research-article

Decision support in tourism based on human-computer cloud

Authors:

Alexander Smirnov,

Andrew Ponomarev,

Tatiana Levashova,

Nikolay TeslyaAuthors Info & Claims

iiWAS '16: Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services

Pages 125 - 132

https://doi.org/10.1145/3011141.3011174

Published: 28 November 2016 Publication History

Abstract

Tourist mobility, high risk and uncertainty in unfamiliar environments cause the importance of information and decision support for tourists. On the other side, complex nature of tourism economic sector, intertwined with other sectors, demand for decision support methodologies and tools helping destination management organizations to plan the activities for promoting and rational development of tourist destinations. Decision support systems in tourism today leverage a variety of technologies both machine-driven and human-driven. This paper applies a novel concept of human-computer cloud as a conceptual and architectural approach to building decision support systems in tourism (both from the tourist's perspective, and from destination management organization's perspective). The main role of human-computer cloud here is to provide a convenient abstraction for computational resources, not only "ordinary" (electronic/software) ones but also human-based. In the paper, we identify the list of typical decision support tasks in tourism domain, outline possible human-based extensions of traditional kinds of decision support systems, and finally discuss how some popular decision support functions in this domain can be mapped to a multi-tiered conceptual architecture of human-computer cloud services. The proposed approach is illustrated by two usage scenarios - itinerary planning and destination visitors' survey.

References

[1]

Ahmad, S. et al. 2011. The jabberwocky programming environment for structured social computing. Proceedings of the 24th annual ACM symposium on User interface software and technology - UIST '11. (2011), 53--64.

Digital Library

[2]

Alter S.L. Decision Support Systems: Current Practice and Continuing Challenge. Reading, MA: Addison-Wesley, 1980.

[3]

Asafe, Y., Bolaji, A., Enaholo, A. and Olubukola, O. 2013. Web-based expert decision support system for tourism destination management in Nigeria. International Journal of Advanced Research in Artificial Intelligence, vol. 2, no. 4, 2013, 59--63.

[4]

Baggio, R. and Caporarello, L. Decision Support Systems in a Tourism Destination: Literature Survey and Model Building. URL: http://www.iby.it/turismo/papers/baggio-dss-tourism.pdf

[5]

Berka, T. and Plößnig, M. 2004. Designing recommender systems for tourism. In ENTER, Cairo, 2004. URL: http://195.130.87.21:8080/dspace/handle/123456789/583

[6]

Bhat, M.A., Ahmad, B., Shah, R.M. and Bhat, I.R. 2010. Cloud Computing: A Solution to Information Support Systems. International Journal of Computer Applications, vol. 11, no. 5, 2010, 5--9.

[7]

Chaves, M., Freitas, L. and Vieira, R. 2012. Hontology: a multilingual ontology for the accommodation sector in the tourism industry. In Proceedings of KEOD2012 - International Conference on Knowledge Engineering and Ontology Development, 2012, 149--154.

[8]

Distefano, S., Merlino, G. and Puliafito, A. SAaaS: A Framework for Volunteer-Based Sensing Clouds. Parallel and Cloud Computing, vol. 1, no. 2, 2012, 21--33.

[9]

Dustdar, S. and Bhattacharya, K. 2011. The social compute unit. IEEE Internet Computing, 15(3), 2011, 64--69.

Digital Library

[10]

Formisano, C. Pavia, D., Gurgen, L. et al. 2015. The Advantages of IoT and Cloud Applied to Smart Cities. In 3^rd International Conference Future Internet of Things and Cloud, 2015, Rome, 325--332.

Digital Library

[11]

Gavalas, D., Konstantopoulos, C., Mastakas, K. and Pantziou, G. 2014. Mobile recommender systems in tourism. Journal of Network and Computer Applications, vol. 39, 2014, 319--333.

Digital Library

[12]

Gretzel, U. 2011. Intelligent systems in tourism: A social science perspective. Annals of Tourism Research, 38(3), 2011, 757--779.

[13]

Guttentag, D. 2010. Virtual reality: Applications and implications for tourism. Tourism Management, 31(5), 2010, 637--651.

[14]

Iren, D. et al. 2014. Utilization of synergetic human-machine clouds: A big data cleaning case. 1st International Workshop on CrowdSourcing in Software Engineering, CSI-SE 2014. (2014), 15--18.

Digital Library

[15]

Kamar, E. et al. 2012. Combining Human and Machine Intelligence in Large-scale Crowdsourcing. Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (2012).

Digital Library

[16]

Keenan, P.B. 2013. Cloud computing and DSS: the case of spatial DSS. International Journal of Information and Decision Sciences, 5(3), 2013, 283--294.

[17]

Kochhar, S. et al. 2010. The anatomy of a large-scale human computation engine. Proceedings of the ACM SIGKDD Workshop on Human Computation - HCOMP '10. (2010), 10.

Digital Library

[18]

Little, G., Chilton, L.B., Goldman, M. and Miller, R.C. 2010. Exploring iterative and parallel human computation processes. In Proceedings of the ACM SIGKDD Workshop on Human Computation, HCOMP '10, 2010, 68--76.

Digital Library

[19]

Maione, I. 2014. Crowdsourcing Applications for Online Tourism Portals. URL: http://www.crowdsourcing.org/editorial/crowdsourcing-applications-for-online-tourism-portals/31290 {Accessed: 10.06.2016}

[20]

Masron, T., Ismail, N. and Marzuki, A. 2016. The Conceptual Design and Application of Web-Based Tourism Decision Support Systems. Theoretical and Empirical Researches in Urban Management, vol. 11, no. 2, 2016, 64--75.

[21]

Mell, P. and Grance, T. 2011. The NIST Definition of Cloud Computing. Recommendations of the National Institute of Standards and Technology, NIST Special Publication 800--145.

[22]

Merlino, G., Arkoulis, S., Distefano, S., Papagianni, C., Puliafito, A. and Papavassiliou, S. 2016. Mobile Crowdsensing as a Service: A Platform for Applications on Top of Sensing Clouds. Future Generation Computer Systems, Vol. 56, 2016, 623--639.

Digital Library

[23]

Noor, N.M.M., Sabri, I.A.A. and Ismail, F. 2010. Intelligent decision support system for tourism destination choice: a preliminary study. International Symposium on Information Technology, IEEE, 2010, 1357 -- 1361.

[24]

Oyster Hotel Reviews Selects SGI^® for Ability to Rapidly Scale and Achieve Low Cost of Ownership {Electronic resource} // SGI (Silicon Graphics International Corp.): {site}. 2009. Press Release. URL: https://www.sgi.com/company_info/newsroom/press_releases/2009/july/oyster.html

[25]

Phuttharak, J. and Loke, S.W. 2013. Logicrowd: A declarative programming platform for mobile crowdsourcing. Proceedings - 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2013 (2013), 1323--1330.

Digital Library

[26]

Ra, M. et al. 2012. Medusa: A Programming Framework for Crowd-Sensing Applications Categories and Subject Descriptors. Proceedings of the 10th international conference on Mobile systems, applications, and services MobiSys '12 (2012), 337--350.

Digital Library

[27]

Rajkumar, N., Gohin, B. and Vinod, V. 2012. . An innovative approach towards Semantic Web Services rummage around for promoting e-tourism. International Journal of Computer Applications, vol. 52, no. 1, 2012, 37--43.

[28]

Ritchie, J.R. and Crouch, G.I. 2003. The Competitive Destination: Sustainable Tourism Perspective. Oxon, UK: CABI Publishing, 2003.

[29]

Ritchie, R.J.B. and Ritchie, J.R.B. 2002. . A framework for an industry supported destination marketing information system. Tourism Management, 23, 2002, 439--454.

[30]

Sabou, M., Onder, I., Brasoveanu, A.M.P. and Scharl, A. 2015. Towards cross-domain decision making in tourism: a linked data based approach {Electronic resource}. Working Papers Series. Modul Vienna University, 2015. MODUL University Working Paper No. 2. URL: https://www.modul.ac.at/index.php?eID=dumpFile&t=f&f=4003&token=abfcfc75cee382583bfd95872d6f4ee9b8ab7b30

[31]

Sabou, M., Brasoveanu, A.M.P. and Onder, I. Linked data for cross-domain decision-making in tourism. Information and Communication Technologies in Tourism 2015 (Proceedings of the International Conference in Lugano, Switzerland, February 3 -- 6, 2015). Springer, 2015, 197--210.

[32]

Sarasua, C., Simperl, E. and Noy, N.F. 2012. CrowdMap: crowdsourcing ontology alignment with microtasks. In Proceedings of the 11th international conference on The Semantic Web - Volume Part I (ISWC'12), Philippe Cudré-Mauroux, Jeff Heflin, Evren Sirin, Tania Tudorache, and Jérôme Euzenat (Eds.), Vol. Part I. Springer-Verlag, Berlin, Heidelberg, 525--541.

Digital Library

[33]

Scekic, O., Truong, H.-L. and Dustdar, S. 2013. Incentives and rewarding in social computing. Communications of the ACM, 2013, vol. 56, no. 6, 72--82.

Digital Library

[34]

Sengupta, B., Jain, A., Bhattacharya, K., Truong, H.-L. and Dustdar, S. 2013. Collective Problem Solving Using Social Compute Units. International Journal of Cooperative Information Systems, vol. 22, no. 4, 2013.

[35]

Soualah-Alila, F., Coustaty, M., Rempulski, N. and Doucet, A. 2016. DataTourism: designing an architecture to process tourism data. Information and Communication Technologies in Tourism 2016 (Proceedings of the International Conference in Bilbao, Spain, February 2--5, 2016). Springer, 2016, 751--763.

[36]

Wang, W. et al. 2008. An intelligent ontology and Bayesian network based semantic mashup for tourism. In Proceedings of IEEE Congress on Services. Honolulu, Hi: IEEE Press, 2008. Part I. 128--135.

Digital Library

[37]

Wiedmann, K.-P. and Langner, S. 2016. . Open source marketing in tourism: motivational drivers and practical approaches. In Open Tourism; eds. by R. Egger, I. Gula, D. Walcher. Berlin, Heidelber: Springer, 2016, 61--77.

[38]

World Tourism Organization: UNWTO Tourism Highlights 2016 Edition, URL: http://cf.cdn.unwto.org/sites/all/files/pdf/unwto_highlights16_en_hr.pdf

[39]

Yoo, K.-H., Sigala, M. and Gretzel, U. 2016. Exploring TripAdvisor. in Open Tourism; eds. by R. Egger, I. Gula, D. Walcher. Berlin, Heidelber: Springer, 2016, 239--255.

[40]

Yu, C.C. 2004. A Web-Based Consumer-Oriented Intelligent Decision Support System for EServices. In Proceedings of the 6th International Conference on Electronic Commerce, 2004, 429--437.

Digital Library

[41]

Yu, C.C. 2005. Personalized and Community Decision Support in eTourism Intermediaries. LNCS 3288, 900--909.

Digital Library

[42]

Zhang, H. 2012. Computational Environment Design, PhD thesis, Harvard University.

Digital Library

[43]

Zhang, L. and Yang, J. 2016. Smart tourism. In Encyclopedia of Tourism. Springer, 2016, 862--863.

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Wang HYu ZRen ZZhang YGuo B(2022)An Efficient HPU Resource Virtualization Framework for Human-Machine Computing SystemsProceedings of the 13th Asia-Pacific Symposium on Internetware10.1145/3545258.3545264(166-174)Online publication date: 11-Jun-2022
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Index Terms

Decision support in tourism based on human-computer cloud
1. Applied computing
  1. Operations research
    1. Decision analysis
2. Information systems
  1. Information systems applications
    1. Decision support systems

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iiWAS '16: Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services

November 2016

528 pages

ISBN:9781450348072

DOI:10.1145/3011141

General Chair:
Gabriele Anderst-Kotsis
Johannes Kepler University Linz, Austria

Copyright © 2016 ACM.

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Published: 28 November 2016

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iiWAS '16

iiWAS '16: 18th International Conference on Information Integration and Web-based Applications and Services

November 28 - 30, 2016

Singapore, Singapore

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Cited By

Wang HYu ZRen ZZhang YLiu JWang LGuo B(2025)FingHV: Efficient Sharing and Fine-Grained Scheduling of Virtualized HPU ResourcesIEEE Transactions on Cybernetics10.1109/TCYB.2024.351856955:2(600-614)Online publication date: Feb-2025
https://doi.org/10.1109/TCYB.2024.3518569
Wang HYu ZRen ZZhang YGuo B(2022)An Efficient HPU Resource Virtualization Framework for Human-Machine Computing SystemsProceedings of the 13th Asia-Pacific Symposium on Internetware10.1145/3545258.3545264(166-174)Online publication date: 11-Jun-2022
https://dl.acm.org/doi/10.1145/3545258.3545264
Ponomarev AShilov N(2021)Human-Computer Cloud and Its Applications in E-TourismResearch Anthology on Decision Support Systems and Decision Management in Healthcare, Business, and Engineering10.4018/978-1-7998-9023-2.ch055(1143-1176)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-9023-2.ch055
Smirnov APonomarev AShilov NKashevnik ATeslya N(2021)Ontology-Based Human-Computer Cloud for Decision SupportResearch Anthology on Decision Support Systems and Decision Management in Healthcare, Business, and Engineering10.4018/978-1-7998-9023-2.ch019(410-430)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-9023-2.ch019
Ponomarev AShilov N(2020)Human-Computer Cloud and Its Applications in E-TourismTools and Technologies for the Development of Cyber-Physical Systems10.4018/978-1-7998-1974-5.ch008(202-235)Online publication date: 2020
https://doi.org/10.4018/978-1-7998-1974-5.ch008
Smirnov AShchekotov MShilov NPonomarev A(2018)Decision Support Service Based on Dynamic Resource Network Configuration in Human-Computer CloudProceedings of the 23rd Conference of Open Innovations Association FRUCT10.5555/3299905.3299954(362-368)Online publication date: 19-Nov-2018
https://dl.acm.org/doi/10.5555/3299905.3299954
Smirnov APonomarev AShilov NKashevnik ATeslya N(2018)Ontology-Based Human-Computer Cloud for Decision SupportInternational Journal of Embedded and Real-Time Communication Systems10.4018/IJERTCS.20180101019:1(1-19)Online publication date: Jan-2018
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Smirnov AShchekotov MShilov NPonomarev A(2018)Decision Support Service Based on Dynamic Resource Network Configuration in Human-Computer Cloud2018 23rd Conference of Open Innovations Association (FRUCT)10.23919/FRUCT.2018.8588080(362-368)Online publication date: Nov-2018
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