9,374 research outputs found
Efficient Subgraph Similarity Search on Large Probabilistic Graph Databases
Many studies have been conducted on seeking the efficient solution for
subgraph similarity search over certain (deterministic) graphs due to its wide
application in many fields, including bioinformatics, social network analysis,
and Resource Description Framework (RDF) data management. All these works
assume that the underlying data are certain. However, in reality, graphs are
often noisy and uncertain due to various factors, such as errors in data
extraction, inconsistencies in data integration, and privacy preserving
purposes. Therefore, in this paper, we study subgraph similarity search on
large probabilistic graph databases. Different from previous works assuming
that edges in an uncertain graph are independent of each other, we study the
uncertain graphs where edges' occurrences are correlated. We formally prove
that subgraph similarity search over probabilistic graphs is #P-complete, thus,
we employ a filter-and-verify framework to speed up the search. In the
filtering phase,we develop tight lower and upper bounds of subgraph similarity
probability based on a probabilistic matrix index, PMI. PMI is composed of
discriminative subgraph features associated with tight lower and upper bounds
of subgraph isomorphism probability. Based on PMI, we can sort out a large
number of probabilistic graphs and maximize the pruning capability. During the
verification phase, we develop an efficient sampling algorithm to validate the
remaining candidates. The efficiency of our proposed solutions has been
verified through extensive experiments.Comment: VLDB201
A Practical View on Renaming
We revisit variable renaming from a practitioner's point of view, presenting
concepts we found useful in dealing with operational semantics of pure Prolog.
A concept of relaxed core representation is introduced, upon which a concept of
prenaming is built. Prenaming formalizes the intuitive practice of renaming
terms by just considering the necessary bindings, where now some passive
"bindings" x/x may be necessary as well. As an application, a constructive
version of variant lemma for implemented Horn clause logic has been obtained.
There, prenamings made it possible to incrementally handle new (local)
variables.Comment: In Proceedings WLP'15/'16/WFLP'16, arXiv:1701.0014
Differential Privacy in Metric Spaces: Numerical, Categorical and Functional Data Under the One Roof
We study Differential Privacy in the abstract setting of Probability on
metric spaces. Numerical, categorical and functional data can be handled in a
uniform manner in this setting. We demonstrate how mechanisms based on data
sanitisation and those that rely on adding noise to query responses fit within
this framework. We prove that once the sanitisation is differentially private,
then so is the query response for any query. We show how to construct
sanitisations for high-dimensional databases using simple 1-dimensional
mechanisms. We also provide lower bounds on the expected error for
differentially private sanitisations in the general metric space setting.
Finally, we consider the question of sufficient sets for differential privacy
and show that for relaxed differential privacy, any algebra generating the
Borel -algebra is a sufficient set for relaxed differential privacy.Comment: 18 Page
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