Skip to main content
SpringerLink
Log in

An evolvable and transparent data as a service framework for multisource data integration and fusion

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

Combining data from multiple sources is a means of enabling unified and comprehensive description of objects in high-dimensional space and helping unlock the potential value of such data. In recent years, more and more studies have focused on this field of research. However, challenges posed by separately stored data and comprehension barriers about different systems hinder the integration of data from different sources. To overcome these problems, this paper proposes a Transparent Data as a Service framework, a novel approach combining Transparent Computing and Representational State Transfer (REST) Web Services based on Linked Data. This framework is capable of integrating data from different sources and offering data services in a transparent way. That is, consumers use data services without the need to know details of where or how the data are stored. Our framework is transparent on three levels: transparent data resource integration, transparent data fusion and transparent data service provision. The Data Model Pool and Data Resource Pool are able to evolve as new data models and datasets are generated in the provision of data services. Finally, we demonstrate the feasibility of the framework by implementing a prototype system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Toppeta D (2010) The smart city vision: How innovation and ict can build smart, livable, sustainable cities. The Innovation Knowledge Foundation Think

  2. Zheng Y (2015) Methodologies for cross-domain data fusion: An overview. IEEE Trans Big Data 1(1):16–34

    Article  Google Scholar 

  3. Wang S, He L, Stenneth L, Philip S Y, Li Z, Huang Z (2016) Estimating urban traffic congestions with multi-sourced data. In: 2016 17th IEEE International conference on mobile data management (MDM), vol 1. IEEE, pp 82–91

  4. Klein LA (2004) Sensor and data fusion: A tool for information assessment and decision making, vol 324. Spie Press Bellingham

  5. Zhang Y, Guo K, Ren J, Zhou Y, Wang J, Chen J (2017) Transparent computing: A promising network computing paradigm. Comput Sci Eng 19(1):7–20

    Article  Google Scholar 

  6. Zhang Y, Zhou Y (2006) Transparent computing: A new paradigm for pervasive computing. In: International conference on ubiquitous intelligence and computing. Springer, pp 1–11

  7. Lanthaler M, Gütl C (2012) On using json-ld to create evolvable restful services. In: Proceedings of the third international workshop on RESTful design. ACM, pp 25–32

  8. Janowicz K, Hitzler P, Adams B, Kolas D, Vardeman II et al (2014) Five stars of linked data vocabulary use. Sem Web 5(3):173–176

    Google Scholar 

  9. Sporny M, Kellogg G, Lanthaler M, W3C RDF Working Group, et al (2014) Json-ld 1.0: A json-based serialization for linked data. W3C Recomm:16

  10. Weiser M (1991) The computer for the twenty-first century. In: Scientific American. IEEE, pp 94–104

  11. Bizer C (2009) The emerging web of linked data. IEEE Intell Syst 24(5):87–92

    Article  Google Scholar 

  12. Bizer C, Heath T, Berners-Lee T (2009) Linked data-the story so far, pp 205–227

  13. Daniel V, Lewis J (2011) Computer scientist. An update on RDF concepts and some ontologies. http://www.ibm.com/developerworks/xml/library/x-rdfconcepts/index.html

  14. Lanthaler M, Gütl C (2013) Hydra: A vocabulary for hypermedia-driven web apis. LDOW:996

  15. Fielding RT, Taylor RN (2002) Principled design of the modern web architecture, vol 2. ACM, pp 115–150

  16. Pautasso C (2014) Restful web services: Principles, patterns, emerging technologies. In: Web services foundations. Springer, pp 31–51

  17. Sato A, Huang R (2015) From data to knowledge: A cognitive approach to retail business intelligence. In: 2015 IEEE International conference on data science and data intensive systems. IEEE, pp 210–217

  18. Sato A, Huang R (2015) A generic formulated kid model for pragmatic processing of data, information, and knowledge. In: 2015 IEEE 12th Intl conf on ubiquitous intelligence and computing and 2015 IEEE 12th intl conf on autonomic and trusted computing and 2015 IEEE 15th intl conf on scalable computing and communications and its associated workshops (UIC-ATC-ScalCom). IEEE, pp 609–616

  19. Sato A, Huang R, Yen N Y (2015) Design of fusion technique-based mining engine for smart business. Human-centric Comput Inf Sci 5(1):1

    Article  Google Scholar 

  20. Fan W, Chen Z, Xiong Z, Chen H (2012) The internet of data: A new idea to extend the iot in the digital world, vol 6. Springer, pp 660–667

  21. Consoli S, Mongiovì M, Recupero D R, Peroni S, Gangemi A, Nuzzolese A G, Presutti V Producing linked data for smart cities: The case of catania

  22. Xinhua E, Han J, Wang Y, Liu L (2013) Big data-as-a-service: Definition and architecture. In: 2013 15th IEEE International conference on communication technology (ICCT), pp 738–742

  23. Bowen D u, Huang R, Chen X, Xie Z, Liang Y, Lv W, Ma J (2016) Active ctdaas: A data service framework based on transparent iod in city traffic. IEEE

  24. Zhang Y, Ren J, Liu J, Xu C, Guo H, Liu Y (2017) A survey on emerging computing paradigms for big data. Chin J Electron 26(1)

  25. Guha R (2011) Introducing schema. org: Search engines come together for a richer web. Google Official Blog

  26. Castanedo F (2013) A review of data fusion techniques. Sci World J:2013

  27. Ren J, Zhang Y, Zhang K, Shen X (2015) Exploiting mobile crowdsourcing for pervasive cloud services: Challenges and solutions. IEEE Communications Magazine 53(3):98–105

    Article  Google Scholar 

Download references

Acknowledgements

The work is partially supported by the Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research (No. 25330270 and No. 26330350), and by National Natural Science Foundation of China (No. 51408018).

Author information

Authors and Affiliations

  1. State Key Laboratory of Software Development Environment, Beihang University, Beijing, China

    Zhipu Xie, Weifeng Lv, Linfang Qin & Bowen Du

  2. Faculty of Computer and Information Sciences, Hosei University, Tokyo, Japan

    Runhe Huang

Authors
  1. Zhipu Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weifeng Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Linfang Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bowen Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Runhe Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bowen Du.

Additional information

This article is part of the Topical Collection: Special Issue on Transparent Computing

Guest Editors: Jiannong Cao, Jingde Cheng, Jianhua Ma, and Ju Ren

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xie, Z., Lv, W., Qin, L. et al. An evolvable and transparent data as a service framework for multisource data integration and fusion. Peer-to-Peer Netw. Appl. 11, 697–710 (2018). https://doi.org/10.1007/s12083-017-0555-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-017-0555-7

Keywords

Search

Navigation