A Web Integration Framework for Cheap Flight Fares

Manuel Sánchez

, Juncal Gutiérrez-Artacho

and Jorge Bernardino

1,3

Superior Institute of Engineering of Coimbra, Polytechnic of Coimbra, Coimbra, Portugal

Department of Translation and Interpreting, University of Granada, Granada, Spain

Centre of Informatics and Systems of the University of Coimbra, University of Coimbra, Coimbra, Portugal

Keywords: Data Integration, Web Services, Relational Database Systems, Graph Systems, Big Data, Data Mining.

Abstract: Travel agencies offer their services via the Internet, which creates new methods of communication and

connection between customers and third companies. Due to the difficulty that the management of large

volume of flight routes represents, it is necessary to capture the information provided by airlines through a

variety of services, providing end customers with competitive fares. In this paper, we analyse the

information source of flight fares offered by airlines, studying the difficulties, limitations and costs involved

in accessing these data. A framework that explores the possibilities of finding "hidden" flight fares that

result in much cheaper options in comparison to the average price of each flight route will be presented.

1 INTRODUCTION

Travel agencies have been around for decades, but

throughout their history, and technological progress,

they have been evolving and adapting to a new

environment: the Internet. In order to make the

transition to the Cloud, travel agencies have been

transformed into a new kind of service provider,

known as OTAs (Online Travel Agencies). This new

business model, which offers its services over the

Internet, has forced companies to create new

methods of communication and connection between

customers and third companies, through the use of

Web Services.

A Web Service is a technology that uses a set of

protocols and standards that are employed to

exchange data between different applications on

different platforms, using the Internet to transmit

these data. Web Services are supplied on a uniform

programming interface called an API (Application

Programming Interface) (Binstock, 2015).

Online Travel Agencies provide Programming

Interfaces to extend their services to third parties,

and this is the key for accessing relevant information

on airfares in order to enable automated

management processes and the optimization of flight

searches. In the field of OTAs, we focus our work

on the flight management segment, and airline ticket

acquisition services.

Due to the size represented by the treatment of

this large volume of data (flight routes on a global

scale), it is necessary to address the relevant topic of

Big Data (IBM Big Data, 2015) in terms of the

capture of information provided by the airlines

through a variety of services and products

(Guterman, 2015). In this paper, we propose a

solution for processing and analyzing all obtained

information, to achieve favourable patterns for

future searches and thus providing customers with

the best fares.

The motivation behind this work is to present

and analyze business and technical tool options on

the market, and develop a new feature increasingly

demanded among customers. With our framework

we find the best deals on airfares and with them

make combinations that result in new flight routes,

offering customers the possibility of travelling to

places they would not have considered. As far as we

know, this new feature is missing from all of these

companies’ services. Despite this, it is partially

achievable, but in order to do so it is necessary to

perform an exhaustive manual search to find a trip

with a truly affordable price.

In practice, the main contributions of this work

are:

• To analyse the tools provided by Online Travel

Agencies (OTAs) and major flight search engines

• To offer their services through third companies,

which will allow the development of new features.

260

Sánchez, M., Gutiérrez-Artacho, J. and Bernardino, J.

A Web Integration Framework for Cheap Flight Fares.

DOI: 10.5220/0006287702600267

In Proceedings of the 13th International Conference on Web Information Systems and Technologies (WEBIST 2017), pages 260-267

ISBN: 978-989-758-246-2

• To obtain the best possible fare for a particular

route

• To discuss why current systems do not

implement this feature automatically.

The remainder of this paper is organized as

follows. Section 2 gives an introduction to the

current systems available on the market, what are the

best options, when thinking about using these

services and what they do not provide to their

customers. Section 3 analyses two of the most

widespread classes of Database Management

Systems (DBMS) on the market, their functions and

features, and the choice of a tool according to the

nature of the problem. Section 4 explains the

problems encountered in establishing a single

information source for flight fares. Section 5

describes our proposed web integration framework

explaining the architecture of the system developed

and giving an example of the results of flight fares

using our solution. Finally, in Section 6 we present

the conclusions and indicate future work.

2 BACKGROUND

Nowadays there are services that OTAs increasingly

use to grasp the attention of potential customers,

which include attractive features that enable greater

accessibility and dynamism. Many of these

companies are well known for offering the best deals

on airfares, others for having flexible tools to specify

a date for travelling, and others also offer the

possibility of choosing multiple destinations.

The main difficulty that these OTAs have when

offering possible flight combinations are the

restrictions applied to tariffs by the airlines

companies. These difficulties differ from temporary

restrictions on advance ticket purchase, up to

combination restrictions between different flights,

which can become quite complex, making too

difficult processing the information for determining

all possible combinations. An example of a

temporary restriction could be the application of a

particular fare that can only be carried out with the

purchase of a ticket 14 days before departure (Valles

& VanLoy, 1991).

We can see an example of combinations of

airfare restrictions when attempting to combine this

fare with another provided by a different company

or airline alliance. This results in an incompatible

approach to applying these prices under a unified

buying process, because the offers are only valid

when combined with other flights from the same

company.

Due to the complexity of the restrictions on the

airfares, the implementation of an upper layer of

analysis of these results is necessary, which allows

us to achieve optimal results searching fares

independently, without infringing the restrictions

applied to each one.

Some examples of implemented systems that

provide similar services with fare graphic

visualizations are companies like Vayant, Skypicker

and Kayak. These companies have systems that,

starting from a predetermined origin, find flight

routes with the existing lowest price across all dates,

so that if we choose a point of origin they can find

the lowest fare flight for each of the possible

destinations.

Other OTAs such as Skyscanner offer total

flexibility for choosing travel dates, being able to

search a range of weeks, months, or even the full

year, thus obtaining the best possible price for a

given route. Figure 1 shows an example of the

Skyscanner application.

Figure 1: Skyscanner application.

Skyscanner is a complete platform where we can

search with flexible dates and general locations such

as a city which may have several airports, or an

entire country, although it is not possible to suggest

the best deals to the client on routes not established

by the user, and they cannot make combinations that

result in new flight routes. A real example of this

can be observed in Figure 2, where we found a flight

from Cancun (CUN, Cancun, Mexico) to Brussels

(BRU, Brussels, Belgium) for €80, which is an

excellent deal, much cheaper than the next lowest

price, €1,000. If we had not made a search between

Mexico and Belgium we would never have found

this offer, because Skyscanner does not display these

suggestions automatically.

3 DATABASE MANAGEMENT

TOOLS

In this section we analyse and compare the most

A Web Integration Framework for Cheap Flight Fares

261

Figure 2: Great fare from Cancun to Brussels.

relevant information storage systems in the market:

Relational Databases and Graph Databases. These

systems are tested considering two metrics: data

storage size and performance using typical queries.

We also studied the services and facilities offered by

each of these databases systems. At the end of the

section we discuss the nature of the problem, i.e.,

possible combinations of routes based on nodes and

relationships to get from an origin point to a given

destination point.

3.1 Relational Databases

Relational Databases have been the standard system

for storing and accessing information used by most

systems since the early '80s, and even today. Most

current business models, due to their characteristics,

internal processes and restrictions, are naturally

adapted for this architecture based on ACID

(Atomicity, Consistency, Isolation and Durability)

properties, helping to understanding and integrate

traditional models.

An important goal of any database system is to

model the real world accurately in a consistent

manner with the user's perception of the data.

Unfortunately most traditional DBMS do not

provide adequate integrity features to ensure the

accuracy of data in their databases. Correspondingly,

most of traditional DBMS do not provide the

necessary optimally modelling tools. They present

difficulties because these databases are more

oriented to define the characteristics that identify the

relationships of the problems that determine how

they relate to each other.

The advantages provided by a relational DBMS

(RDBMS) are based on the capacity for integration

and standardization of the language and architecture,

serving a homogeneous model on mathematical

rules. We also noticed the disk storage a requirement

of information, when compared to other DBMS is an

important feature to consider. In Table 1 we show

the experimental results of the volume of data

occupied in disk with structures of random integers,

and strings with 8KB, and 32Kb. For these

experiments we choose MySQL as a tool for

Relational Databases, and Neo4j for Graph

Database.

Table 1: Size comparison of different data types.

Database #Nodes

Data

Type

MySQL

Size

Neo4j Size

1000int 1000 Int 0.232M 0.428M

5000int 5000 Int 0.828M 1.7M

10000int 10000 Int 1.6M 3.2M

100000int 100000 Int 15M 31M

1000char8k 1000 8K Char 18M 33M

5000char8k 5000 8K Char 87M 146M

10000char8k 10000 8K Char 173M 292M

100000char8k 100000 8K Char 1700M 2900M

1000char32k 1000 32K Char 70M 85M

5000char32k 5000 32K Char 504M 406M

10000char32k 10000 32K Char 778M 810M

100000char32k 100000 32K Char 6200M 7900M

As we can see from Table 1, the results of

storing data into Neo4j database takes between 1.25

to 2 times the size of the MySQL database. One

disadvantage of using relational databases to tackle

this issue is at the same time its best feature, strong

constraints of the model tables/relationships and

structured language (SQL) that needs to be applied.

Therefore discards using an architecture of this type,

opting for the use of other NoSQL models, namely

Graph Systems (Kamel, 1994). Systems based in

NoSQL allow dynamic structures, and are faster to

process the information, a key to dealing with

massive data volume as will see in the next section.

3.2 Graph Databases

Graph Databases are based on NoSQL model that

have gained recognition in recent years due to new

features provided when modelling a database. The

term “NoSQL”, as a term for modern web data

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stores, first began to gain popularity in early 2009. It

is a topic that has gained credit from the IT

community but has yet to garner large-scale

academic study.

The advantages provided by these systems are

evident; the freedom to organize the structures so

that they are not restrictive, and the speed to process

information stored (higher than RDBMS when

larger is the amount of information to be processed

in a query). Table 2 shows the queries to simulate

some of the types of queries used in our provenance

systems. For example, traversals are necessary to

determine data objects (nodes) derived from or

affected by some starting object or node:

• Q0: Find all orphan nodes. That is, find all

nodes in the graph that are singletons, with

no incoming edges and no outgoing edges.

• Q4: Traverse the graph to a depth of 4 and

count the number of nodes reachable.

• Q128: Traverse the graph to a depth of 128

and count the number of nodes reachable.

Table 2: Structural query results (in milliseconds).

Database

MySQL

Neo4j

MySQL

Neo4j

MySQL

Q128

Neo4j

Q128

1000int 1.5 9.6 38.9 2.8 80.4 15.5

5000int 7.4 10.6 14.3 1.4 97.3 30.5

10000int 14.8 23.5 10.5 0.5 75.5 12.5

100000int 187.1 161.8 6.8 2.4 69.8 18.0

1000char8K 1.1 1.1 1.1 0.1 21.4 1.3

5000char8K 7.6 7.5 1.0 0.1 34.8 1.9

10000char8K 14.9 14.6 1.1 0.6 37.4 4.3

100000char8K 187.1 146.8 1.1 6.5 40.9 13.5

1000char32K 1.3 1.0 1.0 0.1 12.5 0.5

5000char32K 7.6 7.5 2.1 0.5 29.0 1.6

10000char32K 15.1 15.5 1.1 0.8 38.1 2.5

100000char32K 183.4 170.0 6.8 4.4 39.8 8.1

For the traversal queries, Q0, Q4, and Q128, Neo4j

was clearly faster, sometimes by a factor of 10, as

detailed in Table 2. This was expected since

relational databases are not designed to do

traversals.

We conclude that the Graph Database systems

are the most optimal for searching existing

connection between nodes, providing response times

much lower than those obtained using Relational

Databases, which is the critical problem of flight

management systems.

3.3 Graph Database Tool

Based on previous experiments, we have chosen the

Graph DBMS architecture. In this section, we

discuss which is the most suitable tool to implement

a system of this type, which contains several

requirements when implementing this solution.

These requirements are the speed of data processing,

heterogeneity of access to information, migration of

the platform, or a possible scalability of the system.

Among all the possibilities, and based on a study of

efficiency of Graph DBMS platforms, we have

determined that the most efficient tool could be

Neo4j (Woodie, 2015). It is a Graph DBMS that we

can get in free community versions, or Enterprise,

which involves an economic outlay, taking

advantage of technical assistance, which can be of

great help in certain occasions.

After choosing the best Graph DBMS tool used to

develop the application, it is necessary to analyze in

which platform will deploy it. When choosing where

to host the database, there are several possibilities:

hosting on AWS / EC2, Windows Azure, or Cloud

Hosting providers like GrapheneDB, GraphStory,

Structr, etc. Thinking about the flexibility of the

system, the best option may be to acquire a server in

a hosting. This option is based in being able to

deploy databases, implement Data Mining system,

and develop diverse features under the same hosting.

3.4 Problem Definition

When thinking about how to approach the solution

to the problem of connections between different

flights, we have the idea of a large interconnected

network in which all points will not be

interconnected, but we have a variety of possibilities

to arrive at the desired point from a given origin. By

this way we can model the problem, and the best

option is using graphs, because the essential

information resides in the interaction between

connections, naturally expressed by graphs DBMS.

An example of the nature of the problem is shown

in Figure 5. In order to determine the possibilities of

reaching from Paris-Orly Airport (ORY, Paris,

France) to the International Brussels Airport (BRU,

Brussels, Belgium), we have not defined a direct

A Web Integration Framework for Cheap Flight Fares

263

route, but if we can determine different possibilities

to reach final destination. The proposed objective is

to reach any destination at the lowest possible cost.

In this case, the intermediate routes that could be

selected to reach the desired destination could be

through the connection at Barcelona airport (BCN,

Barcelona, Spain), Barajas airport (MAD, Madrid,

Spain) and Porto airport (OPO, Porto, Portugal). The

result of final route would be determined by the

minimum possible path and the lowest price found

among all these possibilities.

Figure 3: Routes between airports example.

4 INFORMATION ACCESS

POINT PROBLEMS

The main way to access airlines flight fares are

through GDS (Global Distribution System). GDS is

a network operated by a company that enables

automated transactions between third parties and

booking agents in order to provide travel-related

services to end consumers. A GDS can link services,

rates and bookings, consolidating products and

services across all three-travel sectors (book flight,

book hotel, car hire).

We can find two variants of the GDS systems

(Jonas, 2013); one of them is GNE (Global

Distribution System New Entrant). It does not

include incentives in transactions like regular GDSs,

therefore the profit margin for travel agents is less,

and will offer a more affordable price for the

purchase of flights.

The LCC (Low Cost Carrier) is another variant

that includes the largest listing group flights of low

cost companies and also offering their offers on their

own websites. Therefore, it is also a good option for

our use.

A GDS system is composed of several modules

management programs called CRS (Computer

Reservation System), which is a computerized

system used to store and retrieve information and

conduct transactions related to air travel, hotels, car

rental, or activities. Originally designed and

operated by airlines, CRSs were later extended for

the use of travel agencies (AXSES, 2013).

The main advantage of using these systems is the

centralization of the flights of different airlines,

which provides the same source information point,

and with the same interface for access to data

(Hospitality Net, 2015).

The major disadvantage of the centralization of

these data is the free use of them, which becomes

rather difficult to handle for a single user or a small

business, due to the access and use of these systems

includes a payment. The payment is normally under

a SaaS license, whose cost is determined by the

amount of transactions that are made to the system

(Consumer Reports WEB Watch, 2015).

The GDS systems are not the source of the

information of the flights from airlines. On the

contrary, there are two main agencies that saved and

updated every hour, virtually in real-time

information provided by the airlines. One of them is

ATPCO (Airline Tariff Publishing Company), and

the other is SITA (Société Internationale de

Télécommunications Aéronautiques).

ATPCO is a corporation that publishes the latest

airfares for more than 500 airlines, multiple times

per day, and provides fare data in an electronic

format, which make the information suitable for

computer processing. The only competitor to

ATPCO is SITA, who distributes some fares only in

Asia, Africa and Europe.

Fares are distributed hourly each day and airlines

carefully monitor new public fares filed by their

competition for publication through their systems.

Once time these corporations have distributed the

fares, airlines detect the action of other airlines

increasing or decreasing their fares for specific

connections, and then use this information to set

their own pricing strategy. For instance, if they see a

competitor introducing special promotional pricing

between two cities, they may want to quickly react

by filing their own special fares.

Access to the data provided by these major

corporations is very expensive and imposes several

restrictions, such as having an accredited title of

travel agent, which is obtained by performing a test,

and paid substantial fees. Due to these difficulties,

the best option would be to access the information

by consuming Web services that provide companies

more focused to the end customer, the OTAs (Online

Travel Agencies).

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5 WEB INTEGRATION

FRAMEWORK

In this section we explain our web integration

framework, describing its architecture and also

giving a working example of our proposal.

5.1 Proposed Architecture

The modelling of the solution has been made from

the information source that provides Skyscanner

through their offered API. The information

processing consist in four fundamental steps, and

assume a starting point in the system where the

database has stored the list of existing airports and

all the routes that connect it directly (see Figure 6).

To dispose the system at this early stage, it is

necessary to extract the relevant information from

airports and routes from another sources, in this case

it will be FlightStats and OpenFlights. We can

access the list of active civil airports through these

two organizations, and active routes between each of

them through OpenFlights. To obtain the

information through OpenFlights is only necessary

to download the appropriate files.

If we make requests to FlightStats, the response

will be in JSON format, and we must transform and

store the data in our database. When storing airports,

we get as a result, nodes that will serve as a

connection between possible routes in our system.

Once we have in the system the information about

airports and routes, we can make requests in a

logical and structured way through the Skyscanner

API to obtain flight offers. Skyscanner API is a

useful and essential tool to develop this solution; it

obtains the "best offer" for a given route by a simple

request (offers are subject to airfare restrictions). To

determine the extent of an offer, it is necessary to

know the average price that has a route, and in this

case, Skyscanner also offers an optimal solution.

Requests are performed following the scheme set

out in our database for all stored routes of available

airports. With this requests, we complete a first layer

of flight fares, and the average prices for each route.

This process will be processed with an algorithm

of minimum nodes cover (airports), to avoid

overloading the system with duplicate requests.

At this stage, we already could publish flight

offers, because fares are the lowest found searching

by default. It is also advisable to develop a parallel

system of Offer Management, which will determine

the quality of the offer comparing with the average

price of the route.

Once default fares are stored, we are at second

phase (see Figure 6), in which it will be possible to

make combinations between these routes. The first

combination process is made with one level of

depth, i.e., we expanded a node in one the level of

depth search. The combinations obtained will be the

result of filtering all possibilities with a variety of

date and flight time restrictions.

At the third phase, we perform a process of

comparing the existing offers in the system and new

combinations of routes processed. This process of

comparison will be made between the price of the

new route obtained, and the average price of the

route of the origin and destination of the new offer

(the new route obtained). If not exist connection

route, would be established a new one directly. On

the contrary, if the route already exists and has a

lower price than average, will be stored and offered

as a possible offer.

After completing this processing of information,

we are at phase four of the architecture. After storing

a new route, the process is repeated continuously.

The search for new routes stops when the price of

the new route is higher than the average price. This

search is based in a deep level determined by the

amount of times of processed routes.

The combination process of possible new routes

is activated when storing new data extracted through

Skyscanner, optimizing with that, the response time

to publish a new offer. The process of publishing

results is present at all stages, listening new

insertions into the database.

After presenting the architecture of our system, we

obtain the knowledge to create the framework where

the publication of flight offers is not limited by the

restrictions that apply airlines to their airfares. This

framework also noted for its ability to offer users the

best flight offers, publishing them in an order that

determines the quality of the offer. Figure 6 is a

diagram of the processes that belong to the system

architecture.

Figure 4: Architecture diagram.

A Web Integration Framework for Cheap Flight Fares

265

In the next section we will give a working example

how the system gives the output to users.

5.2 Problem Definition

Our intention is to implement a system to find the

best offers in existing flights without having to

perform a thorough and manual search based on

routes and dates.

The flight search engine used to perform this

analysis of prices is "Skyscanner", because it does

not include internal rates when searching for flights,

and automatically redirects to the main agents

shopping online (Expedia, Opodo, eDreams, etc.)

and major airline companies.

Doing various searches to check the veracity of

this assumption, we can see that a flight from

Barajas airport (Madrid, Spain) and destination

airport New York John F. Kennedy (NY, USA)

could cost nearly 550€. However, if we do an

exhaustive search performing different route

connections in Europe before crossing the Atlantic

Ocean, we can obtain a price around 400€. In this

case was chosen as the optimal connection Oslo

Gardermoen airport (Oslo, Norway).

Therefore, if we search only the origin and

desired destination, flight search engine will show us

different air routes with a price about around 150€

more expensive.

Another case in which we can see that the search

for lower prices are made according to the fares

restrictions of holding corporations, is the alarming

case to make trips from Ireland, in any of its cities,

and as a destination Puerto Vallarta (Jalisco,

México).

If we search with origin airport Dublin (Dublin,

Ireland) and destination Puerto Vallarta (Jalisco,

México), we get a price almost 900€, performing all

routes with an airline holding, Skyteam, including

flights from airlines like Delta, Aeroméxico and Air

France.

On the contrary, we found another cheapest

option for the same date, but under different

corporate holdings, and also making connections

within Europe before crossing the Atlantic Ocean. In

this case the airport connection between the origin

city and destination would the London Gatwick

airport, where the fare is less than 100€ by Ryanair.

From London Gatwick airport to Puerto Vallarta

with the company Thomson Airways would have a

fare less than 400€. A saving of almost 400 euros!

Therefore, for the same origin and destination,

performing different routes, we can be able to get a

price 50% lower than shown by default search

engine. Such examples can be found in several

destinations, both on flights from Europe to

America, like from Europe to Asia.

In this part of the process of analysis of results, is

where take part the functionality of implementing

optimal search minimum cover algorithms of our

proposal. The intention is to filter and limit searches

to a certain depth, obtaining as a result offers lower

Table 3: Skyscanner search at 06/04/2015.

Origin Destination Departure Arrival Price Company

MAD JFK 29/01/2016 30/01/2016

552€ Delta + Aeroméxico + Air France

JFK MAD 05/02/2016 06/02/2016

Origin Destination Departure Arrival Price Company

MAD OSL 29/01/2016 29/01/2016

173€ B. Airways + Iberia

OSL MAD 06/02/2016 06/02/2016

OSL JFK 29/01/2016 29/01/2016

251€ Norwegian

JFK OSL 05/02/2016 06/02/2016

Table 4: Skyscanner search at 06/04/2015.

Origin Destination Departure Arrival Price Company

DUB PVR 11/04/2015 12/04/2015

855€ United

PVR DUB 25/04/2015 27/04/2015

Origin Destination Departure Arrival Price Company

DUB LGW 11/04/2015 11/04/2015

96€

Aer Lingus

LGW DUB 26/04/2015 26/04/2015

LGW PVR 11/04/2015 11/04/2015

387€

Thomson

Airways

PVR LGW 25/04/2015 26/04/2015

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than average price established for a given route, and

after that, making possible combinations of offers

obtained above.

The application of this algorithm would not be

useful for a path when repeating the process, and did

not obtain results in a final path with lower price

than obtained when it made searches by default way.

6 CONCLUSIONS

In our work we conclude that relational databases do

not offer the necessary flexibility to process flight

management problem with optimal solutions, due to

strong structural constraints and a weak dynamic

indexing. We reach the conclusion that Graph

Databases are the best solution for storing and

processing data flight fares and we choose Neo4J.

We review and analyse diverse existing commercial

solutions and tools to implement new features using

data integration between different platforms. To

overcome the restrictions on flight fares was our

main motivation for implementing a data mining

system, in such a way, that we dispose a system free

of restrictions. With this system, we can show to

final customer the best possible flights fares,

combining connections between different airlines

and even different airline holding companies.

Also, the business alliances between airlines hinder

the process to obtain the most competitive offers,

and therefore, when choosing the cheapest way to

travel, these types of alliances would be damaging

the final consumer. It is true that this kind of

enterprise unions provides many facilities to

customers, due to the centralization of the purchase

(Pels, 2001). But, how much would be willing to pay

for this convenience? This is an open question that

we would like to have an answer in a near future.

We present a framework to find the best deals on

airfares, and with them make combinations that have

as results new flight routes, offering to the customer

a trip with a truly affordable price. To the best of our

knowledge this is the first proposal of this new

feature that is missing from all OTA systems.

As future work we propose a new feature

implementated by major GDS, or flight search

companies, to allow a real optimized search.

Resulting that the customers have the option to find

the best flight fare possible for a specific date.

We also must take in consideration the amount of

information processing that this feature needs (IBM

Big Data, 2015), because it would have to perform

an analysis of real-time fares from a massive amount

of data and could easily overload a system.

Therefore, our system should be able to process

flight fares from more than 30M of annual flights,

and the forecast worsens in 2030, can reach up to

60M annual commercial flights.

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