Efficient Query Processing Platform for Uncertain Big Data

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.