UDS: Sustaining quality of context using uninterruptible data supply system

Naoya Namatame, Jin Nakazawa, Kazunori Takashio, Hideyuki Tokuda

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Context mining algorithms from sensor data have been researched and successful results have been shown. However, since these existing works are focused on improving the accuracy of context mining, they are established on the assumption that they can acquire a complete set of necessary data. Therefore, the context mining algorithms do not work sufficiently since the data drops easily in the reality. In this paper, to cope with this problem, we propose a middleware named UDS (Uninterruptible Data Supply System). The system compensates the missing data, creates virtually complete dataset and provides upper layer applications. Applications operating over UDS can work sufficiently with some data actually missing. We have defined two types of characteristic data deficit patterns and created a robust model for both patterns utilizing Bayesian Network. In the evaluation, we show UDS can sustain the quality of context over 80% with 40% data missing.

Original languageEnglish
Title of host publicationQuality of Context - First International Workshop, QuaCon 2009, Revised Papers
Number of pages11
Publication statusPublished - 2009
Event1st International Workshop on Quality of Context, QuaCon 2009 - Stuttgart, Germany
Duration: 2009 Jun 252009 Jun 26

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5786 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other1st International Workshop on Quality of Context, QuaCon 2009


  • Context Inference
  • Data Compensation
  • Reliable System

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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