Multidimensional Analysis Framework on Massive Data of Observations of Daily Living

  • Jianhua LuEmail author
  • Baili Zhang
  • Xueyan Wang
  • Ningyun Lu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10594)


Observations of daily living (ODLs) are cues that people attend to in the course of their everyday life, that inform them about their health. In order to better understand the ODLs, we propose a set of innovative multi-dimensional analysis concepts and methods. Firstly, the ODLs are organized as directed graphs according the “observation-property” relationships and the chronological order of observations, which represents all the information in a flexible way; Secondly, a novel concept, the structure dimension, is proposed to integrate into the traditional multidimensional analysis framework. From the structure dimension that consists of three granularities, vertices, edges and subgraphs, one can get a clearer view of the ODLs; Finally, the hierarchy of ODLs Cube is introduced, and the semantics of OLAP operations, Roll-up, Drill-down and Slice/dice, are redefined to accommodate the structure dimension. The proposed structure dimension and ODLs cube are effective for multidimensional analysis of ODLs.


ODLs Multidimensional analysis OLAP Graph cube 


  1. 1.
    Backonja, U., et al.: Observations of daily living: putting the “personal” in personal health records. In: NI 2012: Proceedings of the 11th International Congress on Nursing Informatics, vol. 2012. American Medical Informatics Association (2012)Google Scholar
  2. 2.
    Wolf, G.: The data-driven life. The New York Times 28, 2010 (2010)Google Scholar
  3. 3.
    Harinarayan, V., Rajaraman, A., Ullman, J.D.: Implementing data cubes efficiently. ACM SIGMOD Rec. 25(2), 205–216 (1996)CrossRefGoogle Scholar
  4. 4.
    Gray, J., et al.: Data cube: a relational aggregation operator generalizing group-by, cross-tab, and sub-totals. Data Min. Knowl. Disc. 1(1), 29–53 (1997)CrossRefGoogle Scholar
  5. 5.
    Chen, C., et al.: Graph OLAP: towards online analytical processing on graphs. In: Proceeding of the Eighth IEEE International Conference on Data Mining (2008)Google Scholar
  6. 6.
    Li, C., et al.: Modeling, design and implementation of graph OLAPing. J. Softw. 22(2), 258–268 (2011)CrossRefGoogle Scholar
  7. 7.
    Zhao, P., et al.: Graph cube: on warehousing and OLAP multidimensional networks. In: Proceeding of the 2011 ACM SIGMOD International Conference on Management of Data (2011)Google Scholar
  8. 8.
    Yin, M., Bin, W., Zeng, Z.: HMGraph OLAP: a novel framework for multi-dimensional heterogeneous network analysis. In: Proceeding of the 15th International Workshop on Data Warehousing and OLAP (2012)Google Scholar
  9. 9.
    Denis, B., Ghrab, A., Skhiri, S.: A distributed approach for graph-oriented multidimensional analysis. In: Proceeding of 2013 IEEE International Conference on Big Data (2013)Google Scholar
  10. 10.
    Wang, Z., et al.: Pagrol: parallel graph OLAP over large-scale attributed graphs. In: ICDE 2014 (2014)Google Scholar
  11. 11.
    Hannachi, L., et al.: Social microblogging cube. In: Proceeding of the 16th International Workshop on Data Warehousing and OLAP (2013)Google Scholar
  12. 12.
    Rehman, N.U., Weiler, A., Scholl, M.H.: OLAPing social media: the case of Twitter. In: Proceeding of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (2013)Google Scholar
  13. 13.
    Qu, Q., et al.: Efficient topological OLAP on information networks. In: Database Systems for Advanced Applications (2011)Google Scholar
  14. 14.
    Jakawat, W., Favre, C., Loudcher, S.: OLAP on information networks: a new framework for dealing with bibliographic data. In: Catania, B., et al. (eds.) New Trends in Databases and Information Systems. Advances in Intelligent Systems and Computing, vol. 241, pp. 361–370. Springer, Cham (2014). doi: 10.1007/978-3-319-01863-8_38 CrossRefGoogle Scholar
  15. 15.
    Brennan, P.F., Casper, G.: Observing health in everyday living: ODLs and the care-between-the-care. Pers. Ubiquit. Comput. 19(1), 3–8 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jianhua Lu
    • 1
    Email author
  • Baili Zhang
    • 1
  • Xueyan Wang
    • 1
  • Ningyun Lu
    • 2
  1. 1.Southeast UniversityNanjingChina
  2. 2.Nanjing University of Aeronautics and AstronauticsNanjingChina

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