DEVELOPING A PRICE MANAGEMENT DECISION SUPPORT

SYSTEM FOR HOTEL BROKERS USING FREE

AND OPEN SOURCE TOOLS

Slava Kisilevich, Daniel Keim, Roman Byshko

Department of Computer and Information Science

University of Konstanz, Konstanz, Germany

Michael Tsibelman*, Lior Rokach

HP Software, Yehud, Israel*

Department of Information Systems Engineering and The Deutsche Telekom Laboratories

Ben-Gurion University of the Negev, Beer-Sheva, Israel

Keywords:

Pricing management, Decision support, Hotels, Hedonic pricing model.

Abstract:

In the Internet age, e-commerce provides customers global reach to a wide variety of products and plays a

dominant role in business activity and competition. Competition is especially aggressive in the online travel

domain where wholesalers, e.g. brokerage companies, contract through their contract managers with thou-

sands of hotel brands and trade hotel products (usually hotel nights) for travel businesses or end customers.

In order to conclude a proﬁtable contract, a contract manager should be able to compare all the particulars

of the prospective partner hotel with those of the competing hotels in the target city. Given that the number

of contract managers is comparatively small compared to the large number of hotels, the possible knowledge

base is limited. Thus, the hotel brokerage companies are only able to bargain with a relatively limited number

of hotels, and the contract proﬁtability relies heavily on the contract managers’ expertise and communication

skills. In this paper we present a price management decision support system (DSS) for hotel brokers that

allows analysis of hotel prices using spatial and non-spatial characteristics, estimation of the objective relative

hotel prices, and determination of the proﬁtability of the existing or future contracts. We built our system

using free and open source tools including geographic information system and data mining frameworks that

allow companies with limited money resources or manpower to implement such a prototype. We show the ef-

fectiveness of our tool by covering all the major components of the DSS such as data selection and integration,

model management and user interface. We demonstrate our tool on the area of Barcelona, Spain using a real

data of 168 hotels provided by one of the travel service providers.

1 INTRODUCTION

Hospitality business is an industry with two levels

of competition. On the ﬁrst level, hotels compete

among each other for travelers. At the second level,

various travel intermediates (travel agencies, travel

wholesalers) compete for the most proﬁtable discount

rate contracts proposed by hotels. Proﬁtability of

any travel intermediate is directly related to the dis-

count rate contracts that are acquired and to the inter-

mediate’s ability of selling the product to customers.

Travel intermediates are dependent on their staff of

professional and highly paid hotel contract managers

to negotiate the best contract. Since the number of ho-

tels in the world is large and the negotiation process

is long, any particular travel intermediate has a rela-

tively small amount of contractors it can assign to any

of available destinations. Consequently, a contractor

is faced with two challenges: (1) to identify hotels

that ﬁt the proﬁle of their end customers, and (2) to

identify hotels in which managers would be inclined

to give better rates during negotiations.

Hotels employ revenue management systems (for

an overview, see Chiang et al. 2007) to determine the

future pricing based on the capacity and demand fore-

cast. Therefore, hotel managers who negotiate the

deal with contractors propose contracts that are prof-

147

Kisilevich S., Keim D., Byshko R., Tsibelman M. and Rokach L..

DEVELOPING A PRICE MANAGEMENT DECISION SUPPORT SYSTEM FOR HOTEL BROKERS USING FREE AND OPEN SOURCE TOOLS.

DOI: 10.5220/0003460701470156

In Proceedings of the 13th International Conference on Enterprise Information Systems (ICEIS-2011), pages 147-156

ISBN: 978-989-8425-54-6

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

itable for the hotel. However, contract managers usu-

ally lack detailed knowledge about the particular ho-

tel and rely more on the local market understanding

and their communication skills. One of the questions

that travel wholesalers ask is whether there is a pos-

sibility to determine the objective market price of a

hotel before the negotiation is started such that this

knowledge can be used by contractors in leveraging

the deal. And if such a possibility exists, then what

is the solution. A naive solution would be to use

the openness and power of the Internet to check for

the prices of the same hotel at competitors’ websites.

However, this apparently simple approach is deemed

impractical since hotels require their dealers to adver-

tise the same price as it is shown on the hotel’s web

page . A practical solution is to use hedonic pricing

theory (Rosen, 1974) to identify hotels with the same

characteristics. Hedonic pricing theory states that the

price of the product is determined by the individual

characteristics of the product. Therefore, by ﬁnding

the hotels with the same characteristics or factors that

affect hotel prices, it will be possible to compare price

rates between similar hotels.

Understanding the factors that affect hotel prices

using the hedonic pricing theory, received much atten-

tion in the research (e.g., Monty and Skidmore 2003;

Thrane 2007; Li et al. 2008; Hung et al. 2010; Chen

and Rothschild 2010; Lee and Jang 2010). The re-

sults show that there is no universal solution to the

factors that affect prices. Moreover, results were af-

fected by many factors such as empirical methods

selected for the analysis, data quality and complete-

ness, and hotel characteristics. The problem of hotel

price estimation using hotel characteristics is an ill-

structured problem since it may have many answers

that depend on the selected parameters. Additionally,

hotel characteristics are of two types: non-spatial, like

room amenities and hotel facilities, and spatial, like

proximity to waterfront or to a business center. It is

easier to answer the question about non-spatial char-

acteristics like Is there a hairdryer in the room then

answering the question How many points of interest

are around the hotel since around is not precisely de-

ﬁned in terms of distance. Therefore, a completely

automated solution process as was demonstrated by

Li et al. (2008) is not feasible in this case since the

guidance of the expert is paramount in the case of

ill-structured problems. Clearly, there is a need for

an interactive decision-support system (DSS) (Shim

et al., 2002; Arnott and Pervan, 2005; Karacapilidis,

2006) that would help the analyst in testing different

hypotheses regarding price factors on selected hotels.

In this system, the analyst can select the region of

investigation by fetching all the necessary data from

his/her corporate database. It should allow him/her to

add additional data that he/she thinks is important in

the analysis. Such data, for example, could be points

of interest around hotels, transportation points, histor-

ical places or information about the proximity of a ho-

tel to waterfront, etc. The analyst can build different

models and apply different algorithms using this sys-

tem and the system should help the analyst in the ﬁnal

decision about the desirability of a hotel and its objec-

tive price. As was mentioned above, the hotel charac-

teristics and model components have spatial charac-

teristics (hotel location, location of points of interest,

etc.). In previous research it was shown (Crossland

et al., 1995) that addition of Geographic Informa-

tion Systems (GIS) technology to a business decision-

making environment improves the performance of the

decision-maker. Therefore, we argue that the hotel

price management system should at least provide sup-

port to input spatial data, to represent complex spatial

relations, to analyze spatial data, and to output spatial

data in the forms of maps, as discussed in Densham

(1991).

The travel intermediates that are interested in the

development of the outlined hotel price management

decision support system will inevitably face at least

two difﬁculties. The ﬁrst difﬁculty is technical and

relates to high costs pertinent to the development it-

self. Usually, such companies employ a staff of web

programmers that develop web infrastructure of their

corporate website and they do not have spare re-

sources for developing complex analytical GIS-based

systems. One of our goals is to show that by using

the right free and open source tools, it is possible to

save development time by extending existing appli-

cations concentrating on the development of compo-

nents related to the price estimation problem only. We

achieve this by extending Java OpenStreetMap Edi-

tor

, a cross-platform editor of OpenStreetMap (Hak-

lay and Weber, 2008) data with a GIS-based interface,

using R Project

, a suit for statistical computing and

Weka (Hall et al., 2009), data mining and machine

learning software. The second difﬁculty is how to ob-

tain the external data that is essential for price esti-

mations, such as points of interest, transportation lo-

cations (buses, trains). These data is originally out

of the scope of wholesalers who generally have only

data about hotel amenities and facilities, and room

prices. There are different free services available

(e.g., GeoNames

) to collect the data but these ap-

proaches work best only for some small predeﬁned

areas and require manual preprocessing. In case of

http://josm.openstreetmap.de/

http://www.r-project.org/

http://www.geonames.org/

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148

a decision support system that is to be applied vir-

tually on every part of the world, there is a need in

a simple process for retrieving the needed data. We

show that this is achieved by using OpenStreetMap

data, which is contributed by thousands of people. Al-

though, some data like the proximity of a hotel to the

seafront is not available through OpenStreetMap, the

analyst is able to decide for this feature and annotate

the hotel under investigation with this information by

using a simple user interface.

The contribution of the paper can be summarized

as follows:

(1) We propose a general all-in-one hotel price man-

agement solution for hotel wholesalers using free and

open source tools.

(2) We simplify considerably the external data acqui-

sition by using OpenStreetMap data.

(3) We enrich the price management process with ge-

ographic information system.

(4) We embed a data mining framework that allows

applying different algorithms on the created models.

(5) The analyst decides on features that are included

into the model.

(6) The analyst applies the desired properties to fea-

tures if needed (for example if a hotel faces water-

front).

2 RELATED WORK

Room rate characteristics for 74 hotels in and around

Oslo were studied by Thrane (2007) using log-linear

regression. Such factors as availability of mini-bars

and hairdryers in a room, and parking near the ho-

tel, signiﬁcantly inﬂuenced the hotel price. However,

room rates were lower in hotels that offer room ser-

vice. In addition, hotels associated with chains are

more expensive than non-chain hotels.

In the study about hotels in Taiwan (Hung et al.,

2010), it was shown by applying quantile regression

analysis, that the age of hotels is negatively related

to the hotel price, while there is no signiﬁcant dif-

ference between chain and non-chain hotels. Yet in

another study that included 73 hotels in Taipei (Chen

and Rothschild, 2010), it was found that such factors

as breakfast, business centers or swimming pools do

not inﬂuence the room price, while the hotel location,

TV, Internet access, and availability of the ﬁtness cen-

ter, have signiﬁcant inﬂuence on room rates.

Lee and Jang (2010) showed that hotel prices are

affected by the proximity of a hotel to an airport or to

central business districts.

Li et al. (2008) applied econometric modeling to

estimate the “objective” economic value of different

hotel characteristics such as proximity to the beach,

distance to the downtown, neighborhood safeness, ho-

tel class, customer reviews, etc. The econometric

model predicts the actual price for a hotel and esti-

mates its overall ranking (overpriced, underpriced) by

calculating the difference between the averaged pre-

dicted price and the averaged real price.

3 PROBLEM DOMAIN

Hotel

Broker

Hotel

Broker

Sells

Website

Owns

Consumer Website

Offline Travel Agents

Owns

Figure 1: Interaction between hotels and hotel intermedi-

ates.

The interaction between hotels and hotel intermedi-

ates is schematically depicted in Figure 1. A hotel

usually has its own website where it promotes room

nights sales directly. The website is the most prof-

itable selling channel because no intermediates are in-

volved. However, the exposure of a hotel web page to

a vast audience is limited because customers prefer

to see the price list of hotels to compare using one

or two travel sites, rather than searching for individ-

ual hotels. Therefore, hotels are interested in being

advertised by other channels with higher probability

of being exposed to end customers. As depicted in

Figure 1, Hotel A is exposed through the Hotel Bro-

ker a channel, while Hotel B is exposed through the

Hotel Borker b channel. Similarly, hotel brokers pro-

mote their products through consumer websites and

ofﬂine travel agents. The hotel intermediate may also

sell hotel nights to other intermediates if that inter-

mediate does not have a direct contract with the ho-

tel. It is clear that the hotel intermediate can reach

the best price by working directly with the hotel. As

was already explained in Section 1, the hotels sell

room nights to the hotel brokers in the form of dis-

count rate contracts. Hotel brokers are committed (as

part of the contract) to keep the prices at their on-

line channels similar to the prices provided by hotels

through their own websites. Therefore, the revenue of

the travel intermediates is the difference between the

ﬁnal hotel price and the contract cost. Consequently,

DEVELOPING A PRICE MANAGEMENT DECISION SUPPORT SYSTEM FOR HOTEL BROKERS USING FREE

AND OPEN SOURCE TOOLS

149

the travel intermediates are highly interested in con-

cluding the contract at the maximally lowest price and

deal with the hotels directly rather than buying rooms

from other hotel brokers. If a Hotel Broker b knows

that Hotel A is identical to Hotel B (whose contract

they already acquired) in terms of characteristics that

determine the hotel prices, then this knowledge will

provide the leverage power in negotiating the prof-

itable deal with Hotel A. The proposed price manage-

ment decision support system is designed to help the

hotel broker company acquire the needed knowledge

about Hotel A. In addition, the same approach can also

help in analyzing the proﬁtability of existing deals by

ﬁnding hotels similar in terms of their characteristics

but different in terms of the prices they advertise.

4 SYSTEM REQUIREMENTS

Figure 2: Use case diagram of system usage and behavioral

requirements.

In the previous chapter we have introduced the prob-

lem that hotel brokerage companies face. In this sec-

tion we outline a number of key attributes that the

decision support system has to have to successfully

aid in the decision process. Figure 2 shows the use

case diagram of the system usage and behavioral re-

quirements. The system supports three user types:

data manager, business intelligence analyst (BIA),

and contract manager. The responsibility of the data

manager is to retrieve the required data that are essen-

tial for the decision process. If the roles of the con-

tract manager and the business intelligent analyst are

separated, then the BIA is responsible for selecting

the needed hotel characteristics like location-based

and non-spatial attributes, building of spatial models

and building of the pricing models for hotels under in-

vestigation. BIA is also responsible for generating the

reports in the clear form that the contract manager can

use during his/her deal negotiation. In this paper we

concentrate only on behavioral requirements of data

managers and business intelligence analysts covering

data handling, model construction and price estima-

tion.

In addition, we took into consideration the fol-

lowing key characteristics during the development by

following the general guidelines of DSS and Spatial-

DSS planning (Densham, 1991):

1. The user interface is powerful and easy to use.

2. The system allows to combine analytical models

and data in a ﬂexible manner.

3. The system allows to explore the solution space by

using the models and generating feasible solutions.

4. The system allows to input, represent, and output

spatial data.

5. The system allows output in different forms (maps,

non-spatial statistics).

5 DATA AND PREPROCESSING

The data about hotels was provided by Travel Global

Systems (TGS)

, a travel service provider, and the ho-

tel brokerage company. The data are divided into a

static and dynamic components. The static data in-

cludes the names of hotels, their internal ids, loca-

tion coordinates in World Geodetic System (WGS84),

hotel facilities, room amenities, and hotel categories.

The dynamic component includes the room prices for

one night that customers received during their search

for accommodation, the date of search, and the date of

order. The type of a desired room was not speciﬁed in

the data. Therefore, we assume that the average price

of a hotel is related to a standard room type which

is the most common room type in most of the hotels.

Consequently, we selected only those room amenities

that corresponded to a standard room.

Every amenity and facility types have an internal

identiﬁcation number. However, preprocessing was

required since some of the amenities and facilities that

referred to the same entity were represented by differ-

ent ids and names. For example, Wireless Internet that

was indicated in one hotel referred to High-speed In-

ternet in another hotel. We manually processed all the

amenities and facilities and merged those that referred

to the same entity providing a mapping between the

corporate ids and the ids used in our system.

http://www.travelholdings.com/

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6 SYSTEM ARCHITECTURE

The following sections describe the main components

of the system.

6.1 Java OpenStreetMap Editor

Java OpenStreetMap Editor (JOSM) is a convenient

tool for editing the OpenStreetMap data. However,

its interface (see Figure 3) and functionality is com-

parable to general purpose GIS packages like Open-

Jump

, UDig

or MapWindow GIS

. It can present

the spatial data in different layers and it is an extensi-

ble plug-in based framework. The primary advantage

of JOSM over other general purpose frameworks is

its ability to handle OpenStreetMap data, which is the

primary source of external data for the price manage-

ment decision support system. The provided control

panel (bottom right corner in Figure 3) is our interface

to the decision support system.

Figure 3: Java OpenStreetMap Editor Main View.

6.2 External Data Collection

The data collection process is an integral part of

JOSM. JOSM reads the data from the OpenStreetMap

database by selecting the boundary of the area, and

can save and load the data locally in the propri-

etary OSM XML format. Therefore, in order to

obtain data for a desired region the data manager

uses the functionality provided by JOSM. The Open-

StreetMap data exist in two different types: (1) point

data (nodes), which have coordinates expressed in

longitude and latitude, and (2) ways, which express

areal features that themselves are referenced through

nodes. The geographical features have a list of at-

tributes that come in a key=value form and determine

different characteristics of the feature. The majority

http://www.openjump.org/

http://udig.refractions.net/

http://www.mapwindow.org/

of widely used attributes are ofﬁcially accepted, while

some attributes can be used internally by an applica-

tion. JOSM differentiate between types of features

and attaches a speciﬁc icon to a feature that was rec-

ognized. This is extremely helpful when the user pre-

pares the data for modeling since different types of the

data will be depicted by different icons, which will fa-

cilitate the data management. For example, hotels are

tagged by a key named tourism with the value hotel,

while restaurants are tagged by a key named amenity

and a value restaurant

. An example of how hotels

are represented in JOSM can be seen in Figure 3.

We have introduced our own attribute waterfront

that is assigned to a hotel by the domain expert in case

when the hotel is near a waterfront.

6.3 Data Integration

Figure 4: Data Reader Component.

The data reader component shown in Figure 4 consists

of three parts: (1) Database connection, (2) Layer se-

lection, and (3) Data type selection. The database

connection component allows the user to connect to

the database and select the corresponding database ta-

ble to read the data from. The layer selection allows

the user to select the existing layer or to create a new

layer where the data will be read. The data type se-

lection allows the user to select one of three types of

data supported by the system: (1) General points -

any data that has longitude and latitude coordinates,

(2) OSM points - it is similar to general points but

this data contains an additional ﬁeld for attributes in

a key=value form, and (3) Spatial Models data - the

aerial data that consists of polygons and created by a

spatial model builder (see below). The component fa-

cilitates the data retrieval by asking the user to select

the right column (e.g., id or geometry column) that is

essential during the reading of the data from a table.

After the general spatial data is read and presented in

one of the layers, the user can annotate it with the ofﬁ-

cial or custom attributes thus turning the general data

into the form recognizable by JOSM.

The user can write the data back to the table by

using data writer component shown in Figure 5. The

data will be read from the currently active layer. First,

For a complete list of ofﬁcial attributes please

see http://wiki.openstreetmap.org/wiki/Map_

Features

DEVELOPING A PRICE MANAGEMENT DECISION SUPPORT SYSTEM FOR HOTEL BROKERS USING FREE

AND OPEN SOURCE TOOLS

151

the user selects the database. The data can be written

to an already existing table or to a new table by pro-

viding a name of a table. The user can also provide

the description of the table that will be stored along

with the data. Additional controls are available for

table management, which allow deletion of an exist-

ing table and deletion of contents in an existing table.

This component is useful during external data selec-

tion as described in Section 6.2 or when the subset of

a corporate hotel data is selected for analysis from the

corporate database.

Figure 5: Data Writer Component.

6.4 Spatial Model Builder

Figure 6: Spatial Model Builder Component.

Figure 6 shows the spatial model builder component.

Like the data writer component, it is composed of

two parts. First, the user selects the database and the

source table where the point data is located. Next,

the user provides the name of the model table where

the spatial model will be stored. We decided to sim-

plify the process of spatial model creation by com-

bining a model generation and table write in one step.

To achieve this, we call the database stored procedure

that invokes the spatial model creation algorithm in R

framework using PL/R procedural language for Post-

greSQL

. When the model is generated, it is writ-

ten directly to a table provided in the spatial model

builder component. Spatial model generates spa-

tial clusters using Voronoi tessellation (Okabe et al.,

2000). The Voronoi tessellation decomposes the met-

ric space into regions of equal nearest neighbors using

http://www.joeconway.com/plr/

the set of generating points. This set of points can in

our case be any external data important for determi-

nation of hotel prices (e.g., points of interest, trans-

portation locations). The example of a transportation

model generated by Voronoi tessellation is presented

in Figure 7 using red lines, which are overlaid by the

corresponding hotels shown as white rectangles.

Figure 7: Transportation Model using Voronoi Tessellation.

The size of the cluster may indicate the relative

density of the generating points located around.

Thus, we may answer the following questions using

the spatial model:

(1) How many hotels are located in every region?

(2) What is the area of a region?

(3) Is the hotel located inside one of the regions?

6.5 Price Modeling

The price modeling component shown in Figure 8 is

the most important component available for the ana-

lyst. It allows the analyst to select the hotel features

that would build up the pricing model. The compo-

nent consists of eight parts. First, the analyst connects

to the database (this part is labeled as 1) that holds all

the required information about hotels, prices, ameni-

ties, facilities, and spatial models. Second, the ana-

lyst retrieves the list of hotels he/she is interested in

(labeled as 2) and selects the hotels that would be part

of a model and hotels that would be used for price

estimation (they will not be part of a model). The

parts labeled as 3 and 6 are responsible for retrieval

of amenities and facilities of the selected hotels. The

analyst has the complete control over the ﬁnal list of

amenities and facilities that will be included into the

model. If the hotel category (stars) is important for in-

clusion into the model, the analyst controls this in the

part labeled 4. The part labeled 5 is called Point and

Spatial Model and it is the most versatile part in the

whole price modeling component. The analyst selects

the spatial characteristics using two types of data. The

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Figure 8: Price Modeling.

point data that was used for generating the spatial

model as explained in Section 6.4 and the spatial mod-

els stored in the corresponding tables. Next, the ana-

lyst selects the desired radius size(s). The deﬁnition

of radius sizes allows the analyst to answer such ques-

tions as: How many points of interest/museums/bus

stops are in the radius of 200 meters around the ho-

tel. The hotel density in the speciﬁed radius can also

be calculated. In the part labeled as 7, the analyst

retrieves the hotel prices and speciﬁes the period for

which the pricing model has to be built. Finally, the

analyst saves the generated model and the hotel test

set (if provided) in ﬁles (labeled as 8) with the format

recognized by Weka, the data mining package embed-

ded into the system.

7 USE CASE

In this section we present one of the possible explo-

rative scenarios of the system usage, which may ﬁt

the situation when the contract manager would like to

understand if already concluded contracts with partic-

ular hotels match the objective price of those hotels.

As an examples, we used 168 hotels in the area of

Barcelona, Spain. Exploration is the common way to

understand the data under investigation. Therefore,

the ﬁrst and foremost step is to visualize the locations

of hotels to understand where hotels are situated in

order to decide which hotels are not important for the

inclusion into a model. This step is shown in Fig-

ure 3. Let us suppose that all the hotels were selected

and the pricing model was built using the price mod-

eling component described in Section 6.5. Our prob-

lem is to identify hotels that are similar in terms of

their characteristics, but differ considerably in price.

Hundreds of attributes can be part of a model and the

analyst may use different methods to ﬁnd groups of

hotels with similar attributes. For the sake of sim-

plicity, we implemented a multidimensional scaling

(MDS) (Kruskal and Wish, 1978), which is a power-

ful technique to investigate multivariate data by trans-

forming the multidimensional data into two dimen-

sions by preserving the relative distance between ob-

jects (hotels in our case). MDS allows for observing

similarities of objects using graphical representation.

The analyst can therefore determine what hotels are

similar in terms of their characteristics and also check

their average relative market price as presented in Fig-

ure 9. Let us focus on two hotels that are enclosed in

the red rectangle. They are located far enough from

the majority of other hotels but relatively close to each

other. However, the inspection of their relative market

price (the average of their price divided by the aver-

age of all other hotels) shows that Canal Olympic Ho-

tel price is 0.57 (43% lower than the average relative

market price in the area) having the absolute price of

75.02 euro, while the price of AC Hotel Gava is 1.11

(11% higher than the average relative market price in

the area) with the absolute price of 144.31 euro. The

DEVELOPING A PRICE MANAGEMENT DECISION SUPPORT SYSTEM FOR HOTEL BROKERS USING FREE

AND OPEN SOURCE TOOLS

153

Figure 9: Similarity of hotel characteristics using Multidimensional Scaling.

difference of 69 euro is very substantial and the an-

alyst is interested in further analysis. By inspecting

the hotels’ location we discover that these two hotels

are also located close to each other geographically as

shown in Figure 10. The analyst decides to use re-

gression analysis to estimate the real prices of these

hotels using all other hotels as a price model (train-

ing data). After selecting the best estimator using 10-

fold cross validation on the training data, we apply

Additive Regression with Isotonic Regression on the

two hotels. The results are presented in Figure 11 and

outlined with red rectangles. The price predicted for

the AC Hotel Gava is 90 euro, lower then the original

price, while the price Canal Olympic Hotel is 78.62,

not signiﬁcantly higher than its original price. Based

on these ﬁndings, the analyst should revise the con-

tract with the hotel AC Hotel Gava if the contract

rate is much more higher than the contract of Canal

Olympic Hotel.

8 DISCUSSION

The proposed price management decision support

system stands out by adding three essential features:

(1) the use of JOSM, a GIS-based tool that was ini-

tially designed to support a very narrow task of cre-

ating and editing OpenStreetMap data, (2) the use of

Figure 10: Locating the hotels on the map.

the OpenStreetMap data as an external data in the pro-

cess of determination of hotel prices, and (3) the use

of data mining framework instead of pure statistical

approaches for price analysis. The advantage of us-

ing JOSM over other general purpose GIS tools was

discussed in Section 6.1. However, the other two fea-

tures require further discussion.

Since OpenStreetMap data retrieval is naturally

supported by JOSM, it simpliﬁes the process of data

acquisition. In comparison, Li et al. (2008) applied a

complex process of data collection. The authors used

Virtual Earth Interactive SDK to measure the number

of restaurants and shopping destinations in proxim-

ity to the hotels. To answer the question whether the

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154

Figure 11: Price estimation using Weka.

hotel is located near the beach, Li et al. (2008) used

image classiﬁcation of satellite data and manually val-

idated the results by using on-demand human anno-

tators through the Amazon Mechanical Turk

paid

service. The advantages of using only one source of

data are clear. First, OpenStreetMap data is reach in

content. It contains information about transportation

such as buses and trains, points of interest, restau-

rants and pubs, places of worship and historical sites.

All this is useful for the hotel price estimation. Sec-

ond, the data can be visualized in the system such

that the analyst can decide which parts are relevant

for the analysis. Third, the absence of some func-

tionality such as determining whether the hotel is lo-

cated near a waterfront, is substituted by the domain

expert himself without the need for applying costly

image classiﬁcation methods and paid human anno-

tators. However, the completeness and correctness

of the OpenStreetMap data still need to be closely

examined because the data is contributed by volun-

teers, and because the project was only recently es-

tablished. A recent study (Zielstra and Zipf, 2010)

conducted on Germany data showed that there is a dif-

ference in terms of data completeness between cities

and rural areas. However, the difference has been

decreased extremely in recent years due to the in-

crease in new members willing to participate in the

http://www.mturk.com/

project (the number of participants doubled within

one year and stands for over 200,000 members in

January 2010). Moreover, the data in large cities is

rich enough. In fact, OpenStreetMap data has been

already used in place of proprietary and commercial

data sets (Zielstra and Zipf, 2010).

The advantage of using data mining over pure sta-

tistical analysis is explained by the type of the prob-

lem we deal with. Statistical analysis usually deals

with well structured problems, small data sets, ho-

mogeneity of data, and a conﬁrmatory type of analy-

sis (Hand, 1998). Recall from Section 1, the problem

of hotel price estimation is an ill-structured problem

with different types of data (spatial and non-spatial)

and input parameters. Here, the use of heteroge-

neous data and exploratory analysis using different

algorithms for price estimation are more appropriate.

This is due to the fact that data mining approaches

can handle high-dimensional data with high degree

of sparseness, multicollinearity, outliers and missing

values that statistical approaches cannot easily han-

dle (Brusilovsky and Brusilovskiy, 2008).

DEVELOPING A PRICE MANAGEMENT DECISION SUPPORT SYSTEM FOR HOTEL BROKERS USING FREE

AND OPEN SOURCE TOOLS

155

9 CONCLUSIONS

In this paper, we presented a practical approach for

implementing a price management decision support

system for hotel brokers and hotel intermediates. We

discussed the problem that hotel brokers face and the

requirements for implementing the decision support

system. The solution was simpliﬁed considerably by

using free and open source tools such as Java Open-

StreetMap Editor (JOSM), R statistical package and

Weka data mining framework. We also simpliﬁed the

process of external spatial data acquisition by using

OpenStreetMap data. In our future work, we plan to

enrich the system with other analytical components,

and we will closely work with the hotel domain ex-

perts to identify problems that have not been yet cov-

ered by the current prototype.

ACKNOWLEDGEMENTS

This work was partially funded by the German Re-

search Society (DFG) under grant GK-1042 (Re-

search Training Group “Explorative Analysis and Vi-

sualization of Large Information Spaces”), and by

the Priority Program (SPP) 1335 (“Visual Spatio-

temporal Pattern Analysis of Movement and Event

Data”).

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