please follow instructions in the pdfMicrosoft Word - CM3104-2018-Coursework.docx 1 ...

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please follow instructions in the pdfMicrosoft Word - CM3104-2018-Coursework.docx 	 1	 Cardiff	School	of	Computer	Science	and	Informatics	 Coursework	Assessment	for	CM3104	Large	Scale	Databases	 	 Module	Code:	CM3104	 Module	Title:		Large	Scale	Databases	 Lecturer:	C.B.	Jones,	A.I.	Abdelmoty,	J.	Shao	 Assessment	Title:	Coursework	1	 Assessment	Number:	1	 Date	Set:	Thursday	1st	November	2018	 Submission	Date	and	Time:	Wednesday	5th	December	2018	at	9:30am.	 Return	Date:	Week	12,	Friday	11th	January	2019.		 	 This	assignment	is	worth	30	%	of	the	total	marks	available	for	this	module.	The	penalty	for	 late	or	non-submission	is	an	award	of	zero	marks.		 	 Your	submission	must	include	the	official	Coursework	Submission	Cover	sheet,	which	can	be	 found	here:	 	 https://docs.cs.cf.ac.uk/downloads/coursework/Coversheet.pdf	 	 	 Submission	Instructions	     Description Type Name  Cover  sheet   Compulsory One PDF (.pdf) file [student number].pdf  PART A Compulsory One PDF (.pdf) file comprising your  answer to all questions with  snapshots of the mongodb shell as  explained below.  PartA_[student number].pdf   Compulsory The javascript file with your answer  to all the questions in Task 2.   PartA_[student number].js  PART B Compulsory  One PDF (.pdf) file that contains  answers to all questions.  PartB_[student number].pdf  PART C Compulsory One PDF (.pdf) file that includes for  each of the five answers to Part C:   1 - The Oracle SQL query;   2 – The answer to the query;   3 - A screen shot of:         the query in Oracle followed by             the Oracle output from the query.  PartC_[student number].pdf  	 Any	deviation	from	the	submission	instructions	above	(including	the	number	and	types	of	 files	submitted)	may	result	in	a	mark	of	zero	for	the	assessment	or	question	part.  	 	 2	 	 	 Coursework	PART	A:	NoSQL	Databases	[worth	10	marks]	 	 In	this	part	of	the	coursework	you	will	make	use	of	two	data	sets:	a	restaurants	data	set	(in	 the	file,	restaurants.js)	and	a	zipcodes	data	set	(in	the	file	zipcodes.js).		You	are	building	an	 application	with	MongoDB	that	will	use	both	data	sets	to	find	information	about	restaurants	 in	different	cities.			 	 An	example	record	from	the	restaurants	dataset	is:	 {	 								"_id"	:	ObjectId("55cba2476c522cafdb053ae8"),	 								"location"	:	{	 																"coordinates"	:	[	 																								-73.9973325,	 																								40.61174889999999	 																],	 																"type"	:	"Point"	 								},	 								"name"	:	"C	&	C	Catering	Service"	 }	 	 An	example	record	from	the	zipcodes	dataset	is:	 {	 								"_id"	:	"01002",	 								"city"	:	"CUSHMAN",	 								"loc"	:	[	 																-72.51565,	 																42.377017	 								],	 								"pop"	:	36963,	 								"state"	:	"MA"	 }	 	 Here	is	a	map	of	a	sample	from	the	restaurants	dataset.		 	 	 3	 Task	1	 Import	both	datasets	into	MongoDB	in	ONE	database.		Each	dataset	should	be	stored	in	a	 separate	collection.		For	example,	you	could	build	a	“services”	database	with	two	 collections:	“restaurants”	and	“zipcodes”.	 	 (1	mark)	 	 Task	2	 1. Find	all	the	Pizza	restaurants	in	the	dataset	and	count	their	number.	 A	Pizza	restaurant	is	any	restaurant	which	has	the	word	“Pizza”	in	its	title.	 (1	mark)	 	 2. For	each	Pizza	restaurant	from	the	above	set,	find	the	city	that	it	is	located	in.	 You	can	assume	that	a	restaurant	is	located	in	the	nearest	city	to	its	location.				 (2	marks)	 	 3. In	your	application	you	will	need	to	retrieve	the	restaurants	and	their	associated	 cities	frequently.		 Create	a	new	collection	(“geodb”)	that	contains	all	the	data	in	the	restaurant	 collection,	but	for	every	restaurant	document	in	the	new	collection,	add	an	 additional	field	“city”	whose	value	is	the	closest	city	to	the	restaurant’s	location.	Use	 the	same	method	of	finding	the	city	as	you	have	done	in	question	2.		 	 Documents	in	your	new	“geodb”	collection	should	be	similar	to	the	following:	 {	 								"_id"	:	ObjectId("55cba2476c522cafdb053ae8"),	 								"location"	:	{	 																"coordinates"	:	[	 																								-73.9973325,	 																								40.61174889999999	 																],	 																"type"	:	"Point"	 								},	 								"name"	:	"C	&	C	Catering	Service",	 								"city"	:	"BROOKLYN"	 }	 (3	marks)	 	 	 	 	 	 	 	 	 	 4	 4. Query	the	new	“geodb”	collection	to	find	restaurants,	grouped	by	city.		Your	answer	 should	include:	city	name,	number	of	restaurants	in	the	city	and	a	list	of	all	the	 names	of	the	restaurants	in	the	city.	 	 An	example	document	of	the	results	of	this	query	is	as	follows:	 	 {	 								"City":	"	MOBILE",	 								"No	of	Restaurants":	3,	 								"Restaurants":	[	 			"Island	Soft	Pretzel	Stop",	 																"Dairy	Queen	Grill	&	Chill",	 																"Statue	Of	Liberty	Deli"									 ]	 }	 (2	marks)	 	 5. A	different	method	of	storing	the	restaurant	and	city	information	together	is	by	 modifying	the	original	“zipcodes”	collection	by	adding	a	field	“restaurantsInCity”	 whose	value	is	an	array	of	all	the	restaurants	that	are	located	in	the	city.		An	 example	of	a	document	in	the	modified	“zipcodes”	collection	is	as	follows:	 {	 								"_id"	:	"36607",	 								"city"	:	"MOBILE",	 								"loc"	:	[	 																-88.1029,	 																30.697486	 								],	 								"pop"	:	8610,	 								"state"	:	"AL"	 								“restaurantsInCity”	:	[“Island	Soft	Pretzel	Stop”,	“Dairy	Queen	Grill	&	Chill”,	"Statue	 Of	Liberty	Deli"]	 }	 	 Explain	whether	this	is	a	sensible	modelling	option	in	MongoDB,	by	referring	to	its	 effectiveness	for	storing	and	retrieving	the	information	about	restaurants	and	 associated	cities.			 (1	mark)	 	 	 	 	 	 	 	 	 	 	 5	 UPLOADS	FOR	PART	A	 	 1. Save	your	answer	to	the	questions	in	Task	2	in	a	javascript	file	with	the	name:	 PartA_[student	number].js		 	 2. Save	your	answer	to	all	questions	in	Task	1	and	Task	2	in	a	pdf	file:												 PartA_[student	number].pdf.		You	can	take	a	snapshot	of	the	question	being	 executed	in	the	MongoDB	shell,	clearly	demonstrating	the	answer	to	the	question	 (include	snapshots	of	all	intermediate	steps	as	appropriate).		A	sample	of	the	results	 is	sufficient	–	this	should	be	the	first	5	documents.	An	example	of	a	sufficient	 snapshot	of	the	answer	to	a	query	to	find	all	records	in	the	“zipcodes”	collection	is	as	 follows.

Aakarsh · Accepted Answer

Coursework PART A: NoSQL Databases 
In this part of the two data sets are provided: a restaurants data set (in the file, 
restaurants.js) and a zipcodes data set (in the file zipcodes.js). An application with MongoDB 
is created that will use both data sets to find information about restaurants in different 
cities. 
Task1 
Import both datasets into MongoDB in ONE database. Each dataset should be stored in a  
separate collection. For example, you could build a “services” database with two  
collections: “restaurants” and “zipcodes”. 
Restaurant Data after importing into MongoDB
Zipcodes data after importing in MongoDB
Task2
1. Find all the Pizza restaurants in the dataset and count their number. 
A Pizza restaurant is any restaurant which has the word “Pizza” in its title. 
Query:    dbo.collection('restaurants').find({name:/Pizza/}).count() 
Output:  1260
2.

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