- · A collection of data arranged for ease and speed of search and retrieval. - database
- · Raw material, from which you can draw conclusions - data
- · Knowledge, intelligence, a particular piece of data with a special meaning or function. - Information
List
specific data that the school collects about each student. What information
could be gathered using all the student data?
·
Score in individual subjects >
class average in individual subject / average as a whole > school average in
individual subject / in all subjects
·
Address of students > maximum
students from each state
And so on
a. Write examples of data and information related to any of the sites.
Database schema of imdb may look like i.imgur.com/pDq0n.png
Say there is a TV series called Mr. Robot
In the title table there may be a row which has title
of an episode, season id , episode no and lot hell of data, but it is of no use
without a proper query made to all tables
On the other hand if I query for keyword, say episode
5 season 2 , I will get what exactly happened in this season, what was the
audience rating and so on, this might be an information worth reading for me.
a. State why you believe data collections of this type
could be important.
Information is something of importance, to spend my
next 45 minutes watching a TV episode which is crap ( I don’t want that) – above
mentioned data collection just helped me, since data is arranged in a
structured manner, which may be queried to give useful information for me.
3. Choose a website that displays data that might be
stored in a database; find 10 data items on the site and list 5 pieces of
information that could be gleaned from the data.
Let’s take example of auctions.godaddy.com , each row in each table
mentioned below is data which has no value for me individually:
I.
There
might be a table called auction Table with a serial id as primary key, domain id
being referred from domain table as foreign key , a seller id mapped to another
table as foreign key , price and so on
II.
There
may be another table with seller details which are also referred in table in
point 1.
III.
There
might be a table which has traffic details, each visit as a row, and a column
called domain id which is mapped to domain table as foreign key
IV.
The
domain table might have domain id referred in point 1 and point 3, which is
also the primary key. There might be a value column being updated by nightly or
weekly evaluation jobs running on the backend, there might be a name field
which also will be unique key. There might be also a tld column to group them
by tld id, tld id may be a foreign key to TLD table.
V.
There
might be a TLD table with id as primary key referred in point 4 as foreign key.
TLD name which may have unique key constraint, also a column to specify whether
godaddy supports direct transfer or not (True/false)
VI.
There
might be a Bids table, with bdId as primary key, a foreign key reference to
auctionTable to identify for which auction entry this bid was made, To identify
type of bid there might be a bidcategoryId, which may be a foreign key to
Bidcategory Table. There might be expireOn column also, if bidcategoryId
referes to buy now, it might be set to some default value.
VII.
There
will be a BidCategory Table which might have data like fixed bid, make offer,
buy now and so on
VIII.
There
might be table called featuredAuction, it could have ids referred as foreign
key from auction table, start and end time of being featured.
IX.
There
might be a currency table to identify, the seller preffered currency details,
which might have foregn key reference in point 2 . Say a seller row in point
two refers to currency with id 10, it might be named as USD, and signature as $
X.
There might be a table to identified types of
bids allowed for an auction entry in point 1. Table in point 6 identifies what
buyer selected, but to identify what all options a an auction entry offer,
there might be a table which has each row as auctionId, AllowedBidType
And so on
list 5 pieces of
information that could be gleaned from the data:
I.
Say I
want to buy a domain which starts with “hemant” and is available under auction,
I might several joins in the query to get this information from above tables.
II.
Say I
want to buy a domain with minimum price as $2 or max price as $100, I might
extract this information from above tables rows.
III.
Say I
want a domain with exactly 5 characters, this information is possible to be
extracted.
IV.
Say I
want to buy a domain with traffic more than 100, this information can be
extracted here
V.
Say I
want to buy a domain with highest no of bids ( which I think make to valuable),
I may extract this information from above tables.
And so on
4. Give examples of how data becomes information for these
two industries:
• film/movie
Example: Some Entity
storing a data row like, personId, MovieId
, role id is of no use to me at first look, but when queried using the
right joins, it can tell me casting information of a movie and I can choose
what movie I want to see, based on what Star character I like.
• hospital/healthcare
Example: Some Entity
in hospital database may say, personId, VisitDate, MedicationId
It is of no use to
doctor, but if queried in right way, it can tell doctor that, antiviral course
of this patient is already complete and no need to give more anti-viral
medicine. This is the real information which saves patient from overdose.
5. Using
this Lunch Room Data Report, answer the questions that follow.
a. What does this report mean?
If I sum up first two columns vs last three columns, - they are equal,
so, I got to know:
·
Date wise, it is trying to record sales, how many
items sold to student, how many to Faculty.
·
Date wise, it wants to store, sale of each type of store
available over the counter in a festival for fund raising.
b. What data was collected?
See above
c. What information does this table provide from the data
collected?
See above
d. How do you think this information is used by those
reading the report?
See above
e. Generate at least two conclusions based on the data
provided.
See above
f. Generate at least two questions that
you would ask about the data provided.
·
On a day say, 4 Dec 2003, Pizza bar
sold 126 items, out of these 126, how many sold to students?
·
On a day say, 6 Dec 2003, Soup/Salad
Bar sold 30 items, but on 02 Dec 2003, it sold 63 items, what food item
declined in sale, what went wrong with which food item ?
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