand sides of a
dependency results in another valid dependency. can also be stated as
X → Y |= WX → WY



IR3 - {✔✔Resolution Result Verified Answers ✔✔}-functional dependencies
are transitive

,What is the blocking factor? - {✔✔Resolution Result Verified Answers ✔✔}-
maximum number of records which can be placed in one block.
=block size/size of each record



How many blocks is track consist of? - {✔✔Resolution Result Verified Answers
✔✔}-track size/block size


How many unspanned records can be stored in one track of this file? -
{✔✔Resolution Result Verified Answers ✔✔}-number of blocks * blocking
factor


How much time would it take, on the average, to read one track worth of data
from disk to main memory (RAM) at one time? - {✔✔Resolution Result Verified
Answers ✔✔}-number of blocks*transfer time per block + average seek time +
average rotational delay


How many buckets would be needed to store the unspanned records if each
bucket consists of 5 consecutive blocks? - {✔✔Resolution Result Verified
Answers ✔✔}-Each block can fit 66 records.
A bucket contains 5 blocks and if only 80% full in a bucket, we can only use 4
blocks, because 5 * 0.8 = 4
Now, each bucket can fill 66 * 4 = 264 records.
So at the end, 132,000,000 records would result in 132,000,000 (records) / 264
(records/bucket) = 500,000 buckets.



closure - {✔✔Resolution Result Verified Answers ✔✔}-To determine the
closure we should go through all dependencies in the given set of F and add to

, the starting set of attributes all those which are derived from it using each of the
dependencies.



determinant - {✔✔Resolution Result Verified Answers ✔✔}-a set of
attributes on which some other attribute is fully functionally dependent.



secondary memory - {✔✔Resolution Result Verified Answers ✔✔}-DISK, is
permanent, slow, big and cheap



lookup time - {✔✔Resolution Result Verified Answers ✔✔}--N/2 for linear
search, where N = #data blocks
N/2 * 0.01s (default)
-log2(N) for binary search * 0.01s



lookup time with primary index - {✔✔Resolution Result Verified Answers
✔✔}--log2(n) + 1 where n = #index blocks
-sparse: log2(#data blocks/fanout)+1*0.01s
-dense: log2(#records/fanout)+1)*0.01s



lookup time with secondary index - {✔✔Resolution Result Verified Answers
✔✔}--log2(n) + 1 where n = #index blocks
-dense: log2(#records/fanout)+1)*0.01s



lookup time with multi-level index - {✔✔Resolution Result Verified Answers
✔✔}--logfanout(n)+1