When Human Service Meets Crowdsourcing: Emerging in Human Service Collaboration

With the sweeping progress of service computing technology and crowdsourcing, individuals are offering their capability as human services online. Companies are orchestrating human services for complex problem-solving, resulting in the rapid growth of human service ecosystems nowadays. Considering the unique characteristics of human services, like capability growth and human-involving collaboration, it is essential to understand the patterns of the development and collaboration among human services. Therefore, this paper proposes a three-layer time-aware heterogeneous network model to quantify the evolution in the human service ecosystem. Based on the model, an exploratory empirical study is presented to uncover how human service providers and consumers develop their capability in service provision and orchestration, as well as how human services collaborate with each other over time. Insights from the emerging patterns open a gateway for further research to facilitate human service adoption, including human service composition recommendation, human skill expansion suggestion, and systematic mechanism design.

[1]  Jia Zhang,et al.  Time-Aware Service Recommendation for Mashup Creation , 2015, IEEE Transactions on Services Computing.

[2]  David R. Karger,et al.  Human-powered Sorts and Joins , 2011, Proc. VLDB Endow..

[3]  Jia Zhang,et al.  Network Analysis of Scientific Workflows: A Gateway to Reuse , 2010, Computer.

[4]  Michael S. Bernstein,et al.  Atelier: Repurposing Expert Crowdsourcing Tasks as Micro-internships , 2016, CHI.

[5]  Alon Y. Halevy,et al.  Crowdsourcing systems on the World-Wide Web , 2011, Commun. ACM.

[6]  Panagiotis G. Ipeirotis,et al.  Reputation Transferability in Online Labor Markets , 2016, Manag. Sci..

[7]  Cheng Wu,et al.  Category-Aware API Clustering and Distributed Recommendation for Automatic Mashup Creation , 2015, IEEE Transactions on Services Computing.

[8]  Rajkumar Buyya,et al.  Computational Intelligence Based QoS-Aware Web Service Composition: A Systematic Literature Review , 2017, IEEE Transactions on Services Computing.

[9]  Wei Tan,et al.  Mirror, Mirror, on the Web, Which Is the Most Reputable Service of Them All? - A Domain-Aware and Reputation-Aware Method for Service Recommendation , 2013, ICSOC.

[10]  Jessica R. Murray,et al.  Crowdsourced earthquake early warning , 2015, Science Advances.

[11]  Daren C. Brabham MOVING THE CROWD AT THREADLESS , 2010 .

[12]  Asuman E. Ozdaglar,et al.  Managing Innovation in a Crowd , 2014, EC.

[13]  Jia Zhang,et al.  ReputationNet: Reputation-Based Service Recommendation for e-Science , 2015, IEEE Transactions on Services Computing.

[14]  Chien-Ju Ho,et al.  Online Task Assignment in Crowdsourcing Markets , 2012, AAAI.

[15]  Jia Zhang,et al.  Recommendation for Newborn Services by Divide-and-Conquer , 2017, 2017 IEEE International Conference on Web Services (ICWS).

[16]  Yi Liu,et al.  A Novel Equitable Trustworthy Mechanism for Service Recommendation in the Evolving Service Ecosystem , 2014, ICSOC.

[17]  Mark Harman,et al.  Developer Recommendation for Crowdsourced Software Development Tasks , 2015, 2015 IEEE Symposium on Service-Oriented System Engineering.

[18]  Angelos Stavrou,et al.  E-commerce Reputation Manipulation: The Emergence of Reputation-Escalation-as-a-Service , 2015, WWW.

[19]  Cheng Wu,et al.  SeCo-LDA: Mining Service Co-occurrence Topics for Recommendation , 2016, 2016 IEEE International Conference on Web Services (ICWS).

[20]  Panagiotis G. Ipeirotis,et al.  The Utility of Skills in Online Labor Markets , 2014, ICIS.

[21]  Kamesh Munagala,et al.  Collaborative Optimization for Collective Decision-making in Continuous Spaces , 2017, WWW.

[22]  Wei Tan,et al.  An Empirical Study of Programmable Web: A Network Analysis on a Service-Mashup System , 2012, 2012 IEEE 19th International Conference on Web Services.

[23]  Hongbing Wang,et al.  Effective service composition using multi-agent reinforcement learning , 2016, Knowl. Based Syst..

[24]  Kenneth C. Wilbur A Two-Sided, Empirical Model of Television Advertising and Viewing Markets , 2008, Mark. Sci..

[25]  Ugur Demiryurek,et al.  PaRE: A System for Personalized Route Guidance , 2017, WWW.

[26]  Sihem Amer-Yahia,et al.  Worker Skill Estimation in Team-Based Tasks , 2015, Proc. VLDB Endow..

[27]  Zibin Zheng,et al.  Selecting an Optimal Fault Tolerance Strategy for Reliable Service-Oriented Systems with Local and Global Constraints , 2015, IEEE Transactions on Computers.

[28]  David Gross-Amblard,et al.  Using Hierarchical Skills for Optimized Task Assignment in Knowledge-Intensive Crowdsourcing , 2016, WWW.

[29]  Richard Dobbs,et al.  A labor market that works: connecting talent with opportunity in the digital age , 2015 .

[30]  Puneet Manchanda,et al.  Quantifying Cross and Direct Network Effects in Online Consumer-to-Consumer Platforms , 2016, Mark. Sci..

[31]  Reynold Cheng,et al.  DOCS: a domain-aware crowdsourcing system using knowledge bases , 2016, VLDB 2016.

[32]  Ming Yin,et al.  Bonus or Not? Learn to Reward in Crowdsourcing , 2015, IJCAI.

[33]  Chandrakant D. Patel,et al.  Everything as a Service: Powering the New Information Economy , 2011, Computer.

[34]  Athman Bouguettaya,et al.  Trusting the Social Web: issues and challenges , 2013, World Wide Web.

[35]  Sihem Amer-Yahia,et al.  Personalized and Diverse Task Composition in Crowdsourcing , 2018, IEEE Transactions on Knowledge and Data Engineering.

[36]  Kristina McElheran,et al.  Do Market Leaders Lead in Business Process Innovation? The Case(s) of E-Business Adoption , 2014, Manag. Sci..

[37]  Wei Tan,et al.  Recommendation in an Evolving Service Ecosystem Based on Network Prediction , 2014, IEEE Transactions on Automation Science and Engineering.

[38]  Boualem Benatallah,et al.  Web Service Composition , 2015 .

[39]  Qinyuan Feng,et al.  Vulnerabilities and countermeasures in context-aware social rating services , 2012, TOIT.

[40]  Sihem Amer-Yahia,et al.  Task assignment optimization in knowledge-intensive crowdsourcing , 2015, The VLDB Journal.

[41]  Zibin Zheng,et al.  Reputation Measurement and Malicious Feedback Rating Prevention in Web Service Recommendation Systems , 2015, IEEE Transactions on Services Computing.

[42]  Schahram Dustdar,et al.  Auction-based crowdsourcing supporting skill management , 2013, Inf. Syst..

[43]  Kwong-Sak Leung,et al.  TaskRec: A Task Recommendation Framework in Crowdsourcing Systems , 2015, Neural Processing Letters.

[44]  Reynold Cheng,et al.  QASCA: A Quality-Aware Task Assignment System for Crowdsourcing Applications , 2015, SIGMOD Conference.

[45]  Athanasios V. Vasilakos,et al.  Web services composition: A decade's overview , 2014, Inf. Sci..

[46]  Jia Zhang,et al.  Optimizing Semantic Annotations for Web Service Invocation , 2019, IEEE Transactions on Services Computing.

[47]  Xiaofei Xu,et al.  Freelancer Influence Evaluation and Gig Service Quality Prediction in Fiverr , 2017, 2017 IEEE International Conference on Web Services (ICWS).

[48]  Jacki O'Neill,et al.  Being a turker , 2014, CSCW.

[49]  Krzysztof Z. Gajos,et al.  Curiosity Killed the Cat, but Makes Crowdwork Better , 2016, CHI.

[50]  Geoffrey G. Parker,et al.  Two-Sided Network Effects: A Theory of Information Product Design , 2010, Manag. Sci..

[51]  Wolf-Tilo Balke,et al.  Skill Ontology-Based Model for Quality Assurance in Crowdsourcing , 2014, DASFAA Workshops.

[52]  Cheng Wu,et al.  Service Recommendation from the Evolution of Composition Patterns , 2017, 2017 IEEE International Conference on Services Computing (SCC).

[53]  R Archana,et al.  Location-Aware and Personalized Collaborative Filtering For Web Service Recommendation , 2016 .

[54]  James A. Hendler,et al.  From the Semantic Web to social machines: A research challenge for AI on the World Wide Web , 2010, Artif. Intell..

[55]  Jonathan Krause,et al.  Leveraging the Wisdom of the Crowd for Fine-Grained Recognition , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[56]  Wil M. P. van der Aalst,et al.  Using Process Mining to Generate Accurate and Interactive Business Process Maps , 2009, BIS.

[57]  Bing Bai,et al.  Service Recommendation for Mashup Creation Based on Time-Aware Collaborative Domain Regression , 2015, 2015 IEEE International Conference on Web Services.