UDS: Sustaining quality of context using uninterruptible data supply system

Naoya Namatame, Jin Nakazawa, Kazunori Takashio, Hideyuki Tokuda

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルQuality of Context - First International Workshop, QuaCon 2009, Revised Papers
ページ109-119
ページ数11
DOI
出版ステータスPublished - 2009 12 1
イベント1st International Workshop on Quality of Context, QuaCon 2009 - Stuttgart, Germany
継続期間: 2009 6 252009 6 26

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
5786 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other1st International Workshop on Quality of Context, QuaCon 2009
CountryGermany
CityStuttgart
Period09/6/2509/6/26

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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