Probabilistic Databases (Synthesis Lectures on Data by Dan Suciu, Dan Olteanu, Christopher Ré, Christoph Koch

Probabilistic Databases (Synthesis Lectures on Data by Dan Suciu, Dan Olteanu, Christopher Ré, Christoph Koch

By Dan Suciu, Dan Olteanu, Christopher Ré, Christoph Koch

Probabilistic databases are databases the place the price of a few attributes or the presence of a few documents are doubtful and identified basically with a few chance. purposes in lots of components resembling details extraction, RFID and clinical information administration, information cleansing, info integration, and monetary threat evaluation produce huge volumes of doubtful info, that are most sensible modeled and processed by way of a probabilistic database. This ebook offers the cutting-edge in illustration formalisms and question processing thoughts for probabilistic info. It starts off through discussing the fundamental rules for representing huge probabilistic databases, through decomposing them into tuple-independent tables, block-independent-disjoint tables, or U-databases. Then it discusses sessions of recommendations for question evaluate on probabilistic databases. In extensional question evaluate, the complete probabilistic inference might be driven into the database engine and, consequently, processed as successfully because the evaluate of normal SQL queries. The relational queries that may be evaluated this manner are known as secure queries. In intensional question evaluate, the probabilistic inference is played over a propositional formulation referred to as lineage expression: each relational question will be evaluated this fashion, however the information complexity dramatically depends upon the question being evaluated, and will be #P-hard. The publication additionally discusses a few complicated subject matters in probabilistic information administration similar to top-k question processing, sequential probabilistic databases, indexing and materialized perspectives, and Monte Carlo databases. desk of Contents: evaluation / information and question version / The question evaluate challenge / Extensional question overview / Intensional question assessment / complicated options

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Are their marginal probabilities. This is the semantics that will be our main focus in the next chapter. The intuition behind it is very simple. On a deterministic database, the query Q returns a set of tuples {t1 , t2 , . }, while on a probabilistic database, it returns a set of tuple-probability pairs {(t1 , p1 ), (t2 , p2 ), . }. These answers can be returned to the user in decreasing order of their probabilities, such that p1 ≥ p2 ≥ . . Notice that while in incomplete databases, we have two variants of the tuple answer semantics, Qposs and Qcert ; in probabilistic databases, we only have one.

These answers can be returned to the user in decreasing order of their probabilities, such that p1 ≥ p2 ≥ . . Notice that while in incomplete databases, we have two variants of the tuple answer semantics, Qposs and Qcert ; in probabilistic databases, we only have one. The connection between them is given by the following, where D = (W, P ): Qposs (W) ={t | (t, p) ∈ Q(D), p > 0} Qcert (W) ={t | (t, p) ∈ Q(D), p = 1} The possible tuples semantics is not compositional. Once we compute the result of a query Q(D), we can no longer apply a new query Q because Q(D) is not a probabilistic database: it is only a collection of tuples and probabilities.

In the second, the query is also applied to all possible worlds, but the set of answers are combined, and a single set of tuples is returned to the user; this is called possible answers semantics. This result can be easily presented to the user, as a list of tuples, but it is no longer compositional since we lose track of how tuples are grouped into worlds. We allow the query to be any function from an input database instance to an output relation: in other words, for the definitions in this section, we do not need to restrict the query to the relational calculus.

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