Solving a Special Case of the Intensional vs Extensional Conjecture in Probabilistic Databases

Mikaël Monet
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引用次数: 5

Abstract

We consider the problem of exact probabilistic inference for Union of Conjunctive Queries (UCQs) on tuple-independent databases. For this problem, two approaches currently coexist. In the extensional method, query evaluation is performed by exploiting the structure of the query, and relies heavily on the use of the inclusion--exclusion principle. In the intensional method, one first builds a representation of the lineage of the query in a tractable formalism of knowledge compilation. The chosen formalism should then ensure that the probability can be efficiently computed using simple disjointness and independence assumptions, without the need of performing inclusion--exclusion. The extensional approach has long been thought to be strictly more powerful than the intensional approach, the reason being that for some queries, the use of inclusion--exclusion seemed unavoidable. In this paper we introduce a new technique to construct lineage representations as deterministic decomposable circuits in polynomial time. We prove that this technique applies to a class of UCQs that had been conjectured to separate the complexity of the two approaches. In essence, we show that relying on the inclusion--exclusion formula can be avoided by using negation. This result brings back hope to prove that the intensional approach can handle all tractable UCQs.
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求解概率数据库中内延猜想的一个特例
研究了元独立数据库上连接查询联合的精确概率推理问题。对于这个问题,目前有两种方法并存。在扩展方法中,查询计算是通过利用查询的结构来执行的,并且严重依赖于包含-排除原则的使用。在内涵方法中,首先以易于处理的知识汇编形式构建查询沿袭的表示。然后,所选择的形式应该确保概率可以使用简单的不连接性和独立性假设有效地计算出来,而不需要执行包含-排除。长期以来,外延方法一直被认为严格地比内延方法更强大,原因是对于某些查询,使用包含-排除似乎是不可避免的。本文介绍了一种在多项式时间内将谱系表示构造为确定性可分解电路的新技术。我们证明了这种技术适用于一类ucq,这些ucq被推测用于分离两种方法的复杂性。从本质上讲,我们表明可以通过使用否定来避免依赖包含-排除公式。这一结果为证明内蕴方法可以处理所有可处理的ucq带来了希望。
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Probabilistic Databases for All Efficient Indexes for Diverse Top-k Range Queries Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems Parallel Algorithms for Sparse Matrix Multiplication and Join-Aggregate Queries Deciding Robustness for Lower SQL Isolation Levels
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