Earticle

초록

영어

Query processing technology has recently received a lot of attention in the business intelligence and information service communities. However, the existing approaches can not efficiently optimize the query performance in the uncertain big data environment. In this paper, we propose QPPUBG, a novel and efficient query processing platform for uncertain big data. QPPUBG mainly includes four modules: (i) query equivalence reconstructing for uncertain big data; (ii) multiple query optimization over probability relation components; (iii) query execution plan constructing over probability relation components, and (iv) physical implementation solution of query for uncertain big data. Specially, QPPUBG can support the possible world instance semantics and efficiently handle arbitrary decision spaces. Moreover, QPPUBG can seamlessly integrate the above four modules into the modern parallel computation frameworks. We present the extensive experiments that demonstrate QPPUBG is both efficient and effective.

목차

Abstract
 1. Introduction
 2. Platform Framework Overview
  2.1. Module 1: Query Equivalence Reconstructing for Uncertain Big Data
  2.2. Module 2: Multiple Query Optimization over Probability Relation Components
  2.3. Module 3: Query Execution Plan Constructing Over Probability Relation Components
  2.4. Module 4: Physical Implementation Solution of Query for Uncertain Big Data
 3. Specific Realization of Our QPPUBG Platform
  3.1. Realization for Module 1
  3.2. Realization for Module 2
  3.3. Realization for Module 3
  3.4. Realization for Module 4
 4. The Advantages of our QPPUBG Platform
 5. Experimental Evaluation
 6. Conclusions
 Acknowledgments
 References

키워드

저자

• Zhenhua Huang [ Department of Computer Science Tongji University, Shanghai, China ]
• Jiawen Zhang [ Department of Computer Science Tongji University, Shanghai, China ]
• Qiang Fang [ Department of Computer Science Tongji University, Shanghai, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605