94,850 research outputs found
Infinite Probabilistic Databases
Probabilistic databases (PDBs) are used to model uncertainty in data in a quantitative way. In the standard formal framework, PDBs are finite probability spaces over relational database instances. It has been argued convincingly that this is not compatible with an open-world semantics (Ceylan et al., KR 2016) and with application scenarios that are modeled by continuous probability distributions (Dalvi et al., CACM 2009).
We recently introduced a model of PDBs as infinite probability spaces that addresses these issues (Grohe and Lindner, PODS 2019). While that work was mainly concerned with countably infinite probability spaces, our focus here is on uncountable spaces. Such an extension is necessary to model typical continuous probability distributions that appear in many applications. However, an extension beyond countable probability spaces raises nontrivial foundational issues concerned with the measurability of events and queries and ultimately with the question whether queries have a well-defined semantics.
It turns out that so-called finite point processes are the appropriate model from probability theory for dealing with probabilistic databases. This model allows us to construct suitable (uncountable) probability spaces of database instances in a systematic way. Our main technical results are measurability statements for relational algebra queries as well as aggregate queries and Datalog queries
Securing Databases from Probabilistic Inference
Databases can leak confidential information when users combine query results
with probabilistic data dependencies and prior knowledge. Current research
offers mechanisms that either handle a limited class of dependencies or lack
tractable enforcement algorithms. We propose a foundation for Database
Inference Control based on ProbLog, a probabilistic logic programming language.
We leverage this foundation to develop Angerona, a provably secure enforcement
mechanism that prevents information leakage in the presence of probabilistic
dependencies. We then provide a tractable inference algorithm for a practically
relevant fragment of ProbLog. We empirically evaluate Angerona's performance
showing that it scales to relevant security-critical problems.Comment: A short version of this paper has been accepted at the 30th IEEE
Computer Security Foundations Symposium (CSF 2017
Duplicate Detection in Probabilistic Data
Collected data often contains uncertainties. Probabilistic databases have been proposed to manage uncertain data. To combine data from multiple autonomous probabilistic databases, an integration of probabilistic data has to be performed. Until now, however, data integration approaches have focused on the integration of certain source data (relational or XML). There is no work on the integration of uncertain (esp. probabilistic) source data so far. In this paper, we present a first step towards a concise consolidation of probabilistic data. We focus on duplicate detection as a representative and essential step in an integration process. We present techniques for identifying multiple probabilistic representations of the same real-world entities. Furthermore, for increasing the efficiency of the duplicate detection process we introduce search space reduction methods adapted to probabilistic data
Explicit probabilistic models for databases and networks
Recent work in data mining and related areas has highlighted the importance
of the statistical assessment of data mining results. Crucial to this endeavour
is the choice of a non-trivial null model for the data, to which the found
patterns can be contrasted. The most influential null models proposed so far
are defined in terms of invariants of the null distribution. Such null models
can be used by computation intensive randomization approaches in estimating the
statistical significance of data mining results.
Here, we introduce a methodology to construct non-trivial probabilistic
models based on the maximum entropy (MaxEnt) principle. We show how MaxEnt
models allow for the natural incorporation of prior information. Furthermore,
they satisfy a number of desirable properties of previously introduced
randomization approaches. Lastly, they also have the benefit that they can be
represented explicitly. We argue that our approach can be used for a variety of
data types. However, for concreteness, we have chosen to demonstrate it in
particular for databases and networks.Comment: Submitte
Infinite Probabilistic Databases
Probabilistic databases (PDBs) model uncertainty in data in a quantitative
way. In the established formal framework, probabilistic (relational) databases
are finite probability spaces over relational database instances. This
finiteness can clash with intuitive query behavior (Ceylan et al., KR 2016),
and with application scenarios that are better modeled by continuous
probability distributions (Dalvi et al., CACM 2009).
We formally introduced infinite PDBs in (Grohe and Lindner, PODS 2019) with a
primary focus on countably infinite spaces. However, an extension beyond
countable probability spaces raises nontrivial foundational issues concerned
with the measurability of events and queries and ultimately with the question
whether queries have a well-defined semantics.
We argue that finite point processes are an appropriate model from
probability theory for dealing with general probabilistic databases. This
allows us to construct suitable (uncountable) probability spaces of database
instances in a systematic way. Our main technical results are measurability
statements for relational algebra queries as well as aggregate queries and
Datalog queries.Comment: This is the full version of the paper "Infinite Probabilistic
Databases" presented at ICDT 2020 (arXiv:1904.06766
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