Movie Maniacs Site: MongoDB Database Queries Assignment for ICT704

This assignment solution demonstrates the application of MongoDB database concepts, focusing on a movie database scenario. It includes the creation of indexes to optimize query performance, followed by a series of queries designed to retrieve specific data. The queries cover various operations, including finding documents based on different criteria (e.g., MovieID, country, director), distinct value retrieval, counting documents, and updating documents. Aggregation queries are also implemented to perform more complex data analysis, such as joining data from multiple collections (Movie and Rating) and calculating average ratings. Finally, the assignment concludes with a comprehensive bibliography of relevant resources used in the assignment.

Running head: MONGODB DATABASE
MongoDB Database
Name of the Student
Name of the University

1MONGODB DATABASE
Table of Contents
Part A: Query.............................................................................................................................2
Index 1:...................................................................................................................................2
Index 2:...................................................................................................................................2
Index 3:...................................................................................................................................2
Query 1:..................................................................................................................................2
Query 2:..................................................................................................................................2
Query 3:..................................................................................................................................2
Query 4:..................................................................................................................................2
Query 5:..................................................................................................................................2
Query 6:..................................................................................................................................2
Query 7:..................................................................................................................................3
Query 8:..................................................................................................................................3
Query 9:..................................................................................................................................3
Query 10:................................................................................................................................3
Query 11:................................................................................................................................3
Bibliography:..............................................................................................................................5

2MONGODB DATABASE
Part A: Query
Index 1:
db.getCollection('Movie').createIndex( { MovieID: 1 } );
Index 2:
db.getCollection('Movie').createIndex( { MovieID: 1 }, { collation: { Country: "Japan" } } );
Index 3:
db.getCollection('Movie').createIndex( { MovieName: 1 }, { collation: { Country:
"Japan" } } );
Query 1:
db.getCollection('Movie').find({}, {MovieID: '1', '_id':0})
Query 2:
db.getCollection('Movie').find({Country: 'Japan'})
Query 3:
db.getCollection('Movie').find({}, {MovieName: 1, Director: 1, '_id':0})
Query 4:
db.getCollection('Movie').find({}, {MovieName: '2001', Director: 'Japan', '_id':0})
Query 5:
db.getCollection('Movie').distinct("Director")
Query 6:
db.getCollection('Movie').count({}, {MovieID: 1, '_id':0})

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Query 7:
db.Movie.find({OscarsWon: { $exists: false }})
Query 8:
db.getCollection('Movie').find({ReleaseDate: {$lte: '1968'}})
Query 9:
db.Movie.aggregate([{$lookup:{from:"Movie", localField: "MovieID",
foreignField: "MovieID", as: "MovieRating"}}, {$replaceRoot: { newRoot:
{ $mergeObjects: [ { $arrayElemAt: [ "$MovieRating", 0 ] }, "$$ROOT" ] } } }, {$group:
{_id: {Movie: "$MovieID", MovieName: "$MovieName"}, averageRating: {$avg:
"$Rating"}}}]);
Query 10:
db.Movie.aggregate([{ $lookup:{from:"Rating", localField: "MovieID", foreignField:
"MovieID", as: "MovieRating"}}, {$replaceRoot: { newRoot: { $mergeObjects:
[ { $arrayElemAt: [ "$MovieRating ", 0 ] }, "$$ROOT" ] } } }, {$project : {"MovieName":
1, MovieRating: {"Rating": 1, "Comments": 1}}}]);
Query 11:
db.Movie.update(
{ MovieName: "ET" },
{
MovieID: "6",
MovieName: "E.T.",
Director: "Steven Spielberg",

4MONGODB DATABASE
ReleaseDate: "1982",
OscarsWon: "4",
Country: "USA"
},
{ upsert: true }
)
Query 12: db.getCollection
('Movie').update
({MovieID: "12"},
{$set:{ notes: "Terminator and Terminator 2 are rated together"}})
db.getCollection
('Movie').update
({MovieID: "18"},
{$set:{ notes: "The trilogy consists of the three movies "}})

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Bibliography:
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Abbes, H. and Gargouri, F., 2016. Big data integration: A MongoDB database and modular
ontologies based approach. Procedia Computer Science, 96, pp.446-455.
Abbes, H., Boukettaya, S. and Gargouri, F., 2015, November. Learning ontology from big
data through MongoDB database. In Computer Systems and Applications (AICCSA), 2015
IEEE/ACS 12th International Conference of (pp. 1-7). IEEE.
Aboutorabi, S.H., Rezapour, M., Moradi, M. and Ghadiri, N., 2015, August. Performance
evaluation of SQL and MongoDB databases for big e-commerce data. In Computer Science
and Software Engineering (CSSE), 2015 International Symposium on (pp. 1-7). IEEE.
Chauhan, D. and Bansal, K.L., 2017. Using the Advantages of NoSQL: A case study on
MongoDB. International Journal on Recent and Innovation Trends in Computing and
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quality in open data: A case study. In de Innovacion y Tendencias en Ingenieria (CONIITI),
2017 Congreso Internacional (pp. 1-5). IEEE.

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