Advertisement

Exploratory Search of Web Data Services Based on Collective Intelligence

  • Devis BianchiniEmail author
  • Valeria De Antonellis
  • Michele Melchiori
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10360)

Abstract

Developers of data-intensive web applications benefit from the integration of data sourced from the web. Web data services are solutions off-the-shelf, provided by third parties, that enable access to web data sources. Web data services are usually discovered according to different features, related to lightweight descriptions. Recent approaches in literature convey on new research challenges, considering also collective intelligence in developers’ networks, containing information about service co-usage in existing applications and ratings on services given by developers who used them in their own development experiences. Following this direction, in this paper, we contribute with a distinguishing viewpoint, by proposing an explorative approach, that enables web applications developers to iteratively discover services of interest by also relying on collective intelligence, in a Web 2.0 context.

Keywords

Web data service model Exploratory search Collective intelligence Web-oriented architecture 

References

  1. 1.
    Idreos, S., Papaemmanouil, O., Chaudhuri, S.: Overview of data exploration techniques. In: ACM Conference on Management of Data (SIGMOD) (2015)Google Scholar
  2. 2.
    Bianchini, D., Antonellis, V., Melchiori, M.: Capitalizing the designers’ experience for improving web API selection. In: Meersman, R., Panetto, H., Dillon, T., Missikoff, M., Liu, L., Pastor, O., Cuzzocrea, A., Sellis, T. (eds.) OTM 2014. LNCS, vol. 8841, pp. 364–381. Springer, Heidelberg (2014). doi: 10.1007/978-3-662-45563-0_21 Google Scholar
  3. 3.
    Cao, B., Liu, X., Li, B., Liu, J., Tang, M., Zhang, T.: Mashup service clustering based on an integration of service content and network via exploiting a two-level topic model. In: Proceedings of the 23rd International Conference on Web Services (ICWS 2016) (2016)Google Scholar
  4. 4.
    Xiong, W., Wu, Z., Li, B., Gu, Q., Yuan, L., Hang, B.: Inferring service recommendation from natural language api description. In: Proceedings of the 23rd International Conference on Web Services (ICWS 2016) (2016)Google Scholar
  5. 5.
    Gao, W., Chen, L., Wu, J., Bouguettaya, A.: Joint modeling users, services, mashups and topics for service recommendation. In: Proceedings of the 23rd International Conference on Web Services (ICWS 2016) (2016)Google Scholar
  6. 6.
    Liu, X., Fulia, I.: Incorporating user, topic, and service related latent factors into web service recommendation. In: IEEE International Conference on Web Services (2015)Google Scholar
  7. 7.
    Balakrishnan, R., Kambhampati, S., Manishkumar, J.: Assessing relevance and trust of the deep web sources and results based on inter-source agreement. ACM Trans. Web 7(2), 32 (2013)CrossRefGoogle Scholar
  8. 8.
    Vaculin, R., Neruda, C., Sycara, K.: Modeling and discovery of data providing services. In: Proceedings of the 2008 IEEE International Conference on Web Services, pp. 1032–1039 (2008)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Devis Bianchini
    • 1
    Email author
  • Valeria De Antonellis
    • 1
  • Michele Melchiori
    • 1
  1. 1.Department of Information EngineeringUniversity of BresciaBresciaItaly

Personalised recommendations