Final project database systems topic database systems for traveloka

and bind the attribute for the file and relation.Based on our ERD, we have: Locations id, city_name Primary key id Customers id, name, email, phone_number, location, gender Primary key i

VIETNAM NATIONAL UNIVERSITY, HANOI INTERNATIONAL SCHOOL

DATABASE SYSTEMS Topic: DATABASE SYSTEMS FOR TRAVELOKA

Class: INS 205501

Lecturer: NGUYEN DINH TRAN LONG

GROUP 10:

Đỗ Hồng Phúc

Phan Viết Anh Quân

Lê Mạnh Hùng

traveloka

FINAL PROJECT

Group 10

Hanoi, December 2023

TABLE OF CONTENT

L INTRODUCTION

1 About the Organization

2 Requirement Specification

3 Business Narrative

Il ANALYZING AND DESIGNING THE DATABASE

1 ERD

2 Relational Schema

3 Some read data and SQL script

II REPORT AND INFORMATION

1 Summary some reports and requirements for real usage

2 Requirements and Solutions

IV CONCLUSION

I INTRODUCTION

1 About the Organization

Traveloka was established in February 2012 by three Indonesian software engineers: Ferry Unardi, Albert, and Derianto Kusuma Unardi and Albert had met while studying computer science in the United States, while Unardi met Kusuma while both were interning at

Microsoft All three ultimately worked in Silicon Valley Unardi, frequently encountering travel difficulties when visiting his hometown of Padang, came upon the idea of a website to ease travel logistics in Indonesia As he had no business experience, he began an MBA

program at Harvard Business School However, in early 2012, the Indonesian tech market was attracting international investors and e-ticketing was becoming more popular, so Unardi dropped out early and the three founders moved to Jakarta and established Traveloka

2 Requirement Specification

The information used in this report is gathered from the following sources:

a Contact:

We designed the database primarily based on information from public information about the company and sidelined information we received when we contacted them

b Researching new / dataset:

We compare and draw conclusions from some datasets that are similar to our target Some data are referenced from Kagle

3 Business Narrative

The database will solve two main activities: “managing the information of the products”,

“managing the information of all sale transactions”

The hotel management system receives room reservations and categorizes them into units

such as "Standard Rooms," "Executive Suites,” "Luxury Suites," and more To provide easily

recognizable information, the system stores details about the supplier linked to each room category Each room reservation is assigned a unique ID for easy identification and error

prevention in management This method ensures clear information about room types,

enhancing the booking experience for customers

Group 10

“Managing the information of Sale Transactions” involves a complex system that can store

information about the Store, Products, Employees, Customers, and their VIP customers It is

responsible for keeping clear and accurate records of Operation contracts, Product Sales

agreements, Labor agreements, and Customer dates

i ERD

|

/

Figure 1: ERD

2 Relational Schema

Reservations Fk2 | Hotel_ID

FK3 | Section_ID

FK | Location_ID

The next step is to define the attributes such as: primary key, foreign key, and bind the

attribute for the file and relation.Based on our ERD, we have:

Locations (id, city_name)

Primary key (id)

Customers (id, name, email, phone_number, location, gender)

Primary key (id),

Foreign key (Location_ID) references Locations (Location_ID)

Hotels (id, locationID, hotel_name)

Primary key (id)

Foreign key (Location_id) references Locations (Location_id)

Sections (id, sectionID, room_type)

Primary key (id)

Foreign key (Hotel_id) references hotels (Hotel_id)

Sales (id, apply_month, sale_percent)

Primary key (id)

Rooms (id, hotelID, sectionID, saleID, floor)

Primary key (id)

Foreign key (section_id) references sections (section_id)

Foreign key (hotel_id) references hotels (hotel_id)

Foreign key (sale_id) references sales (sale_id)

Reservations (id, customerID, hotelID, sectionID, roomID, dayStart, dayEnd, price)

Primary key (id)

Foreign key(CustomerID) references customers (customer_id)

Foreign key(HotelID) references hotels (hotel_id)

Foreign key(SectionID) references sections (section_id)

Foreign key(RoomID) references rooms (room_id)

Our detailed SQL statement

CREATE DATABASE Final;

USE Final,

drop table if exists reservations;

drop table if exists rooms;

drop table if exists sales;

drop table if exists sections;

drop table if exists hotels;

drop table if exists customers;

drop table if exists locations;

create table locations

(

primary key (location_id)

b

create table customers

(

phone_number varchar(10) not null,

location_id int,

primary key (customer_id),

foreign key (location_id) references locations (location_id)

create table hotels

(

location_id int not null,

hotel_name varchar(50) default ",

);

primary key (hotel_id),

foreign key (location_id) references locations (location_id)

create table sections

(

room_type varchar(30) not null default ",

primary key (section_id),

foreign key (hotel_id) references hotels (hotel_id)

create table sales

(

apply_month int not null,

sale percent int not null,

primary key (sale_id)

create table rooms

(

hotel_id int not null,

section_id int not null,

sale id int not null,

floor int not null,

primary key (room_id),

foreign key ( section_id) references sections (section_id),

foreign key (hotel_id) references hotels (hotel_id),

Group 10

);

foreign key (sale_id) references sales (sale_id)

create table reservations

(

reservation_id int not null primary key,

3 Some read data and SQL script

— Results g¥ Messages

1 13000 Nguyen Duc Quang ducquang3000@gmail.com 0123458789 7000 nam

2 Le Nhat Huy nhathuy3010@gmail com 0123456780 7000 nam

Figure 3: Customer Data

Group 10

100% x

Eä Resultls gẩ Messages

hotelid location id hotel name

1 [1100 j Royal Hotel

3 1300 7000 JW Marriott Hanoi

4 1400 7010 Tan Son Nhat Hotel

5 1500 7010 Eastin Grand Hotel Saigon

6 1600 7020 Pavilion Hotel

7 1700 7030 Vinpearl Hotel Imperia Haiphong

8 1800 7040 Muong Thanh Luxury Quang Ninh Hotel

9 1900 7060 Saphir DaLat Hotel

Figure 4: Hotel Data

100% x

FB Results cũ Messages location_id _ city

1 [7000 | Ha Noi

2 Ho Chi Minh

3 Da Nang

4 Hai Phong

5 Quang Ninh

6 Quang binh

š Da Lat

8 Hue

9 Khanh Hoa

10 7090 Lang Son

11 7110 Thanh Hoa

12 7220 Nghe An

13 7330 Bac Ninh

14 7440 Hung Yen

15 7550 Nam Dinh

16 7660 Phu Yen

17 7770 Buon Ma

18 7880 Can Tho

19 7990 Kien Giang

Figure 5 : Location Data

Figure 6: Reservation Data

Figure 7 : Room Data

Figure 8 : Sale Data

Figure 9 : Hotel Data

I REPORT AND INFORMATION

1 Summary some reports and requirements for real usage

2019

descending order

level of the customer with the longest number of days staying at the hotel in Hanoi

in Hanoi

who have ever booked a room for each hotel before October 20, 2019 in descending order

e Customer name, hotel name, cheapest room id that the customer has booked in case

this person only stays 3 days

more customers booking rooms in February or December in Hanoi

2 Requirements and Solutions

2019

SELECT

h.hotel_name AS "lên Khách Sạn,

rv.room_¡d AS ID Phòng được đặt nhiều nhất trong năm 20 19',

COUNT(rv.reservation_¡đ) AS 'Số lượt đặt phong'

FROM

hotels h

JOIN

reservations rv ON h.hotel_id = rv.hotel_id

WHERE

YEAR(tv.day_ start) = 2019

GROUP BY

h.hotel_name, rv.room_id, h.hotel_id Add h.hotel_id to GROUP BY

HAVING

COUNT(rv.reservation_id) = (

SELECT TOP 1 COUNT(reservation_id)

FROM reservations

WHERE hotel_id = h.hotel_id

GROUP BY room_id

ORDER BY COUNT(reservation_id) DESC

);

in descending order

SELECT

rv.hotel_id AS 'ID Khach San’,

h.hotel_name AS "lên Khách Sạn,

AVGf(rv.prce) AS 'Muc chi tieu trung binh cua khach san trong nam 2019 (VND}

FROM

reservations rv

JOIN

hotels h ON rv.hotel_id = h.hotel_id

WHERE

YEAR(tv.day_ start) = 2019

GROUP BY

rv.hotel_1d, h.hotel_ name

ORDER BY

AVGf(rv.price) DESC;

level of the customer with the longest number of days staying at the hotel in Hanoi

SELECT

c.name AS 'Tén Khach Hang’,

DATEDIFF(day, rv.day_start, rv.day_end) AS 'S6 ngay lun lai’,

rv.price AS 'Chi tiêu của Khách hàng'

FROM

reservations rv

JOIN

customers c ON rv.customer_id = c.customer_id

WHERE

tv.hotel_id IN (SELECT h-hotel_id

FROM hotels h

JOIN locations | ON h.location_id = Llocation_id

WHERE I.city = 'Hà Nội)

AND DATEDIFF(day, rv.day_start, rv.day_end) = (

SELECT MAX(DATEDIFF(day, day_ start, day_end))

FROM reservations

WHERE hotel_id IN (

SELECT h.hotel_id

FROM hotels h

JOIN locations | ON h.location_id = | location_id

WHERE L.city ='Ha NO’

)

);

2019

SELECT

h.hotel_name AS "lên Khách Sạn có số lượt đặt phòng nhiều nhất tháng 12,

COUNT(rv.reservation_id) AS 'S6 lượt đặt phòng'

FROM

hotels h

JOIN

reservations rv ON h.hotel_id = rv.hotel_id

WHERE

MONTH(v.day_ start) = 12

GROUP BY

rv.hotel_1d, h.hotel_ name

HAVING

COUNT(rv.reservation_id) = (

SELECT TOP 1 COUNT(reservation_id)

FROM reservations

WHERE MONTH(day_start) = 12

GROUP BY hotel_id

ORDER BY COUNT(reservation_id) DESC

);

SELECT

h.hotel_name AS "lên Khách Sạn,

STRING AGG(r.room_id, ', ') AS 'ID Phong’,

rfloor AS 'Tang'

FROM

rooms r

JOIN

hotels h ON r.hotel_id = h-hotel_id

WHERE

r.floor = (SELECT MAX(floor)

FROM rooms

WHERE hotel_id = h.hotel_id)

GROUP BY

h.hotel_id, h.hotel_name, r-floor

ORDER BY

r.floor DESC;

December in Hanoi

SELECT

r.room_id AS 'ID Phong’,

h.hotel_name AS "lên Khách sạn',

rfloor AS 'Tầng',

s.room_type AS 'Loai Phong’,

STRING_AGG(CONVERT(VARCHAR, sl.sale_percent) + '%',',") AS 'Mire giam gia’, sLapply_month AS 'Thang ap dung’

FROM

sales sl

JOIN

rooms r ON r.room_id =r.room_id

JOIN

sections s ON r.section_id = s.section_id

JOIN

hotels h ON r.hotel_id = h-hotel_id

WHERE

s.room_type = 'Double'

AND r.hotel_id IN (

SELECT h.hotel_id

FROM hotels h

JOIN locations | ON h.location_id = Llocation_id

WHERE Lcity = 'Ha Noi’

)

AND sl.apply_month = 12

GROUP BY

r.room_id, h-hotel_name, r-floor, s.room_type, sl.apply_month;

SELECT

h.hotel_name AS "lên Khách sạn',

AVG(sLsale_percent) AS 'Mirc giam giá trung bình tháng 1'

FROM

rooms r

INNER JOIN

hotels h ON r.hotel_id = h-hotel_id

INNER JOIN

sales sl ON r.sale_id = sl.sale_id

WHERE

sLapply_month = |

GROUP BY

h.hotel_id, h.hotel_name;

who have ever booked a room for each hotel before October 20, 2019 in descending order

SELECT

h.hotel_name AS "lên Khách sạn',

COUNT(CASE WHEN c.gender = 'Nam' THEN 1 END) AS 'Số khách hàng Nam', COUNT(CASE WHEN c.gender = 'Nữ' THEN 1 END) AS 'Số khách hàng Nữ, COUNT(DISTINCT rv.customer_id) AS 'Téng sé khach'

FROM

reservations rv

INNER JOIN

customers c ON rv.customer_id = c.customer_id

INNER JOIN

hotels h ON rv.hotel_id = h.hotel_id

WHERE

rv.day_ start <'2019-10-20'

GROUP BY

h.hotel_id, h.-hotel_ name

ORDER BY

COUNT(DISTINCT rv.customer_id) DESC;

© Customer name, hotel name, cheapest room id that the customer has booked in

case this person only stays 3 days

SELECT

c.name AS 'Khach hang’,

h.hotel_name AS 'Tén khach san’,

rroom_¡d AS 'Số phòng',

r.price AS 'Giá tién’,

r.day_start AS 'Ngay dat phòng,

r.day_end AS 'Ngay tra phong’

FROM

customers c

JOIN

reservations r ON c.customer_id = r.customer_id

JOIN

hotels h ON r.hotel_id = h-hotel_id

WHERE

DATEDIFF(day, r.day_start, r.day_end) = 3

AND r.price = (

SELECT MIN(price)

FROM reservations

WHERE DATEDIFF(day, day_ start, day_end) = 3

);

more customers booking rooms in February or December in Hanoi

SELECT

r.room_id AS 'ID Phong’,

h.hotel_name AS "lên Khách sạn',

s.room_type AS 'Loai phong’,

sỈ.sale_percent AS 'Mức giảm gia’,

COUNT(CASE WHEN MONTH(tv.day_start) = 2 THEN 1 END) AS 'Số lượt đặt trong thang 2’,

COUNT(CASE WHEN MONTH(tv.day_ start) = 12 THEN 1 END) AS'S6 luot dat trong thang 12'

FROM

rooms r

JOIN

reservations rv ON r.room_id=rv.room_id

JOIN

hotels h ON ry.hotel_id = h.hotel_id

JOIN

sales sl ON r.sale_id = sl.sale_id

JOIN

sections s ON r.section_id = s.section_id

WHERE

h.hotel_id IN (

SELECT h.hotel_id

FROM hotels h

JOIN locations | ON h.location_id = Llocation_id

WHERE Lcity = 'Ha Noi’

)

GROUP BY

r.room_id, h.hotel_name, s.room_type, sl.sale_percent

HAVING

COUNT(CASE WHEN MONTH(rv.day_ start) = 2 THEN 1 END) >= 2

OR COUNT(CASE WHEN MONTH(rv.day_ start) = 12 THEN 1 END) >= 2;

To conclude the project, I hereby summarize some of the plus points and also the downsides

of the database built by our group as follows:

1 Advantages of the project to the business:

how many partners they are working with

The database provides a much more scientific way of managing the amount of items and which item is available

The database provides a clearer view of the flow of product - from the store to its customer then to the second customer

ERD provides the business a better view of relations between all of the subjects

The database provides a better way to audit the shop’s data compared to pens and papers

With a database, traveloka stores can track customer preferences and purchase history to provide more personalized recommendations and promotions This can help build customer loyalty and increase sales

°

°

There are not many types of data that can be inserted in

This database lacks real information, there are only 20 samples for each table, not more, it would be better for the real business if there was more

information

This database lacks options for auditing the items that customers buy and also the quantity bought by them

This project will help the business a big deal in storing data and manage the basic data but not very ideal for auditing