49,189 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
Probabilistic learning for selective dissemination of information
New methods and new systems are needed to filter or to selectively distribute the increasing volume of electronic information being produced nowadays. An effective information filtering system is one that provides the exact information that fulfills user's interests with the minimum effort by the user to describe it. Such a system will have to be adaptive to the user changing interest. In this paper we describe and evaluate a learning model for information filtering which is an adaptation of the generalized probabilistic model of information retrieval. The model is based on the concept of 'uncertainty sampling', a technique that allows for relevance feedback both on relevant and nonrelevant documents. The proposed learning model is the core of a prototype information filtering system called ProFile
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