Constructing Provenance Cubes Based on Semantic Neuroimaging Data Provenances

  • Jianhui ChenEmail author
  • Jianhua Feng
  • Ning Zhong
  • Zhisheng Huang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 480)


The systematic Brain Informatics (BI) study is a data-driven process and all decision-making and suppositions depend on the deep understanding of brain data. Aiming at unstructured brain data, semantic neuroimaging data provenances, called BI provenances, have been constructed to support the quick and comprehensive understanding about data origins and data processing. However, the existing file-based or transaction-database-based provenance queries cannot effectively meet the requirements of understanding data and generating decision or suppositions in the systematic study, which needs multi-aspect and multi-granularity information of provenances. Inspired by the online analytical processing (OLAP) system, this paper proposes provenance cubes to support multi-aspect and multi-granularity provenance queries. A Data-Brain based approach is also designed to develop a BI OLAP system based on provenances cubes. The case study demonstrates significance and usefulness of the proposed approach.


Online Analytical Processing (OLAP) Provenance Queries OLAP System Brain Informatics (BI) Extract Provenance Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The work is supported by National Basic Research Program of China (2014CB744600), China Postdoctoral Science Foundation (2013M540096), International Science & Technology Cooperation Program of China (2013DFA32180), National Natural Science Foundation of China (61272345), Open Foundation of Key Laboratory of Multimedia and Intelligent Software (Beijing University of Technology), Beijing.


  1. 1.
    Zhong, N., Chen, J.H.: Constructing a new-style conceptual model of brain data for systematic brain informatics. IEEE Trans. Knowl. Data Eng. 24(12), 2127–2142 (2011)CrossRefGoogle Scholar
  2. 2.
    MacKenzie-Graham, A.J., Horn, J.D.V., Woods, R.P., Crawford, K.L., Toga, A.W.: Provenance in neuroimaging. NeuroImage 42(1), 178–195 (2008)CrossRefGoogle Scholar
  3. 3.
    Chen, J.H., Zhong, N., Liang, P.P.: Data-brain driven systematic human brain data analysis: a case study in numerical inductive reasoning centric investigation. Cogn. Syst. Res. Int. J. 15(16), 17–32 (2012)CrossRefGoogle Scholar
  4. 4.
    McGuinness, D.L., Harmelen, F.V.: Owl web ontology language overview. Technical report, World Wide Web Consortium (W3C) recommendation (2004).
  5. 5.
    Klyne, G., Carroll, J.J.: Resource description framework (rdf): concepts and abstract syntax. Technical report, World Wide Web Consortium (W3C) recommendation (2004).
  6. 6.
    Prud’hommeaux, E., Seaborne, A.: Sparql query language for rdf. Technical report, World Wide Web Consortium (W3C) recommendation (2008).
  7. 7.
    Greenfield, D., Lyon, G.F., Vogl, R., Feinstein, S.: System and method for online analytical processing. Technical report 7,010,523, Google Patents (2006)Google Scholar
  8. 8.
    Romero, O., Abello, A.: A framework for multidimensional design of data warehouses from ontologies. Data Knowl. Eng. 69(11), 1138–1157 (2010)CrossRefGoogle Scholar
  9. 9.
    Skoutas, D., Simitsis, A.: Ontology-based conceptual design of etl processes for both structured and semi-structured data. Int. J. Seman. Web Inf. Syst. 3(4), 1–24 (2007)CrossRefGoogle Scholar
  10. 10.
    Vassiliadis, P.: Modeling multidimensional databases, cubes and cube operations. In: Proceedings of 10th International Conference on Scientific and Statistical Database Management (SSDBM), Capri, Italy, pp. 53–62 (1998)Google Scholar
  11. 11.
    Liang, P., Zhong, N., Lu, S., Liu, J., Yao, Y., Li, K., Yang, Y.: The neural mechanism of human numerical inductive reasoning process: a combined ERP and fMRI study. In: Zhong, N., Liu, J., Yao, Y., Wu, J., Lu, S., Li, K. (eds.) WImBI 2006. LNCS (LNAI), vol. 4845, pp. 223–243. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Chaudhuri, S., Dayal, U.: An overview of data warehousing and olap technology. ACM SIGMOD Rec. 26(1), 65–74 (1997)CrossRefGoogle Scholar
  13. 13.
    Lu, S.F., Liang, P.P., Yang, Y.H., Li, K.C.: Recruitment of the pre-motor area in human inductive reasoning: an fmri study. Cogn. Syst. Res. Int. J. 1(1), 74–80 (2010)CrossRefGoogle Scholar
  14. 14.
    Jia, X.Q., Liang, P.P., Lu, J., Yang, Y.H., Zhong, N., Li, K.C.: Common and dissociable neural correlates associated with component processes of inductive reasoning. NeuroImage 56(4), 2292–2299 (2011)CrossRefGoogle Scholar
  15. 15.
    Mei, Y., Liang, P.P., Lu, S.F., Zhong, N., Li, K.C., Yang, Y.H.: Neural mechanism of figural inductive reasoning: an fmri study. Acta Psychol. Sin. 42(4), 496–506 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Jianhui Chen
    • 1
    Email author
  • Jianhua Feng
    • 1
  • Ning Zhong
    • 2
    • 4
  • Zhisheng Huang
    • 3
    • 4
  1. 1.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  2. 2.Department of Life Science and InformaticsMaebashi Institute of TechnologyMaebashi-CityJapan
  3. 3.Department of Computer ScienceVrije University AmsterdamAmsterdamThe Netherlands
  4. 4.International WIC InstituteBeijing University of TechnologyBeijingChina

Personalised recommendations