TO DEVELOP IMPROVE METHODS FOR BUSINESS PROCESS MODELING USING DATA MINING
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Abstract
In today’s fast-evolving business landscape, managing business processes effectively has become increasingly important due to frequent changes in customer demands and the growing complexity of operations. Traditional approaches like workflow mining and process retrieval, while useful, often involve extensive manual effort. One prominent technique used in data mining is clustering, which divides datasets into meaningful groups by iterating through data and refining clusters until stable groupings are formed. Current search engines, however, struggle to provide personalized, comprehensive answers to tourists or visitors searching for specific information, such as transportation, tourist attractions, shopping options, accommodations, and restaurants within a city. To address this challenge, this research proposesaninnovative system thatintegratesdataminingtechniquestodelivertailored, efficientsolutions. Developedin a Hadoop environment, the system utilizes K-Means Clustering and Map Reduce to process large datasets and provide quick, personalized recommendations for travelers. The paper outlines the proposed architecture and demonstrates how this system can revolutionize the way tourism and transportation information is delivered, enhancing user experience by offering accurate, context-sensitive information.
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References
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[40]. M. Lee and J. Kim, “Big Data Analytics for Predicting Tourist Behaviors,” *Asia Pacific Journal of Tourism Research*, vol. 28, no. 5, pp. 300–320, 2023.
[41]. F. Wang and L. Zhao, “Optimizing K-Means Clustering with MapReduce for Big Data,” *International Journal of Data Science and Analytics*,vol.10,no.3,pp. 150–170, 2023.
[42]. T. Zhang and W. Xu, “Scalable K-Means in Hadoop Ecosystem: Challenges and Solutions,” *IEEE Transactions on Big Data*, vol.9,no.4,pp.320–340,2022.
[43]. R.Patel and S.Mehta, “Enhanced Data Partitioning for K-Means Clustering in Hadoop,” *Journal of Computational Intelligence*,vol.18,no.2,pp.220–235,2022.
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[2]. Research on Intelligent Tourism Information System Based on Data Mining Algorithm‖ Jia Du. 23 September 2021.Research article.
[3]. Big data intelligent tourism management platform design based on abnormal behavior identification Jingyi Rong, Huijuan Hao , Wenyue Xu Volume 21, March 2024, 200312
[4]. Intelligent Tourism Recommendation Algorithmbased on Text Mining and MP Nerve Cell Model of Multivariate TransportationModesXIAOZHOU1,2,MINGZHANSU3, GUANGHUIFENG4,5,ANDXINGHANZHOU December24,2020, IEEE
[5]. Online Travel Agent (OTA) For Tourism System Using Big Data and Cloud‖ Ahmad Nurul Fajar, Aldian Nurcahyo, March 2020.
[6]. A. K. Tripathy, P. K. Tripathy, N. K. Ray, and S. P. Mohanty, “iTour: the future of smart tourism: an IoT framework for the independentmobility of touristsin smart cities,” IEEE Consumer Electronics Magazine, vol. 7, no. 3, pp. 32–37, 2018.
[7]. E. Sigalat- Signes, R. Calvo- Palomares, B. Roig-Merino, and I. García-Adán, “Transition towards a tourist innovation model: the smart tourism destination,” Journal of Innovation& Knowledge, vol. 5, no. 2, pp. 96–104, 2020.
[8]. H. Lee, J. Lee, N. Chung, and C. Koo, “Tourists’ happiness: are there smart tourism technology effects?”AsiaPacificJournalofTourismResearch,vol.23,no.5, pp.486–501,2018.
[9]. C. Koo, L. Mendes-Filho, and D. Buhalis, “Guest editorial,” Tourism Review, vol. 74, no. 1, pp. 1–4, 2019.
[10]. T. Zhang, C. Cheung, and R. Law, “Functionality evaluation for destination marketing websites in smart tourism cities,” Journal of China Tourism Research, vol. 14, no. 3, pp. 263–278, 2018.
[11]. M. A. C. Ruíz, S. T. Bohorquez, and J. I. R. Molano, “Colombiantourism:proposalapptofostersmarttourism inthecountry,”AdvancedScienceLetters,vol.23,no.11, pp.10533–10537,2017.
[12]. W. Wang, N. Kumar, J. Chen et al., “Realizing the potentialoftheInternet of Things for smart tourism with 5G and AI,” IEEE Network, vol. 34, no. 6, pp. 295–301, 2020.
[13]. I. Guerra, F. Borges, J. Padrão, J. Tavares, and M. H. Padrão, “Smart cities, smart tourism? The case of the city of Porto,” Revista Galega de Economia, vol. 26, no. 2, pp. 129–142, 2017.
[14]. Y.Topsakal,M.Bahar,andN.Yüzbaşioğlu,“Review of smart tourism literature by bibliometric and visualization analysis,” Journal of Tourism Intelligence and Smartness, vol. 3, no. 1, pp. 1–15, 2020.
[15]. S. Joshi, “Social network analysis in smart tourism- driven service distribution channels: evidence from tourismsupplychain of Uttarakhand, India, ”International Journal of Digital Culture and Electronic Tourism, vol. 2, no. 4, pp. 255–272, 2018.
[16]. F.Femenia-Serra,B.Neuhofer,andJ.A.Ivars-Baidal, “Towards a conceptualisation of smart tourists and their role within the smart destination scenario,” Service Industries Journal, vol. 39, no. 2, pp. 109–133, 2019.
[17]. C. Koo, F. Ricci, C. Cobanoglu, and F. Okumus, “Specialissueonsmart, connectedhospitality and tourism, ”Information Systems Frontiers,vol.19,no.4,pp. 699–703, 2017.
[18]. H.Abdel Rady and A.Khalf, “Towardssmarttourism destination: an empirical study on Sharm el Sheikh city, Egypt,” International Journal of Heritage, Tourism and Hospitality, vol. 13, no. 1, pp. 78–95, 2019.
[19]. T.Pencarelli, “Thedigitalrevolutioninthetravel and tourism industry,” Information Technology& Tourism, vol. 22, no. 3, pp. 455–476, 2020.
[20]. C. J. P. Abad and J. F. Álvarez, “Landscape as digital content and as marttourismresourcein the miningarea of Cartagena La Unión (Spain),” Land, vol. 9, no. 4, pp. 1– 22, 2020.
[21]. P.M.da Costa Liberato, E.Alén-González, and D.F.V. de Azevedo Libera to, “Digital technology in a smart tourist destination: the case of Porto,” Journal of Urban Technology, vol. 25, no. 1, pp. 75–97, 2018.
[22]. J.-J.Hew,G.W.-H.Tan, B.Lin, and K.-B.Ooi, “Generating travel-related contents through mobile social tourism: does privacy paradox persist?” Telematics and Informatics, vol. 34, no. 7, pp. 914–935, 2017.
[23]. Z. Ghaderi, P. Hatamifar, and J. C. Henderson, “Destination selection by smart tourists: the case of Isfahan, Iran,” Asia Pacific Journal of Tourism Research, vol. 23, no. 4, pp. 385–394, 2018.
[24]. T. T. Nguyen, D. Camacho, and J. E. Jung, “Identifying and ranking cultural heritage resources on geotagged social media for smart cultural tourism services, ”Personal and Ubiquitous Computing, vol.21,no. 2, pp. 267–279, 2017.
[25]. P. Del Vecchio and G. Passiante, “Is tourism a driverfor smart specialization? Evidence from Apulia, an Italian region with a tourism vocation,” Journal of Destination Marketing&Management,vol.6,no.3,pp.163–165,
2017.
[26]. M. Liu and Y. Zhang, “Clustering Techniques for Tourism Applications: Analyzing Visitor Profiles,” Tourism Data Analytics Journal, vol. 8, no. 2, pp. 102–113, 2023.
[27]. J.Choi and H.Kim, “ABig Data- Driven Approachto Smart Transportation Systems,” International Journal of Smart Systems, vol. 9, no. 1, pp. 45–60, 2023.
[28]. S. Karthikeyan, “Advancing Tourism Forecasting Through Data Mining: A Case Study,” Tourism Science Advances, vol. 6, no. 4, pp. 214–228, 2022.
[29]. R. Dasand S. Bose, “Personalized Recommendations in Tourism Using Fuzzy Clustering,” Journal of Tourism Intelligence, vol. 5, no. 3, pp. 178–191, 2022.
[30]. T. Verma and R. Singh, “A Comparative Study of Partitioning Algorithms for Destination Marketing,” E- Tourism Journal, vol. 7, no. 2, pp. 89–101, 2021.
[31]. P. Roy, “Analyzing Tourist Behavior Patterns Using Soft Computing Techniques, ”International Journal of Data Mining and Tourism Insights, vol. 10, no. 2, pp. 54–67, 2023.
[32]. N. Alok and A. Chakra borty, “Emerging Trends in Smart Tourism Infrastructure with IoT,” Future Tourism Review, vol. 12, no. 1, pp. 30–45, 2024.
[33]. K. Xu and Y. Huang, “Designing Data-Driven Smart Transportation Systems for Cities, ”Smart City Innovations Journal, vol. 11, no. 3, pp. 58–71, 2022.
[34]. A. Patel and D. Sharma, “Integration of Artificial Intelligence in Tourism Decision-Making,” Journal of Artificial Intelligence and Tourism, vol. 8, no. 1, pp. 20– 35, 2023.
[35]. V. Gupta and P. Rao, “Impact of Big Data Analytics on Tourist Destination Planning,” Smart Travel Analytics Journal, vol. 9, no. 4, pp. 122–137, 2021.
[36]. A. Sharma and P. Gupta, “Leveraging AI and Big Data for Real-Time Smart Tourism Insights,” *Tourism Management Perspectives*, vol. 48, pp. 117–128, 2023.
[37]. W. Chen et al., “A Comparative Study on Big Data- Driven Destination Personalization,” *Information & Management*, vol. 60, no. 4, pp. 101–116, 2023.
[38]. E. Moreno et al., “Smart Tourism Tools for Real-Time Visitor Analytics,” *Sustainable Tourism Development Journal*, vol. 12, no. 6, pp. 240–265, 2023.
[39]. A. Kumar and P. Singh, “Digital Platforms in Tourism: Enhancing Mobility and Experience,”*International Journal of Digital Economy and Tourism*, vol. 11, no. 2, pp. 190–212, 2022.
[40]. M. Lee and J. Kim, “Big Data Analytics for Predicting Tourist Behaviors,” *Asia Pacific Journal of Tourism Research*, vol. 28, no. 5, pp. 300–320, 2023.
[41]. F. Wang and L. Zhao, “Optimizing K-Means Clustering with MapReduce for Big Data,” *International Journal of Data Science and Analytics*,vol.10,no.3,pp. 150–170, 2023.
[42]. T. Zhang and W. Xu, “Scalable K-Means in Hadoop Ecosystem: Challenges and Solutions,” *IEEE Transactions on Big Data*, vol.9,no.4,pp.320–340,2022.
[43]. R.Patel and S.Mehta, “Enhanced Data Partitioning for K-Means Clustering in Hadoop,” *Journal of Computational Intelligence*,vol.18,no.2,pp.220–235,2022.
[44]. J. Li and Y. Chen, “Efficient Map Reduce Algorithms for K-Means Clustering,” *Cluster Computing Journal*,vol. 24, no. 3, pp. 300–325, 2021.
[45]. T. Nguyen and L. Pham, “Adaptive K-Means Clustering for Evolving Datasets on Hadoop,” *Journal of Big Data Technologies*, vol. 5, no. 1, pp. 40–65, 2023.