Romania has a huge tourist’s potential, but currently it is too little valued and exploited. As a result, one of the strategic developments of the economy aimed the tourism industry. The strategic decisions are based on different trends obtained from sophtourism industry, data mining techniques, distributed databases

Barala Adina

Danubianu Mirela

Socaciu Tiberiu

English

Research Papers in Economics

922 
TOWARD A DISTRIBUTED DATA MINING SYSTEM FOR TOURISM INDUSTRY 
 
Danubianu Mirela 
Stefan cel Mare University of Suceava Faculty of Electrical Engineering andComputer Science 13 Universitatii 
Street, Suceava mdanub@eed.usv.ro mobile: +40744.547164 
Socaciu Tiberiu  
Stefan cel Mare University of Suceava Faculty of Economic Science and Public Administration 13 Universitatii 
Street, Suceava socaciu@seap.usv.ro 
Bărâlă Adina  
Stefan cel Mare University of Suceava Faculty of Electrical Engineering andComputer Science 13 Universitatii 
Street, Suceava adina@eed.usv.ro 
 
Romania has a huge tourist’s potential, but currently it is too little valued and exploited. As a result, one of the strategic 
developments of the economy aimed the tourism industry. The strategic decisions are based on different trends obtained from 
sophisticated analysis of data. Data mining helps businesses sift through layers of seemingly unrelated data for meaningful 
relationships, where they can anticipate, rather than simply react to, environment challenges. The aim of this paper is to 
present two models of data mining systems, considering that the data is processed from a distributed database. 
 
Keywords: tourism industry, data mining techniques, distributed databases 
 
JEL code : C89, D89 
 
Introduction 
Tourism is a great income generator due to an increased demand for its services. Their components range from 
quality and wide range of transportation to infrastructure, accommodation, food and beverage, support services and 
travel distribution services.  
Romania has a huge tourist’s potential, unfortunately, too little valued and exploited. As a result, one of the 
strategic developments of the economy aimed the tourism industry. But strategies are based on different trends 
obtained from sophisticated analysis of data. Providing the managers in the tourism industry with information about 
and insight into the existing data is the key function of the data warehouse systems [1]. 
Data mining - techniques for exploration and analysis of large quantities of data in order to discover meaningful 
patterns and rules - helps businesses sift through layers of seemingly unrelated data for meaningful relationships, 
where they can anticipate, rather than simply react to, environment challenges. 
A system which enables the use of data mining techniques on data stored in a data warehouse is ideal for high 
quality analyzes to support strategic decision. But designing and implementing a Data Warehouse is a complex and 
expansive  process [2],  so we  can  apply  the  data  mining  algorithms  on  large volumes  of  data  from  relational 
databases. 
The aim of this paper is to present the opportunity to use data mining methods on data from tourism and also to 
present two models of data mining systems, considering that the data is processed from a distributed database. 
 
Data mining 
Data mining is the exploration and analysis, by automatic or semiautomatic means, of large quantities of data in 
order to discover meaningful patterns and rules. So, data mining is defined as the process of extracting interesting 
and previously unknown information from data, and it is widely accepted to be a single phase in a complex process 
known as Knowledge Discovery in Databases (KDD). 
This process consists of a sequence of the following steps [3]: 
  data cleaning – to remove noise and irrelevant data 
  data integration – where multiple data sources are combined 
  data selection – for retrieve from the database only the relevant data for the analyze 
  data transformation – where data are transformed or consolidated into forms appropriate for mining 
  data mining – the phase where the algorithms are applied in order to extract data patterns 
  pattern evaluation – to find the interesting patterns who representing new knowledge 
  knowledge presentation – when the visualization techniques are used to present the mined knowledge to 
the user 
In order to ensure that the extracted information generated by the data mining algorithms is useful, additional 
activities are required, like incorporating appropriate prior knowledge and proper interpretation of the data mining 
results.  923 
Figure 1 presents these phases and the most important interdepencies between them. 
Figure 1. The steps of the CRISP-DM process (adapted from Wirth and Hipp) 
 
Information Systems Used in Tourism Industry 
Information technology was initially viewed by the tourism industry as a back-office function that supports the 
finance  and  accounting  areas.  The  industry  has  advanced  far  beyond  this  view  during  the  past  decade.  As 
information is vital for tourism industry, effective use of Information Technology is necessary. 
An Information System regarding tourism activity should have some characteristics. It should collect, select and 
process information that is internal to the tourism activity coming from entities related to this sector such as 
National Tourism Agency and it should have subsystems that receive data from other business sectors, such as 
passengers landed from  regular  and  nonregular  flights, passengers  flying  with  low-cost.  It  should  support  the 
decision-making process via integration of data from various sources, integrating them in a manner that permits 
analyses and comparisons between tourism indicators for the different regions of the countries and for different 
time periods. Also it should include historic information, such as the amounts of overnight stays per country of 
residence and for all types of hotel establishments throughout the 12 months of the year for every region [4]. It 
should also support the specialists who process and use tourism demand forecasts. As such, it detects the 10 
countries with the highest tourism demand in the previous year. Taking into account the 10 countries listed and the 
forecasting methods suited to the specificity and nature of the data, the system prepares the necessary data to be 
processed by the specialists who create forecasts for tourism demand. [5] 
Currently, the most used information systems in Romanian tourism industry are the front-office systems and the 
reservation systems. 
Front-office information systems are those data processing systems that provide reports in visual or written form. 
They are used mainly in the management of tourist accommodation (hotels, motels, hostels or cruise ships) or in the 
travel agencies activities. These systems may be used for: tourists registration when the personal data about tourists 
are collected; marketing of various tourism products, such as rental cars; rooms management, when are collected 
and processed data regarding the rooms status, (allows instant viewing of room availability for all room types, 
indicates whether rooms are dirty or clean, allows rooms to be placed out of inventory or out of order to restrict 
rental) and tracks of revenues, providing transaction processing and obtain information about any debts and credits 
in relation to customers 
Information Systems Used for Reservations provide rapid access to information and ensures the accuracy of this 
information. They bring information services, booking and selling and are used both by individual tourists and 
travel  agents  or  commissioners.  Most  often  this  type  of  systems  uses  Web  technologies.  These  systems  use 
hardware and software specific to conduct them activities. Although providers of tourist services in Romania 
currently use such systems for ticketing most, is well to remember that these systems can be used for marketing or 
management activities. 
In the tourism industry knowing the guests - where they are from, how much they spend, and when and on what 
they spend it- can help a company to formulate marketing strategies and maximize profits. Due to technological 
development touristic companies have accumulated large amounts of customer data, which can be organized and 
integrated in databases that can be used to guide marketing decision [6]. Since identification of important variables 
and relationships located in these consumer-information systems can be a difficult task, some companies have 
attempted to raise the power of information by using data mining technologies. 
 
How can use Data Mining Technologies in Hospitality 
Hospitality is used to describe hotels and similar accommodations as well as restaurants and catering organizations 
(Holloway, 2006) and represent a very important aspect of the tourist industry. 
Business 
Understanding 
Data 
Understanding 
Data  
Preparation 
Modeling 
Evaluation 
Deployment 924 
If hospitality organizations want to compete successfully, they must do so by using technology to drive value to 
both the customer and to the firm.”[] 
In this area Information Systems have been used to assist the delivery of hospitality services. Some of the key ways 
are (Buhalis, 2003): improved capacity management and operations efficiency; central room inventory control; last 
room availability information; yield management capability; marketing, sales and operational reports; tracking 
frequency flyers and repeat hotel guests; internal management of operations from transactions to human resources. 
Most of the items on the above list apply only to hotels and accommodation providers 
In order to make high quality marketing research and planning data-mining technology allows hotel companies to 
predict consumer-behavior trends, which are potentially useful for marketing applications. 
The tasks performed by data mining can be grouped into the following five categories. 
Classification arranges customers into pre-defined segments that allow the size and structure of market groups to be 
monitored. Also, predictive models can be built to classify activities. Classification uses the information contained 
in sets of predictor variables, such as demographic and lifestyle data, to assign customers to segments. 
Clustering group customers based on domain knowledge and the database, but does not rely on predetermined 
group definitions. This function is beneficial because it aids hoteliers in understanding who their customers are. For 
example, clustering may reveal a subgroup within a predetermined segment with homogenous purchasing behavior 
(a subgroup of holiday shoppers within the transient segment) that can be targeted effectively through a specific ad 
campaign with the scope that the members of the subgroup will increase their number of stays or become more 
loyal. On the other hand, clustering may indicate that previously determined segments are not parsimonious and 
should be consolidated to increase advertising efficiency. Information such as demographic characteristics, lifestyle 
descriptors, and actual product purchases are typically used in clustering.  
Deviation detection uncovers data anomalies, such as a sudden increase in purchases by a customer. Information of 
this type can prove useful if a hotel corporation wants to thank a guest for her or his recent increase in spending or 
offer a promotion in appreciation. Marketing managers may also attempt to draw correlations between surges in 
deviations with uncontrollable business-environment factors that are not represented in the database.  
Association entails the detection of connections between records, driven by association and sequence discovery. 
For example, a possible detected association may be that a particular segment’s average length of stay increases 
after a specific advertising campaign. Another association task could be employed in an effort to determine why a 
specific  promotion  was  successful  in  one  market,  but  ineffective  elsewhere.  Specific  information  regarding 
customer-purchase histories is necessary to formulate probabilistic rules pertaining to subsequent purchases.  
Forecasting predicts the future value of continuous variables based on patterns and trends within the data. For 
instance, the forecasting function can be used to predict the future size of market segments. With forecasting one 
can also use data trends to project which hotel amenities are of growing importance to consumers and will be key 
drivers of the future perception of value of consumers. 
 
Distributed Model for a Data Mining System  
Data  Mining systems have  the  following  characteristics: they must not limit the size of data sets, the 
performances are optimized for large data sets and they are enough flexible to use various techniques of 
data mining. Also they offer support for multi-user access and requires a total control over data access. 
Finally they provide management and maintenance at a distance. 
The basic elements of a data mining system of data are: user interface, the specific data mining services, 
data access and data itself. 
Usually, Data Mining systems are built using client-server architecture, with different distribution on the 
two components of the items listed above. 
In order to achieve a prototype of a data mining system for hotel industry of Bucovina, we proposed two 
models of system architecture and we have studied some of their advantages and disadvantages. 
We started from the reality that each accommodation establishment manages its own data. Passing over 
specific needs all these work with databases containing data on customers, on services requested, on the 
amount spent, so on... If these systems allow a part of their data, in terms of ensuring data privacy, to be 
used for analysis, then it is possible that projections by necessary attributes of the tables to be available for 
sharing. If individual systems are connected through a communications network can assume that we are 
dealing with a heterogeneous distributed database, as shown in Figure 2. 
In order to study the two models we chose ten accommodation establishments that have agreed to share the 
data available for this purpose. 
The first stage was a selection of required data. For that we have applied a projection by a list of fields 
with the same meanings of the tables, corresponding to the following model: 
 
( ) Õ
nk k k c c c k T
,... 2 , 1                                                                  (1) 925 
 
Where (c1k, c2k..cnk ) represent the list of n atributes required from table Tk.
 
Figure 2. Distributed Database Architecture 
 
The set of all these projections in a site form a fragment of the distributed database. Starting from this 
condition were analyzed the following situations. 
In the first case, were installed in each site user interfaces and services suitable for data mining. Thus it 
was possible to apply local data mining methods (e.g. discovery of association rules). Local results were 
replicated in a single node and were combined to obtain the global solution. It is obvious that in the site 
where data were replicated were required additional operations in order to verify and validate the final 
results. 
For example, for association rules was necessary to calculate the global support and confidence. In this 
case there are two major disadvantages. The first is related to the small volume of data processed on the 
local sites, which may lead to partial results inconclusive. The second disadvantage is the need to conduct 
further operations in the site where the results are collected. 
The other option is a replication of all fragments in a single site where the specific components for data 
mining systems are installed. On these aggregated data we apply different methods of data mining. 
The advantage of this approach lies in that additional operations for further validation of the global results 
are eliminated. However, there is a drawback related to large volume of data transferred on the network. 
 
Conclusion and Future Work 
In this paper we have shown that data mining techniques can be applied successfully in the field of tourism, 
especially in connection with strategic marketing. We also kept in mind that these techniques can be applied to data 
sets from various sources, which can be successfully treated as fragments of a distributed database. In this context
we have examined two models of distribution of components of a data mining system and how we can apply 
specific methods and we have underline their advantages and disadvantages. 
 
References 
1. M. Danubianu, T. Socaciu, A. Barila Some Aspects Of Data Warehousing In Tourism Industry, accepted for 
publishing in The Annals of the “Stefan cel Mare” University Suceava. Fascicle of The Faculty of Economics and 
Public Administration, 2009 
2.  M.  Danubianu,    Advanced  Information Technology  –  Support  of  Strategic  Decision  in  Romanian Tourism 
Industry , in Proceedings of IECS 2009, Sibiu , Romania 
3. R. Wirth and Hipp, ( 2000) J. CRISP-DM: Towards a standard process model for data mining. In Proceedings of 
the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining, pages 
29-39, Manchester, UK 
4. A. Poon Tourism, Technology and Competitive Strategies. Wallingford: CAB International, 1993
5. C. Ramos, F. Perna, Information system for Tourism Activity Monitoring and Forecasting  Indicators as an 
experience for Portugal, Tourism and Hospitality Research, February, 2009 
DB 
DB 
DB 
DB 
DB 
Site 3 
Site 5 
Site 4 
Site. 2 
Site. 1 
Network 
Comunication 926 
6. M. Danubianu, V. Hapenciuc Improving Customer Relationship Management In hotel industry by Data Mining 
Techniques, Proceeding of “Competitiveness and Stability in the Knowledge-Based Economy”, Vol: CD, 30-31 
Mai, 2008, Craiova, Romania, ISSN/ISBN: 978-606-510-162-3, Pagini: 2444-2452 
   

Advanced Information Technology – Support of Strategic Decision in Romanian Tourism Industry ,

Barila Some Aspects Of Data Warehousing In Tourism Industry, accepted for publishing in The Annals of the “Stefan cel Mare” University Suceava. Fascicle of The Faculty of Economics and Public Administration,

Hapenciuc Improving Customer Relationship Management In hotel industry by Data Mining

Information system for Tourism Activity Monitoring and Forecasting Indicators as an experience for Portugal, Tourism and Hospitality Research,

J. CRISP-DM: Towards a standard process model for data mining.

Tourism, Technology and Competitive Strategies.

TOWARD A DISTRIBUTED DATA MINING SYSTEM FOR TOURISM INDUSTRY

http://steconomice.uoradea.ro/anale/volume/2009/v4-management-and-marketing/188.pdf

TOWARD A DISTRIBUTED DATA MINING SYSTEM FOR TOURISM INDUSTRY

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