application of data warehouse and decision support system in soaring site recommendation
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Application of data warehouse and Decision Support System in soaring site recommendation
Francisco Araque a, Alberto Salguero b, and
Maria M. Abad c
a Dept. of Inform~itica Univ. of Ja6n, Spain
faraque@uj aen.es
b ETSI. Inform~itica Univ. of Granada, Spain
alb erts @ corre o. ugr. es
c Vivegranada S. L. L. Granada, Spain
Abstract Data Warehouse (DW) technology is a new discipline, which has not yet been broadly applied to tourism area, and in the soaring field (paragliding, sailplane, hang gliding, etc) in particular. Site selection process depends on a number of factors, making it a complex decision-making task. It is common for the decision makers to use their subjective judgment and gut feelings based on their experience in selecting the most appropriate place for soaring. The reason is that data for place selection originate from varied sources and are not organized in a format that decision makers can readily use to derive any meaningful information. To solve this problem, the integration of a data warehouse and a Decision Support System (DSS) seems to be efficient to help retrieve data from different databases and information sources and analyze them in order to provide useful and explicit information. Data warehousing is based on online analytical processing (OLAP) concept. The users can also generate data trends over a period of time to make any forecasts. In this paper we will describe how new information technology techniques as data warehousing can be used in the tourism industry. How to analyze the successes and failures of finished gliding routes and how to use the existing data to analyze patterns and trends for new gliding are the problems we have to face.
Keywords: Decision support systems and data warehousing; System architectures and integration; Recommendation systems; Intemet/Extranet/WWW interfaces and developments.
1 Introduction It is obvious that there is no organization running without data. The data can be viewed as tangible assets of an organization just as any physical asset. So, they need to be stored and made available to those who need them when they need them. However, the data by themselves are useless. So, they must be put together to produce useful information, in turn, information becomes the basis for relational decision making. To facilitate the decision-making process, a new development o f database systems was developed called Data Warehouse (DW). The D W can be generally described as a decision-support tool that collects its data from operational databases and various external sources, transforms them into information and making that
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information available to decision-makers (top managers) in a consolidated and consistent manner (Kimball & Ross, 2002), (Inmon, 2002). The persistence of huge amounts of data (possibly distributed and heterogeneous) opens a perspective for various statistical analysis methods which are essential for strategic decisions in tourism.
The soaring community is very extensive. These pilots have had to hone good meteorological sense to maximize their soaring experience. It's worth nothing that sailplanes have soared past the tropopause, and hang gliders and paragliders have reached altitudes above 4,000 m. Hang gliders and paragliders routinely make unpowered cross country trips of more than 50 Km. How to analyze the successes and failures of finished soaring routes and how to use the existing data to analyze patterns and trends for new gliding are the problems we have to face. In order to provide information for predicting patterns and trends more convincingly and for analyzing a problem or situation more efficiently, an integrated Decision Support System (DSS, specifically designed to allow end users to perform computer generated analyses of data on their own) (Ahmada et al. 2004) designed for this particular purpose is needed. In this paper we will describe how new information technology techniques as data warehousing can be used in the tourism industry.
The system will have two different kinds of users: expert and novel ones. By expert we mean those (pilots) who will be able to use DSS for an easier choice of the appropriate route for a precise location and time. They will be able to select the adequate parameters according to their own experience, and depending on the kind of flight they intend to perform. Novel ones are Tour operators and travel agencies. They are supposed to use a much simpler DSS, with which they will be able to generate predefined or simple reports without having deep knowledge in soaring. Tour operators and travel agencies can use DSS in order to foretell (taking into consideration both former flights data loaded in the DW and the one week weather forecast) whether the conditions for soaring routes will be favourable and in which geographical areas they could be performed (in our example the data correspond to Granada and its province). In this way, trips can be arranged and activities can be organized with great reliability.
The use of new technology such as Data Warehousing, Decision Support Systems, Data Mining, data integration, etc. have been proposed previously in many fields, not only in tourism (Kirkg6ze & Tjoa, 1998), (Haller et al. 2000), (Moura et al. 2004). In the first reference the use of DWs and Data Mining as a basis for strategic decision in tourism is proposed. In the second one the Integrating Heterogeneous Tourism Information data sources is addressed using three-tier architecture, consisting of a Data Source Adapter Layer, a Mediation Layer and a Client Layer. And in the last one a Real-Time Decision Support System for space missions control is put forward using Data Warehousing technology.
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First reviewed in this paper are the concepts of the DW and On-Line Analysis Processing (OLAP). The method of creating a data mart is then shown, changing the data in the DW into a multidimensional data cube and integrating the DW with a DSS. Finally, an application example is given to illustrate the use of the Tourism Management Decision Support System developed in this study.
2 Data Warehouse concept
(Inmon, 2002) defined a DW as "a subject-oriented, integrated, time-variant, non-volatile collection of data in support of management's decision-making process." A DW is a database that stores a copy of operational data whose structure is optimized for query and analysis. The scope is one of the DW def'ming issues: it is the entire enterprise. Related to a more reduced scope, a new concept is defined: a data mart is a highly focused DW whose scope is a single department or subject area. The DW and data marts are usually implemented using relational databases (Hammer et al. 1995), (Harinarayan et al. 1996) def'ming multidimensional structures.
There are two approaches to build a DW. In the first approach, stand-alone data marts assigned to individual business units or processes are developed and later integrated into an enterprise-wide DW. In the second approach, a complete warehouse in the form of distributed data marts is build. These data marts are populated with data either at the time of initial development or at different stages depending on the availability of time and resources (Inmon, 2002), (Kimball & Ross, 2002).
The generic architecture of a DW is illustrated in Figure 1 (Chaudhuri & Dayal, 1997). Data sources include existing operational databases and flat files (i.e., spreadsheets or text files) in combination with external databases. The data are extracted from the sources and then loaded into the DW using various data loaders and ETL tools (Araque, 2002), (Araque, 2003a). The warehouse is then used to populate the various subject (or process) oriented data marts and OLAP servers. Data marts are subsets of a DW categorized according to functional areas depending on the domain (problem area being addressed) and OLAP servers are software tools that help a user to prepare data for analysis, query processing, reporting and data mining.
Thus, a DW coupled with OLAP enables managers to creatively approach, analyze and understand the problems. The OLAP analyzes data using special DW schemas and enables users to view data using any combination of variables. The DW system is used to provide solutions for gliding problems, since it transforms operational data into strategic decision-making information. The DW stores summarized information instead of operational data. This summarized information is time-variant and provides effective answers to queries such as Will the air be soarable? Will thunderstorms be a factor?, Strong boundary layer winds can disrupt the desired vertical column of unstable air and destroy thermal lift? and so on.
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Monitoring & Admnistration ~ c---q
~ Metadata Repository
OLAP b x Servers ] " ~ Data Warehouse "
sources Transform \
Operational Refresh ~ Serve "~
Data sources Data Marts Tools
Fig. 1. A generic DW architecture. (Chaudhuri & Dayal, 1997).
To extract this information from a distributed environment, we would need to query multiple data sources and integrate the information at a particular point before presenting the answers to the user. In a DW environment, such queries find their answers in a central place, thus reducing the processing and management costs. After the initial loading, warehouse data must be regularly refreshed, modifications of operational data since the last DW refreshment must be propagated into the warehouse such that warehouse data reflect the state of the underlying operational systems (Araque & Samos, 2003), (Araque, 2003b). To make flight decisions, pilots use all the information they can get, and they want it in usable form.
3 Design Process
There are two main ways to implement a conceptual multidimensional data model, below mentioned. The first approach is adopted in this work as it fits with the scope of this research. A more detailed information about different design processes is provided by (Kimball & Ross, 2002).
3.1 Data Mart
The conceptual multidimensional data model can be physically realized in two ways: (1) by using a trusted relational database approach (star schema/snowflake schema) or (2) by making use of a specialized multidimensional database.
The snowflake schema is adopted here mainly because of its clarity, convenience and rapid indexing ability (Kimball & Ross, 2002). The other methods are not so suitable here, since they involve more or less much more complicated transformation,
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which does not appear to be justified in our situation. The snowflake schema is a variation of the star structure, in which all dimensional information is normalized (i.e., dimension tables have sub-dimension tables to avoid dependency of non-key attributes), while keeping fact table structure the same. To take care of hierarchy, the dimension tables are connected with sub-dimensions tables using many-to-one relationships. A snowflake schema contains two types of tables: fact tables and dimension tables (Kimball & Ross, 2002). Fact table contain the quantitative or factual data about soaring entity. Dimension tables are smaller and hold descriptive data that reflect the dimensions of an entity. SQL queries then use predef'med and user-defined links between the fact and dimension tables within the star schema, with constraints on the data to retum required information. Instead of developing a full-scale DW, a data mart is developed for the purpose of this research.
The data mart design essentially consists of three steps as follows:
1 Identifying facts and dimensions. Facts represent quantitative (or factual) data about a business entity while dimensions contain descriptive data that reflect the dimensions of that entity. In other words, fact data contains the physical information about a factual event and the dimension data shows the description of that fact.
2 Designing fact and dimension tables. The dimension tables are connected with the fact table by foreign keys (FK; a foreign key is a primary key of one entity that is also an attribute in another entity). As a result, a fact table contains facts and foreign keys to the dimension tables. The relationship between a fact table and dimension tables is illustrated in Figure 3. In a query, the system first accesses one or more dimension tables, and then accesses the fact table (Kimball & Ross, 2002).
3 Designing data mart schemas. The schema is a database design containing the logic and showing relationships between the data organized in different tables (or relations). A data mart is composed of a central fact table and a set of surrounding dimension tables.
3.2 Our System
The pilots should heighten their meteorological intuition (have had to hone good meteorological sense) to maximize their soaring experience. All by tapping the resources of the free air without a powered assist after release. Soaring pilots get their lift from two main sources, terrain and instability. Ridge and wave soaring is done in the region downstream from mountains or other prominent terrain. Thermal soaring uses instability in the flee, open atmosphere.
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The data sources that feed the Data Mart are:
• The US National Weather Service Webpage. We can access weather measurements (temperature, pressure... ) by the hour in every airport in the world. Thus, we can obtain parameters such as temperature, humidity general conditions, pressure, and wind speed and direction. These parameters will determine the subgroup of possible flight areas appropriate to begin it.
• To obtain a more detailed analysis and to select the best zone to fly pilots can now add another tool to their arsenal, the SkewT diagram. The SkewT, or sounding chart, is a vertical snapshot of temperature, dew point and winds above a point on the earth. These diagrams make possible to obtain valuable information about cloud bases and tops, fog, inversions, and winds. This tool also provides a set of index calculated from de data of the diagram. For example, we can find an index (LIFT) that shows numerically the potential capacity of the atmosphere to generate vertical movements of air masses.
• By means of a program, pilots send via intemet the flights they have performed. This information can be obtained through the GPS Equipments (Global Positioning System) that help navigate. As flight routes influenced by different atmospheric status, the ideal conditions for every flight area could be determined.
• These flights are performed in a precise relief, which is determinant at the time of applying the tests. This information can be obtained from the SRTM data (Shuttle Radar Topography Mission). These are a set of folders containing tables that show the complete Earth heights.
• When analyzing the flights, the features and capacities of the different models of gliders. Identical flights performed with a lower capacity glider imply a greater effort. These features can be obtained for the different paraglider models through the web page para2OOO.org.
The fact table contains:
• The first item to analyze in this case is the one corresponding to the displacement of the pilots. This is reflected on the Data Mart scheme by the table Displacement. The rest of data revolve around this information and they are distributed or organized as follows.
The dimension tables are:
• All adjoining upward displacements make up a whole that we will call Lift. This dimension characterizes the degree of use of the upward currents found by the pilots in the flight.
• The sounding dimension refers to the data of the diverse atmosphere layers. Data associated to a displacement correspond to the one obtained at the closest Weather Station (in time and location) in which the displacement takes place. By
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dividing the atmosphere in vertical sections we can easily delimit the type of curve that defines the general characteristics of a specific layer.
• The Weather dimension corresponds with the weather data obtained in different airports. They just analyze the features of the surface mass of air, but if necessary, they can be more precise with respect to time and space. A certain displacement will be associated with the information of the dimension taken from the nearest weather station
• In addition to the present atmosphere condition, previously stored forecast information can be stored by means of the Weather-forecast and Sotmding-forecast dimensions. Other than the current information, they also indicate the frequency rate with which the prediction was made.
• When specifying displacements the start position and end position dimensions are used. Movements are classified according to the type of relief in which they take place, together with the standard classification subject to administration constraints concerning the territory. Generally ascending currents are favoured in rugged areas and more obstacles can be expected to be met in fiat land.
• Since the evaluation of the atmosphere conditions for a specific area is made through the information obtained from the real flights that took place in identical conditions, introducing variables that allow us to asses the pilot abilities becomes highly recommended. Through the pilot parameter it is possible to provide a report showing the pilot experience, degree of skill and the kind of sail used.
A soaring model with dimensions and fact tables properties is shown in Figure 3. The core part of any star schema is the fact table, which is shown as Table 1. There is one fact table and eight dimension tables shown in Figure 3. These dimension tables are connected with the fact table by foreign keys, which can keep all the views coherent.
Table 1. Fact table
Foreign Keys Key_Pilot, Key_StartPosition, Key_EndPosition, Key_Lift Key_Sounding, Key_SoundingForecast, Key_Weather, Key_WeatherForecast
Mesures Distance, Drop, Interval
Our DW architecture is illustrated in Figure 2. It can be seen that data sources include existing operational databases, web pages and GPS sources. The data are extracted from the sources and then loaded into the DW/DM using ETL tools. The warehouse is then used to populate the various subject (or process) oriented data marts and OLAP servers (sotiware tools that help a user to prepare data for analysis, query processing and reporting.
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Several software choices are available to build a data mart such as MS Access, Oracle or any other database management software. MS Access is selected in this research for its user friendliness and easy availability. It is important to note that OLAP software packages such as MS OLAP Server or SQL Server are also available in the market. These software packages allow users to write their queries using any chosen analytical model. The advantage is that the users can use different graphical wizards to easily write their queries and display results in different graphical formats. Since our intention is not to develop a commercial software product, we utilized MS Access. Moreover, the main thrust of this research is not the techniques or tools used in the front-end of the model, but the technique of data warehousing itself.
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conditions in order to filter the output report. According to the parameters used in the example shown in Figure 3, the output report contains all the movements in an ascent that fit the following criteria: altitude is longer than 100 meters, the ascent took place in mountains of Granada, LIFT was lower than 1 and the surface wind orientation was between 90 ° and 260 ° . The output report can be exported to standard formats, so that it can be readable by GPS mapping software such as OziExplorer. Figure 5 shows a snapshot of the tridimensional view of the output report.
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For non-expert users, such as tour-operators or travel agencies, the output report can be used as filtering information for their online reservation systems. While logging reservation systems do not need supplementary information as weather forecast, other products in the tourism industry, such as eco-tourism can take a tremendous advantage of last-minute DW, such as the soaring DW or other DW def'med for sport activities subject to weather conditions. The system constitutes an enhanced modification of the current infoTours (Abad et. al. 2001), an online availability and reservation system of the travel agency ViveGranada S.L.L. (www.vivegranada.coLm., www.vivespain.com). It allows to query a last-minute DW and use the output report to filter the online availability of open doors activities offered by the online reservation system. Figure 2 shows the architecture.
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Fig. 5. Soaring Routes Display in 3D
ViveGranada has recently developed a DSS, WebTour, based on a reservation data mart. The system can be used not only by its staff but also by customers and providers accessing the extranet. Thus, travel agencies that use the online reservation system and lodging owners or open-door activities organizations can query the DSS in order to improve the making decision process. The functionality and interfaz of WebTour were designed according to the opinion of executives of ViveGranada. Opinion given by the current users has been used as a preliminar research in order to develop the prototype of Tourism Management Decision Support System (TMDSS). Structured interviews will be made for the design of the final system.
5 Conclusions and future work
In this paper, the development of a prototype for TMDSS employing the integration of the DW technology with an OLAP is delineated. It has been illustrated, through a real case application in soaring field. TMDSS is advanced at least in the following aspects: enables insight to be gained into the factors having impacts on tourism management activities that will help managers in making decisions to improve management performance, users can interact with the computer so that the users can constantly refine the view of data to pursue various ways of thought, provides extremely fast response to queries, and is multidimensional so users can view multiple perspectives and can also choose different view angles. In short, TMDSS is able to assist tourism managers and pilots by providing accurate and timely information for soaring decision-making. Our future research issues include the following: (1) To
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develop a data mart where users can add attributes to meet their requirements; (2) Include in the DSS other advanced capabilities like: exception reporting, drill-down, and so on.
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Acknowledgements This work has been supported by the Spanish Research Program under project TIN2004-06204-C03-02. The authors would like to thank Ram6m Morillas Salmer6n (three times paramotor world champion www.ramonmorillas.com) he has given to this work.