Big Data Implementation in Tourism. Noreen Mhd Hashim. Journal of Information Systems Research and Innovation 10(3), 64-68, December 2016

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1 Big Data Implementation in Tourism Noreen Mhd Hashim Author(s) Contact Details: Universiti Teknologi Malaysia, Skudai, Johor, Malaysia Published online: December JISRI All rights reserved Abstract This paper proposes the definition of big data in tourism. Several methodology will be analyze to suit in order to retrieve data, based on customer behavior. According to previous research papers, there are several methodology that has been used to conduct an analysis on big data. This paper will analyze based on three type of methodology which are User Generated Content (UGC), the Indicator and another one is Smart Tourism. By focusing on these three methodology, this study wants (1) to identify methodology been used to understand the volume big data in tourism (2) Tourism implementation methodology. Keywords: Big Data, Tourism, User Generated Content, Smart Tourism. because sometime the information and respond post of the activities in the websites not holistic about the place that they want to visit. However in different perspective, tourism information can now be delivered to tourists in a more timely and accurate manner via those channels (smart tourism), helping them to more effectively make decisions (Cui, Lin, & Huang, 2015). Smart tourism is a new buzzword applied to describe the increasing reliance of tourism destinations, their industries and their tourists on emerging forms of ICT that allow for massive amounts of data to be transformed into value propositions, Figure 1 show the component and layers for smart tourism (Gretzel, Sigala, Xiang, & Koo, 2015). Figure 1: The component and layers for smart tourism 1. INTRODUCTION Big data is a term that describes a huge of data. The data will include structured and unstructured data. This massive data will use by many industry and organizations depends their needs. Some organizations use geographical to determine the potential success for new location prior expanding the operation or maybe other organizations use for analyzing. Other than that, big data provides new insight about interests and behaviors of customers (Shafiee & Ghatari, 2016) so that can view in any dimension of opinions and needs. Most information are very useful especially for tourist and travel agencies. Essential for several reasons: not only in the preparation of the trip (choice of hotel for example), but also during the trip (choice of restaurant) (Chareyron, Da-Rugna, & Raimbault, 2014). With this information tourist ease to planning their travel itineraries and have more knowledge about the place there are going. Traditionally, they seek suggestions from travel agencies, tourism official websites or tourism experts blogs (Li, Bao, Song, & Duh, 2016). Regardless, some the information not that much and need to discover this is In industry perspective, big data can manage and organize their tourism plans better (Shafiee & Ghatari, 2016) and meet customer satisfaction. The information that usually they try to share with the customer is funding critical infrastructure such as airports, roads and hotels (Li et al., 2016) other than that popular question that they will be ask is interesting places either beach or mountain area. Community websites sharing photographs, opinions, reviews or ratings comments get an increasingly important role in the current internet ecosystem. TripAdvisor, Instagram, Flickr, Panoramio, Hotels.com, booking.com, expedia websites became essential for many users of social networks, in particular regarding their vacations or business trips, Each one of these sites have its own informations, communities and Page 64

2 characteristics, Figure 2 shown the photography sharing website facts and figure 3 shown TripAdvisor and Hotels.com facts (Chareyron, Branchet, & Jacquot, 2015; Chareyron et al., 2014). This facts shown that how recent people to find the information by website medium. Figure 2: Shown the photography sharing website facts Figure 3 shown TripAdvisor and Hotels.com facts Besides that, tourist tendencies, perceived experiences and attraction factors can be specified via analysis of UGC data generally at the destination level and specifically at the level of special perspectives (Shafiee & Ghatari, 2016). However, this high volume data is difficult to analyze, therefore, a Social-Aware visualized Analytic system (SAT) for Tourist behaviors developed, that aims to automatically collect, clean and integrate all forms of tourists activity data from multiple social media sites, further manage the multiple facets of the activity data for efficient analytical query processing, and allow users to visually and interactively explore the trajectory and the public opinions that tourists have had on the attractions, figure 4 shown system architecture using SAT (Li et al., 2016). Figure 4 System architecture In travel and tourism, where planning, spontaneity, risk, adventure and expectation all weigh so heavily on the journey (World Tranvel and Tourism Council, 2012). Therefore, tourism industry need big data as the information as a guide. 2. METHODOLOGY In order to gather the tourism information, there is a methodology or tools that been use to handle massive data. User Generated Content (UGC) UGC is defined as any form of content such as blogs, wikis, discussion forums, posts, chats, tweets, podcasts, digital images, video, audio files, advertisements and other forms of media that was created by users of an online system or service, often made available via social media websites. Definitely companies such as ebay, Google, Facebook, and LinkedIn have been established on the basis of big data since the beginning. Because users produced high values of data in new formats (unstructured data, data produced by a click on different menus of websites, web server to record events, relations of social networks, and the results of controlled experiments) they had no way except adopting new management methods and technologies to organize this volume of data (Shafiee & Ghatari, 2016). With the popularity of social media, there has been an increasing amount of user generated content (UGC) on the Web, including an abundance of information posted by tourists (Li et al., 2016). The information obtained from online user generated content (UGC) is the smart basis of business in a city. Other that than tourist using Point of Interest Mapping (POI) to searching the information. POI mapping is a specific location that someone may find useful or interesting. This part automatically maps each touristrelated UGC to an exact place, by the approach of geoinformation mapping and approximate string matching (Li et al., 2016). To visualize analytics by using UGC first should have a task description such as identify the popular tourist places, present the statistics of tourists at each place and discover the difference of the hot tourist places at different time. Then next to visual analytics design and dynamic data loading, to accomplish the tasks, composite visualization design with integrated views and superimposed views to visualize the tourist-related social media data, final step integrate coordinate views to produce the visualization to understand the tourist behavior from multiple dimension. For Google Maps View the tourism attraction corresponding to each UGC to an exact geographic place on the map and presents the common tour itineraries. Apart from the Google Maps View, an additional Temporal View is designed to help users understand the periods that tourists have stayed in the tourist places and then users can easily get the information about the duration of the selected tourist s staying at each place. To summarizes the tourist related contents using world cloud view. As a result, users can easily catch the keywords of public opinions on a tourist place or a group of tourist places at a certain time that they select on the Google Maps View or in the Filter/Search View (Li et al., 2016). The Indicator The images and comments by social networks through the tags associated to images and comments provide Page 65

3 extra information for the users. Every information from the users on a specific tourist site characterize precisely the practices, experiences and the satisfaction of tourists in this destination. Many studies have emerged in recent years on the use of data from community websites, such as Flickr, Panoramio, or Twitter. However, all these researches are facing the same issue: how to demonstrate that these data provide new knowledge. To mix data from multiple social networks, and also administrative data, such as the number of inhabitants, hotels, restaurants, and the numbers of beds (Chareyron et al., 2015). Therefore, to analyse data using the indicators, one researcher have done a scale of district and integrated data that provided by French National Institute for Statistics and Economic Studies (Insee). They created tourism indicator shown in Figure 5. Indicators focus on the level of use of social networks in tourist destinations Figure 5 Tourism Indicator Tourist function rate: a classic indicator based on the number of accommodations over the number of residents. Two indicators based on Trip Advisor: Indicators focus on a penetration rate based on Flickr and Instagram. Indicators to measure the correlation between the different sources of information INSEE) Number of Flickr pictures/ Insee district population Number of Instagram pictures/ Insee district population. Number of Flickr users/ Number of users on Tripadvisor (commenting Attractions) Number of total reviews on Tripadvisor / (Number of Flickr users + Number of Instagram users) Figure 6 shown France with the indicator, on this map can visualize the main international tourist area in red. The proportion of comments by foreign is higher than in the blue area (Chareyron et al., 2015). Figure 6 France with the indicator Indicators use TripAdvisor separating the number of accommodation and the number of reviews on these hosting. Indeed the number of reviews is variable, depending on the type of accommodation and provides additional information about the structure of the area. Number of Tripadvisor Attractions/Number of TripAdvisor accommodation & restaurants to measure the relationship between tourist services and attractions. Number of TripAdvisor French reviewers/total of TripAdvisor reviewers, to evaluate the international dimension of a destination. Another way to understand the international dimensions is provided by Flickr Data: Number of Flickr French photographers/total Flickr photographers penetration rate (Vs accommodation) (Number of TripAdvisor accommodation reviewed/ Number of accommodation by district, according to INSEE) penetration rate (Vs Population) (Number of reviewed/ district population, according to Beside that in other cases indicator can be calculated to analyze using CFA (Confirmatory Factor Analysis) that use to examine the convergent and discriminant validity of the six key variables (perceived security, website image, perceived value, trust, satisfaction and e-loyalty). Based on factor analysis results, the items with the highest and lowest loadings for each construct were combined first, followed by those with the next highest and lowest loadings, until all the items for each construct were assigned to one of the indicators. Scores for each indicator were then computed as the mean of the scores on the items that constituted each indicator (Cui et al., 2015). Using indicator can analyze precisely the big data in the tourism industry. Smart Tourism As mention earlier Smart Tourism is to describe the increasing reliance of tourism destinations, their Page 66

4 industries and their tourists on emerging forms of ICT that allow for massive amounts of data to be transformed into value propositions. SMART Tourism is reliant on four core information and communication technologies: IoT, mobile communication, cloud computing, and artificial intelligent technology, which are all pre-existing in the SMART city (SISCA, 2016). Smart tourism also clearly rests on the ability to not only collect enormous amounts of data but to intelligently store, process, combine, analyze and use big data to inform business innovation, operations and services. Numerous technologies support big data creation and in the context of smart tourism, they are often the ones put into the limelight. However, a lot of innovation is also happening in the other big data areas with the ultimate goal of deriving intelligence from massive amounts of data, which is what is at the core of smart tourism initiatives (Gretzel et al., 2015). Technology developments have been acting as a catalyst for the development of smartness. The presence of an advanced technological infrastructure utilized for the management of information and the connection of all social and economic actors within the urban area. The following sections present briefly exploration of the identified components of smartness from the smart city case studies on Barcelona, Amsterdam and Helsinki. a) Innovation When exploring the cases it becomes obvious that smartness is driven by innovation and innovation drives smartness. This implicates for the smart tourism destination that technologies such as sensors, mobile applications and information systems identified from the analysed smart cities are implemented for collecting, processing and transferring large amounts of data. b) Human Capital Human capital is a core component is essential in smart places. To support the development of human capital the cases support and enable different educational systems. Barcelona facilitated the integration of the Smart City Campus within the 22@ Innovation District. Enhancing human capital propels collective intelligence and the cross-linking of knowledge ultimately creating a smart (in the sense of intelligent) city or tourism destination. c) Social Capital The rich interactions identified in the analysed smart cities hold the ability to create value for all and enhance the competitiveness of the smart tourism destination. d) Leadership On the contrary, Amsterdam and Helsinki take on a bottom-up management style, where they both set up platforms based on partnerships between public, private, academic and citizen groups. People living and working in the area commence different smart city activities and initiatives. To enhance the development of collective intelligence through the integration of operant resources in the ecosystem, institutional logics or leadership is required. Within the context of smart tourism destinations, leadership should ensure the development of an innovation-fostering environment where all stakeholders have access to big data and agility in order to develop their competitiveness. Figure 7 illustrates the conceptual framework for the development of smart tourism destinations (Boes, Buhalis, Inversini, Morrison, & Gretzel, 2016). Figure 7 Smart Tourism Destination Framework Indirectly, the development of SMART Cities facilitates seamless access to value added services for tourists of a city, like access to real-time information on public transportation. Technologies connect the physical, information, social, and commercial infrastructure of tourism, and supply SMART Tourism value to multiple stakeholders of a destination (SISCA, 2016). 3. LIMITATION Big data keep increasing day by day. Classical search tools suffer the limitation that results proposed to the user could be loosely correlated each other (Cassavia, Dicosta, Masciari, & Saccà, 2015). Other than that, exploring customers' mental models (Shafiee & Ghatari, 2016) could be subjective. To understand the human behaviours could hard to collect the precise tourist need. Page 67

5 Besides that limited access to post-visit feedback from tourists to support their data driven policy making], such as funding critical infrastructure like airports, roads and hotels (Chareyron et al., 2014). 4. CONCLUSION Even though big data is a very massive volume and high velocity, it still have a way to retrieve it and also can be used in tourism agency to optimize their customer needed. By using User Generated Content (UGC), the tourism agency can get the required data from the various resource (i.e. Facebook, Instagram, Twitter or Panoramio) and manipulate that data to meet with their requirement. For Indicator method tourism agency can acquire more precise big data based on their constraint. Hence they can use this information to make an analysis for future improvement. Last but not lease, Smart Tourism that lead future development to be more innovative and create value to enhance the competitive of the smart tourism destination. Thus, that are the methodology that been discuss can be implement in tourism agency. However, to fully meet tourist requirement still need to be analyze. For future work, I will detail the process in Smart Tourism. 5. CITATION AND REFERENCE Boes, K., Buhalis, D., Inversini, A., Morrison, A., & Gretzel, U. (2016). Smart tourism destinations: ecosystems for tourism destination competitiveness. International Journal of Tourism Cities, 2(2), Cassavia, N., Dicosta, P., Masciari, E., & Saccà, D. (2015). Improving tourist experience by Big Data tools. Proceedings of the 2015 International Conference on High Performance Computing and Simulation, HPCS 2015, Li, M., Bao, Z., Song, L., & Duh, H. (2016). Social-aware visualized exploration of tourist behaviours International Conference on Big Data and Smart Computing, BigComp 2016, Shafiee, S., & Ghatari, A. R. (2016). Big Data in Tourism. 10th International Conference on E-Commerce with Focus on E-Tourism, SISCA. (2016). Smart Tourism. The Scottish Informatics & Computer Science Alliance, (May). Retrieved from World Tranvel and Tourism Council. (2012). Big data Insights for Travel & Tourism. Retrieved from ata&oldid= AUTHOR PROFILES: Noreen Mhd Hashim received the bachelor degree in information technology majoring in software engineering in 204. She is a research student of Universiti Teknologi Malaysia Currently, she is a student in Information Technology Management in Universiti Teknologi Malaysia. Her interests are in big data and shared services analysis. Chareyron, G., Branchet, B., & Jacquot, S. (2015). A new area tourist ranking method. Proceedings IEEE International Conference on Big Data, IEEE Big Data 2015, Chareyron, G., Da-Rugna, J., & Raimbault, T. (2014). Big data: A new challenge for tourism. Proceedings IEEE International Conference on Big Data, IEEE Big Data 2014, (figure 1), Cui, F., Lin, D., & Huang, Y. (2015). The impact of perceived security on consumer E-loyalty: A study of online tourism purchasing. Proceedings IEEE 1st International Conference on Big Data Computing Service and Applications, BigDataService 2015, Gretzel, U., Sigala, M., Xiang, Z., & Koo, C. (2015). Smart tourism: foundations and developments. Electronic Markets, 25(3), Page 68