Human-Computer Cloud and Its Applications in E-Tourism

The chapter addresses two problems that typically arise during the creation of decision support systems that include humans in the information processing workflow, namely, resource management and complexity of decision support in dynamic environments, where it is impossible (or impractical) to implement all possible information processing workflows that can be useful for a decision-maker. The chapter proposes the concept of human-computer cloud, providing typical cloud features (elasticity, on demand resource provisioning) to the applications that require human input (so-called human-based applications) and, on top of resource management functionality, a facility for building information processing workflows for ad hoc tasks in an automated way. The chapter discusses main concepts lying behind the proposed cloud environment, as well as its architecture and some implementation details. It is also shown how the proposed human-computer cloud environment solves information and decision support demands in the dynamic and actively developing area of e-tourism.

[1]  Oksana B. Petrina,et al.  A Semantic Approach to Designing Information Services for Smart Museums , 2016, Int. J. Embed. Real Time Commun. Syst..

[2]  Amit P. Sheth,et al.  Semantic Modeling for Cloud Computing, Part 1 , 2010, IEEE Internet Computing.

[3]  Fausto Giunchiglia,et al.  SmartSociety -- A Platform for Collaborative People-Machine Computation , 2015, 2015 IEEE 8th International Conference on Service-Oriented Computing and Applications (SOCA).

[4]  Seokcheon Lee,et al.  Resource Welfare Based Task Allocation for UAV Team with Resource Constraints , 2015, J. Intell. Robotic Syst..

[5]  B. Pröll,et al.  Covering the semantic space of tourism: an approach based on modularized ontologies , 2009, CIAO '09.

[6]  Thomas R. Gruber,et al.  A translation approach to portable ontology specifications , 1993, Knowl. Acquis..

[7]  Praveen Paritosh,et al.  The anatomy of a large-scale human computation engine , 2010, HCOMP '10.

[8]  Chiara Franzoni,et al.  Crowd Science: The Organization of Scientific Research in Open Collaborative Projects , 2014 .

[9]  Giancarlo Guizzardi,et al.  Ontological foundations for structural conceptual models , 2005 .

[10]  Wolfgang Emmerich,et al.  SLAng: a language for defining service level agreements , 2003, The Ninth IEEE Workshop on Future Trends of Distributed Computing Systems, 2003. FTDCS 2003. Proceedings..

[11]  Elvis Hozdic Socio-Cyber-Physical Systems Alternative for Traditional Manufacturing Structures , 2019, New Technologies, Development and Application II.

[12]  Amit P. Sheth,et al.  Semantic Modeling for Cloud Computing, Part 2 , 2010, IEEE Internet Computing.

[13]  Alexis Battle,et al.  The jabberwocky programming environment for structured social computing , 2011, UIST.

[14]  Chien-Chih Yu,et al.  Personalized and Community Decision Support in eTourism Intermediaries , 2005, DEXA.

[15]  Hannes Werthner,et al.  Harmonise: A Step Toward an Interoperable E-Tourism Marketplace , 2005, Int. J. Electron. Commer..

[16]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

[17]  Symeon Papavassiliou,et al.  Mobile crowdsensing as a service: A platform for applications on top of sensing Clouds , 2016, Future Gener. Comput. Syst..

[18]  Alexander V. Smirnov,et al.  Human-computer cloud for decision support in tourism: Approach and architecture , 2016, 2016 19th Conference of Open Innovations Association (FRUCT).

[19]  Yi Peng,et al.  The analytic hierarchy process: task scheduling and resource allocation in cloud computing environment , 2011, The Journal of Supercomputing.

[20]  Javier Cámara,et al.  Socio-Cyber-Physical Systems: Models, Opportunities, Open Challenges , 2019, 2019 IEEE/ACM 5th International Workshop on Software Engineering for Smart Cyber-Physical Systems (SEsCPS).

[21]  Tarmiji Masron,et al.  THE CONCEPTUAL DESIGN AND APPLICATION OF WEB-BASED TOURISM DECISION SUPPORT SYSTEMS , 2016 .

[22]  Ramesh Govindan,et al.  Medusa: a programming framework for crowd-sensing applications , 2012, MobiSys '12.

[23]  Andreas Krause,et al.  Community sense and response systems: your phone as quake detector , 2014, CACM.

[24]  Rennie Naidoo,et al.  Unravelling Design Controversies in a Transnational Healthcare Information System: An Actor-Network Analysis , 2014, InnovaInt. J. Actor Netw. Theory Technol. Innov..

[25]  Alexander V. Smirnov,et al.  Context-based infomobility system for cultural heritage recommendation: Tourist Assistant—TAIS , 2017, Personal and Ubiquitous Computing.

[26]  Noel Healy World Tourism Organization , 2011, Permanent Missions to the United Nations No.301.

[27]  Eng Wah Lee,et al.  Business-OWL (BOWL)—A Hierarchical Task Network Ontology for Dynamic Business Process Decomposition and Formulation , 2012, IEEE Transactions on Services Computing.

[28]  Lydia B. Chilton,et al.  Exploring iterative and parallel human computation processes , 2010, HCOMP '10.

[29]  Oksana B. Petrina,et al.  Towards an understanding of smart service: The case study for cultural heritage e-Tourism , 2016, 2016 18th Conference of Open Innovations Association and Seminar on Information Security and Protection of Information Technology (FRUCT-ISPIT).

[30]  Jintao Li,et al.  Data fusion in cyber-physical-social systems: State-of-the-art and perspectives , 2019, Inf. Fusion.

[31]  Antonio Díaz-Andrade From Intermediary to Mediator and Vice Versa: On Agency and Intentionality of a Mundane Sociotechnical System , 2010, InnovaInt. J. Actor Netw. Theory Technol. Innov..

[32]  Minjie Zhang,et al.  A belief propagation-based method for task allocation in open and dynamic cloud environments , 2017, Knowl. Based Syst..

[33]  Lior Shamir,et al.  Leveraging Pattern Recognition Consistency Estimation for Crowdsourcing Data Analysis , 2016, IEEE Transactions on Human-Machine Systems.

[34]  Schahram Dustdar,et al.  The Social Compute Unit , 2011, IEEE Internet Computing.

[35]  Heiko Ludwig,et al.  Web Service Level Agreement (WSLA) Language Specification , 2003 .

[36]  Rodolfo Baggio,et al.  Decision Support Systems in a Tourism Destination: Literature Survey and Model Building , 2005 .

[37]  Schahram Dustdar,et al.  Collective Problem Solving using Social Compute Units , 2013, Int. J. Cooperative Inf. Syst..

[38]  Andrew Ponomarev,et al.  Verification-enabling interaction model for services in smart space: a TAIS case , 2015, 2015 17th Conference of Open Innovations Association (FRUCT).

[39]  Takuro Yonezawa,et al.  The Advantages of IoT and Cloud Applied to Smart Cities , 2015, 2015 3rd International Conference on Future Internet of Things and Cloud.

[40]  Ulrike Gretzel,et al.  Intelligent systems in tourism: a social science perspective , 2011 .

[41]  Alexander V. Smirnov,et al.  Decision support in tourism based on human-computer cloud , 2016, iiWAS.

[42]  Nikolay Teslya,et al.  Smart tourism destination support scenario based on human-computer cloud , 2016, 2016 19th Conference of Open Innovations Association (FRUCT).

[43]  U. Gretzel,et al.  Smart tourism challenges , 2017 .