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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)

Abstract

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.

Keywords

ODLs Multidimensional analysis OLAP Graph cube 

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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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