Veri Madenciliği ve Turizmde Veri Madenciliği Çalışmaları

Tuğba ŞEN KÜPELİ, (Ankara Hacı Bayram Veli Üniversitesi, Turizm Fakültesi, Turizm İşletmeciliği Bölümü, Ankara, Türkiye)
Kurban ÜNLÜÖNEN (Ankara Hacı Bayram Veli Üniversitesi, Turizm Fakültesi, Turizm İşletmeciliği Bölümü, Ankara, Türkiye)
Journal of Tourism and Gastronomy Studies
3 3
Yıl: 2021 Cilt: 9 Sayı: 1 Sayfa Aralığı: 275 - 298 Metin Dili: Türkçe DOI: 10.21325/jotags.2021.789 İndeks Tarihi: 30-09-2021

Veri Madenciliği ve Turizmde Veri Madenciliği Çalışmaları

Öz:
Modern teknoloji, veri üretme, depolama ve analizi konularındaki anlayışı internetin de etkisiyle hızla dönüştürmektedir. Farklı mecralarda oldukça büyük miktardaki veriler olağanüstü bir hızda ve çeşitlilikte üretilmekte, aynı hızla veri depolama ve analiz tekniklerine olanak sağlayan teknolojik çözümler geliştirilmektedir. Bu çalışma kapsamında ise bilgi ve iletişim teknolojilerinin gelişimine bağlı olarak ortaya çıkan ve kullanımı hızla tüm alanlara yayılan veri madenciliği ve turizm alanındaki gelişimi incelenmektedir. Bu amaçla, veri madenciliği ve birbirleriyle ilişkili karmaşık yapıdaki diğer kavramlar açıklanıp, veri madenciliği teknikleri kullanılarak yapılan 1999 ile 2020 yılları arasındaki çalışmalar incelenmiştir. Buna göre, veri madenciliği konusundaki çalışmaların, turizm pazarlaması, imaj yönetimi veya turizm talebi gibi farklı turizm dinamiklerine yönelik çözümler getirmesine rağmen, yeterli düzeyde olmadığı ve kavramın alandaki gelişiminin eksik kaldığı sonuçlarına ulaşılmıştır. Çalışmada, veri madenciliği konusuyla ilgili genel bir çerçeve çizilerek turizm literatüründeki mevcut durumun aktarılması bakımından bu alanda önemli bir ihtiyaç giderilmeye çalışılmaktadır.
Anahtar Kelime:

Data Mining and Data Mining Studies in Tourism

Öz:
With the power of the internet, digital technology is increasingly changing the perceptionof data creation, storage, and analysis. A large volume of data is generated at an incrediblespeed and variety in several different media, and at the same speed, technological solutionsare created that enable data storage and advanced analysis techniques. The concept of datamining, which emerge due to the advancement of information and communicationtechnologies and its development in the tourism field is examined. Thus, data mining andother related concepts are explained, and studies conducted using data mining techniquesbetween 1999 and 2020 examined. Accordingly, while solutions were proposed for varioustourism dynamics, including tourism marketing, demanding or image management in datamining studies, it was concluded that they were not at a high extent and that the progressof the conceptualization in the field remained insufficient. This research, therefore, is oneof the leading studies that attempt to meet the needs in terms of drawing a generalframework about the concept of data mining and including the current developments intourism literature.
Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • Acumen. (2020). Deep learning market global industry analysis, market size, opportunities and forecast, 2019 - 2026. Acumen Research and Consulting. Erişim Adresi: https://www.acumenresearchandconsulting.com/deeplearning- market. Erişim Tarihi: 05.01.2021.
  • Akıncı, A. N. (2019). Büyük veri uygulamalarında kișisel veri mahremiyeti (Uzmanlık Tezi). TC Cumhurbaşkanlığı Strateji ve Bütçe Başkanlığı.
  • Alaei, A. R., Becken, S., & Stantic, B. (2019). Sentiment analysis in tourism: Capitalizing on big data. Journal of Travel Research, 58(2), 175-191.
  • Altunkaynak, B. (2019). Veri madenciliği yöntemleri ve r uygulamaları kavramlar – modeller–algoritmalar (İkinci Baskı ed.). Ankara: Seçkin Yayınevi.
  • Bach, M. P., Schatten, M., & Marušić, Z. (2013). Data mining applications in tourism: A keyword analysis. Paper presented at the Central European Conference on Information and Intelligent Systems. 24, 2013.
  • Bengio, Y., Goodfellow, I., & Courville, A. (2017). Deep learning. (1), USA: MIT press Massachusetts.
  • Blossom, H. D., & Alex, N. (2018). Ehealth. In A. E. Grant & J. H. Meadows (Eds.), Communication technology update and fundamentals (16 ed.). New York: Routledge.
  • Bose, I. (2009). Data mining in tourism. In Encyclopedia of information science and technology, second edition (pp. 936-940), IGI Global.
  • Bramer, M. (2007). Principles of data mining. (180),Springer.
  • Buhalis, D. (1998). Strategic use of information technologies in the tourism industry. Tourism management, 19(5), 409-421. doi:Doi 10.1016/S0261-5177(98)00038-7
  • Buhalis, D. (2019). Technology in tourism-from information communication technologies to etourism and smart tourism towards ambient intelligence tourism: A perspective article. Tourism Review.
  • Buhalis, D., & O'Connor, P. (2005). Information communication technology revolutionizing tourism. Tourism recreation research, 30(3), 7-16.
  • Carrasco, R. A., Araque, F., Salguero, A., & Vila, M. A. (2008). Applying fuzzy data mining to tourism area. In Handbook of research on fuzzy information processing in databases (pp. 563-584): IGI Global.
  • Cavanillas, J. M., Curry, E., & Wahlster, W. (2016). The big data value opportunity. In J. M. Cavanillas, E. Curry, & W. Wahlster (Eds.), New horizons for a data-driven economy (pp. 3-11): Springer, Cham.
  • Dolgun, M. Ö., Özdemir, T. G., & Oğuz, D. (2009). Veri madenciliğinde yapısal olmayan verinin analizi: Metin ve web madenciliği. İstatistikçiler Dergisi: İstatistik ve Aktüerya, 2(2), 48-58.
  • Emel, G. G., & Taşkın, Ç. (2006). Identifying segments of a domestic tourism market by means of data mining. In H.-D. Haasis, H. Kopfer, & J. Schönberger (Eds.), Operations research proceedings 2005 (pp. 653-658), Springer.
  • Ersöz, F. (2019). Veri madenciliği teknikleri ve uygulamaları (3. Baskı ed.). Ankara: Seçkin Yayınevi.
  • Firican, G. (2017). The 10 vs of big data. Erişim Adresi: https://tdwi.org/articles/2017/02/08/10-vs-of-big-data.aspx. Erişim Tarihi: 27.12.2020.
  • Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International journal of information management, 35(2), 137-144. doi:10.1016/j.ijinfomgt.2014.10.007
  • GartnerITGlossary (2020). Big data. Erişim Adresi: http://www.gartner.com/it-glossary/big-data/. Erişim Tarihi: 27.12.2020.
  • Ghasemaghaei, M., & Calic, G. (2020). Assessing the impact of big data on firm innovation performance: Big data is not always better data. Journal of business research, 108, 147-162. doi:10.1016/j.jbusres.2019.09.062
  • Ghasemaghaei, M., Ebrahimi, S., & Hassanein, K. (2018). Data analytics competency for improving firm decision making performance. Journal of Strategic Information Systems, 27(1), 101-113. doi:10.1016/j.jsis.2017.10.001
  • Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning, (1), MIT press Cambridge.
  • Google Akademik (2020). Allintitle: Tourism "data mining". Erişim Adresi: https://scholar.google.com.tr/scholar?q=allintitle:+tourism+%22data+mining%22&hl=tr&as_sdt=0,5. Erişim Tarihi: 06.01.2021
  • GoogleCloud (2020). Büyük veri nedir. Erişim Adresi: https://cloud.google.com/what-is-big-data?hl=tr. Erişim Tarihi: 14.12.2020.
  • Grant, A. E., & Meadows, J. H. (2018). Communication technology update and fundamentals (16 ed.). New York: Routledge.
  • Gu, Z., Zhang, Y., Chen, Y., & Chang, X. (2016). Analysis of attraction features of tourism destinations in a megacity based on check-in data mining—a case study of shenzhen, china. ISPRS International Journal of Geo- Information, 5(11), 210.
  • Guoxia, Z., & Jianqing, T. (2009). The application of data mining in tourism information. Paper presented at the 2009 International Conference on Environmental Science and Information Application Technology.
  • Ha, S. H., & Park, S. C. (1998). Application of data mining tools to hotel data mart on the intranet for database marketing. Expert Systems with Applications, 15(1), 1-31. doi:Doi 10.1016/S0957-4174(98)00008-6
  • Halkiopoulos, C., & Koumparelis, A. (2020). Historical advertisements of hotels, tour agencies and involved services of tourism industry in greece: A data mining analysis via image processing. In V. Katsoni & T. Spyriadis (Eds.), Cultural and tourism innovation in the digital era (pp. 209-218): Springer.
  • Han, J., Pei, J., & Kamber, M. (2011). Data mining: Concepts and techniques: Elsevier.
  • Hand, D. J., Mannila, H., & Smyth, P. (2001). Principles of data mining (adaptive computation and machine learning), MIT Press.
  • Holst, A. (2020). Amount of information globally 2010-2024. Statista. Erişim Adresi: https://www.statista.com/statistics/871513/worldwide-datacreated/#:~: text=The%20total%20amount%20of%20data,ever%2Dgrowing%20global%20data%20sphere. Erişim Tarihi: 27.12.2020.
  • IBM. (2020). What is big data analytics? IBM. Erişim Adresi: https://www.ibm.com/analytics/hadoop/big-dataanalytics? mhsrc=ibmsearch_a&mhq=Zettabyte. Erişim Tarihi: 27.12.2020.
  • Jeffcock, P. (2018). What's the difference between ai, machine learning, and deep learning? Erişim Adresi: https://blogs.oracle.com/bigdata/difference-ai-machinelearningdeeplearning#:~: text=Machine%20learning%20is%20a%20subset,to%20solve%20more%20complex %20problems. Erişim Tarihi: 13.01.2021.
  • Kavlakoğlu, E. (2020). Ai vs. Machine learning vs. Deep learning vs. Neural networks: What’s the difference? Erişim Adresi: https://www.ibm.com/cloud/blog/ai-vs-machine-learning-vs-deep-learning-vs-neural-networks. Erişim Tarihi: 27.05.2020.
  • Koh, H. C., & Gan, L. L. (2009). A data mining analysis of healthcare tourism in singapore. Advances on Information Processing and Management, 26.
  • Law, R. (1998). Hospitality data mining myths. Hospitality Review, 16(1), 7.
  • Law, R., Buhalis, D., & Cobanoglu, C. (2014). Progress on information and communication technologies in hospitality and tourism. International Journal of Contemporary Hospitality Management, 26(5), 727-750. doi:10.1108/Ijchm-08-2013-0367
  • Law, R., Li, G., Fong, D. K. C., & Han, X. (2019). Tourism demand forecasting: A deep learning approach. Annals of tourism research, 75, 410-423. doi:10.1016/j.annals.2019.01.014
  • Law, R., Mok, H., & Goh, C. (2007). Data mining in tourism demand analysis: A retrospective analysis. Paper presented at the International Conference on Advanced Data Mining and Applications.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. doi:10.1038/nature14539
  • Li, Q., Li, S., Zhang, S., Hu, J., & Hu, J. (2019). A review of text corpus-based tourism big data mining. Applied Sciences, 9(16), 3300. doi:ARTN 330010.3390/app9163300
  • Liu, S. (2020). Artificial intelligence (ai) worldwide - statistics & facts. Erişim Adresi: https://www.statista.com/topics/3104/artificial-intelligence-ai-worldwide/. Erişim Tarihi: 27.12.2020.
  • Mariani, M. (2019). Big data and analytics in tourism and hospitality: A perspective article. Tourism Review, 75(1), 299-303.
  • Martín, C. A., Torres, J. M., Aguilar, R. M., & Diaz, S. (2018). Using deep learning to predict sentiments: Case study in tourism. Complexity, 2018, 1-9.
  • Mayer-Schönberger, V., & Cukier, K. (2013). Big data, a revaluation that will transform how we live, work, and think (B. Erol, Trans.), Houghton Mifflin Harcourt Publishing Company.
  • Mazumdar, S., Seybold, D., Kritikos, K., & Verginadis, Y. (2019). A survey on data storage and placement methodologies for cloud-big data ecosystem. Journal of Big Data, 6(1), 15. doi:ARTN 1510.1186/s40537-019- 0178-3
  • Mirela, D., Tiberiu, S., & Adina, B. (2009). Toward a distributed data mining system for tourism industry. Annals of Faculty of Economics, 4(1), 922-926.
  • Mistilis, N., & Dwyer, L. (2000). Information technology and service standards in mice tourism. Paper presented at the Journal of Convention & Exhibition Management.
  • Mitchell, T. M. (1999). Machine learning and data mining. Communications of the ACM, 42(11), 30-36. doi:Doi 10.1145/319382.319388
  • Morgan, S. (2020). The 2020 data attack surface report arcserve. Erişim Adresi: https://1c7fab3im83f5gqiow2qqs2k-wpengine.netdna-ssl.com/wpcontent/ uploads/2020/12/ArcserveDataReport2020.pdf. Erişim Tarihi: 26.12.2020.
  • Okuhara, K., Yeh, K.-Y., Hsia, H.-C., & Ishii, H. (2008). Data mining of geographical accessibility for tourism in regional area. Paper presented at the 2008 Eighth International Conference on Intelligent Systems Design and Applications.
  • Olmeda, I., & Sheldon, P. J. (2002). Data mining techniques and applications for tourism internet marketing. Journal of Travel & Tourism Marketing, 11(2-3), 1-20.
  • Oracle. (2018). Make ai collaboration your next b2b marketing move. Erişim Adresi: https://blogs.oracle.com/cx/make-your-next-b2b-marketing-move-ai-collaboration. Erişim Tarihi: 12.01.2021.
  • Oracle. (2020). What is big data? Big data defined. Erişim Adresi: https://www.oracle.com/big-data/what-is-bigdata. html. Erişim Tarihi: 03.01.2020.
  • Park, S., Xu, Y., Jiang, L., Chen, Z., & Huang, S. (2020). Spatial structures of tourism destinations: A trajectory data mining approach leveraging mobile big data. Annals of Tourism Research, 84, 102973. doi:ARTN 10297310.1016/j.annals.2020.102973
  • Pertheban, S., Narayana Samy, G., & Shanmugam, B. (2020). A systematic literature review: Information accuracy practices in tourism. Journal of Quality Assurance in Hospitality & Tourism, 21(1), 1-30. doi:10.1080/1528008x.2018.1563016
  • Phillips-Wren, G. (2012). Ai tools in decision making support systems: A review. International Journal on Artificial Intelligence Tools, 21(02), 1240005. doi:Artn 124000510.1142/S0218213012400052
  • Press, G. (2020). 6 predictions about data in 2020 and the coming decade. Forbes. Retrieved from https://www.forbes.com/sites/gilpress/2020/01/06/6-predictions-about-data-in-2020-and-the-comingdecade/? sh=5d7138624fc3
  • Reinsel, D., Gantz, J., & Rydning, J. (2018). The digitization of the world: From edge to core. International Data Corporation. Retrieved from: https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagatedataage- whitepaper.pdf. Erişim Tarihi: 21.12.2020.
  • Salguero, A., Araque, F., Carrasco, R., De Campos, A., & Martínez, L. (2007). Using fuzzy data mining for finding preferences in adventure tourism. Eurofuse Workshop. Erişim Adresi: https://www.researchgate.net/profile/Luis_Martinez19/publication/241881953_Using_Fuzzy_Data_Mining_for _finding_preferences_in_adventure_tourism/links/582446ef08aeb45b58899315/Using-Fuzzy-Data-Mining-forfinding- preferences-in-adventure-tourism.pdf. Erişim Tarihi: 16.01.2021.
  • SAS. (2020). Data mining what it is & why it matters. Erişim Adresi: https://www.sas.com/tr_tr/insights/analytics/data-mining.html#dmusers. Erişim Tarihi: 15.02.2021.
  • SEAGATE. (2020). Rethink data: Put more of your business data to work from edge to cloud. Erişim Adresi: https://www.seagate.com/files/www-content/our-story/rethink-data/files/Rethink_Data_Report_2020.pdf. Erişim Tarihi: 26.12.2020.
  • Shapoval, V., Wang, M. C., Hara, T., & Shioya, H. (2018). Data mining in tourism data analysis: Inbound visitors to japan. Journal of Travel Research, 57(3), 310-323. doi:10.1177/0047287517696960
  • Sheng, F., Zhang, Y., Shi, C., Qiu, M., & Yao, S. (2020). Xi’an tourism destination image analysis via deep learning. Journal of Ambient Intelligence and Humanized Computing, 1-10. doi:10.1007/s12652-020-02344-w
  • Siddiqa, A., Karim, A., & Gani, A. (2017). Big data storage technologies: A survey. Frontiers of Information Technology & Electronic Engineering, 18(8), 1040-1070. doi:10.1631/Fitee.1500441
  • Strohbach, M., Daubert, J., Ravkin, H., & Lischka, M. (2016). Big data storage. In J. M. Cavanillas, E. Curry, & W. Wahlster (Eds.), New horizons for a data-driven economy (pp. 119-141): Springer, Cham.
  • Tichter, G., Grossmann, W., & Werthner, H. (1999). Using data mining in analysing local tourism patterns. In Information and communication technologies in tourism 1999 (pp. 1-10): Springer.
  • Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3-28. doi:10.1257/jep.28.2.3
  • Wilkinson, J. S. (2018). Internet of things (iot). In A. E. Grant & J. H. Meadows (Eds.), Communication technology update and fundamentals (16 ed., pp. 159-168). New York: Routledge.
  • Wolfewicz, A. (2020). Deep learning vs. Machine learning – what’s the difference? Erişim Adresi: https://www.levity.ai/blog/difference-machine-learning-deep-learning. Erişim Tarihi: 01.14.2021.
  • Woodie, A. (2020a). Data boom gets even bigger, thanks to covid-19. Erişim Adresi: https://www.datanami.com/2020/05/07/the-big-cloud-data-boom-gets-even-bigger-thanks-to-covid-19/. Erişim Tarihi: 29.12.2020.
  • Woodie, A. (2020b). Storage in the exabyte era. Erişim Adresi: https://www.datanami.com/2020/02/19/storage-in-the-exabyte-era/. Erişim Tarihi: 29.12.2020.
  • Wu, J., Wang, J., Nicholas, S., Maitland, E., & Fan, Q. (2020). Application of big data technology for covid-19 prevention and control in china: Lessons and recommendations. Journal of Medical Internet Research, 22(10), e21980. doi:10.2196/21980
  • Xu, J. (2014). Managing digital enterprise: Ten essential topics, Springer.
  • Yan, Y., Chen, J., & Wang, Z. (2020). Mining public sentiments and perspectives from geotagged social media data for appraising the post-earthquake recovery of tourism destinations. Applied Geography, 123, 102306. doi:ARTN 10230610.1016/j.apgeog.2020.102306
  • Zhang, J., & Dong, L. (2021). Image monitoring and management of hot tourism destination based on data mining technology in big data environment. Microprocessors and Microsystems, 80, 103515.
  • Zheng, Z., Zhu, J., & Lyu, M. R. (2013). Service-generated big data and big data-as-a-service: An overview. Paper presented at the 2013 IEEE international congress on Big Data.
  • Zhu, Y. (2018). A history of communication technology. In A. E. Grant & J. H. Meadows (Eds.), Communication technology update and fundamentals (16 ed., pp. 9-21). New York: Routledge.
APA ŞEN KÜPELİ T, Ünlüönen K (2021). Veri Madenciliği ve Turizmde Veri Madenciliği Çalışmaları. , 275 - 298. 10.21325/jotags.2021.789
Chicago ŞEN KÜPELİ TUĞBA,Ünlüönen Kurban Veri Madenciliği ve Turizmde Veri Madenciliği Çalışmaları. (2021): 275 - 298. 10.21325/jotags.2021.789
MLA ŞEN KÜPELİ TUĞBA,Ünlüönen Kurban Veri Madenciliği ve Turizmde Veri Madenciliği Çalışmaları. , 2021, ss.275 - 298. 10.21325/jotags.2021.789
AMA ŞEN KÜPELİ T,Ünlüönen K Veri Madenciliği ve Turizmde Veri Madenciliği Çalışmaları. . 2021; 275 - 298. 10.21325/jotags.2021.789
Vancouver ŞEN KÜPELİ T,Ünlüönen K Veri Madenciliği ve Turizmde Veri Madenciliği Çalışmaları. . 2021; 275 - 298. 10.21325/jotags.2021.789
IEEE ŞEN KÜPELİ T,Ünlüönen K "Veri Madenciliği ve Turizmde Veri Madenciliği Çalışmaları." , ss.275 - 298, 2021. 10.21325/jotags.2021.789
ISNAD ŞEN KÜPELİ, TUĞBA - Ünlüönen, Kurban. "Veri Madenciliği ve Turizmde Veri Madenciliği Çalışmaları". (2021), 275-298. https://doi.org/10.21325/jotags.2021.789
APA ŞEN KÜPELİ T, Ünlüönen K (2021). Veri Madenciliği ve Turizmde Veri Madenciliği Çalışmaları. Journal of Tourism and Gastronomy Studies, 9(1), 275 - 298. 10.21325/jotags.2021.789
Chicago ŞEN KÜPELİ TUĞBA,Ünlüönen Kurban Veri Madenciliği ve Turizmde Veri Madenciliği Çalışmaları. Journal of Tourism and Gastronomy Studies 9, no.1 (2021): 275 - 298. 10.21325/jotags.2021.789
MLA ŞEN KÜPELİ TUĞBA,Ünlüönen Kurban Veri Madenciliği ve Turizmde Veri Madenciliği Çalışmaları. Journal of Tourism and Gastronomy Studies, vol.9, no.1, 2021, ss.275 - 298. 10.21325/jotags.2021.789
AMA ŞEN KÜPELİ T,Ünlüönen K Veri Madenciliği ve Turizmde Veri Madenciliği Çalışmaları. Journal of Tourism and Gastronomy Studies. 2021; 9(1): 275 - 298. 10.21325/jotags.2021.789
Vancouver ŞEN KÜPELİ T,Ünlüönen K Veri Madenciliği ve Turizmde Veri Madenciliği Çalışmaları. Journal of Tourism and Gastronomy Studies. 2021; 9(1): 275 - 298. 10.21325/jotags.2021.789
IEEE ŞEN KÜPELİ T,Ünlüönen K "Veri Madenciliği ve Turizmde Veri Madenciliği Çalışmaları." Journal of Tourism and Gastronomy Studies, 9, ss.275 - 298, 2021. 10.21325/jotags.2021.789
ISNAD ŞEN KÜPELİ, TUĞBA - Ünlüönen, Kurban. "Veri Madenciliği ve Turizmde Veri Madenciliği Çalışmaları". Journal of Tourism and Gastronomy Studies 9/1 (2021), 275-298. https://doi.org/10.21325/jotags.2021.789
Sayfa Oluşturulma Tarihi
25-04-2024 4:2

İLETİŞİM

TÜBİTAK ULAKBİM TR Dizin

Yüzüncüyıl, İşçi Blokları Mahallesi

Muhsin Yazıcıoğlu Caddesi No:51/C

06530 Çankaya / ANKARA +90 (312) 298 92 00

trdizin@tubitak.gov.tr

TÜBİTAK ULAKBİM Ulusal Akademik Ağ ve Bilgi Merkezi Cahit Arf Bilgi Merkezi © Tüm Hakları Saklıdır. Gizlilik Politikası Kullanım Şartları