Subgraph Pattern Matching over Uncertain Graphs with Identity Linkage Uncertainty

There is a growing need for methods which can capture uncertainties and answer queries over graph-structured data. Two common types of uncertainty are uncertainty over the attribute values of nodes and uncertainty over the existence of edges. In this paper, we combine those with identity uncertainty. Identity uncertainty represents uncertainty over the mapping from objects mentioned in the data, or references, to the underlying real-world entities. We propose the notion of a probabilistic entity graph (PEG), a probabilistic graph model that defines a distribution over possible graphs at the entity level. The model takes into account node attribute uncertainty, edge existence uncertainty, and identity uncertainty, and thus enables us to systematically reason about all three types of uncertainties in a uniform manner. We introduce a general framework for constructing a PEG given uncertain data at the reference level and develop highly efficient algorithms to answer subgraph pattern matching queries in this setting. Our algorithms are based on two novel ideas: context-aware path indexing and reduction by join-candidates, which drastically reduce the query search space. A comprehensive experimental evaluation shows that our approach outperforms baseline implementations by orders of magnitude

Dura

The reactive event processing language, that is developed in the context of this project, has been called DEAL in previous documents. When we chose this name for our language it has not been used by other authors working in the same research area (complex event processing). However, in the meantime it appears in publications of other authors and because we have not used the name in publications yet we cannot claim that we were the first to use it. In order to avoid ambiguities and name conflicts in future publications we decided to rename our language to Dura which stands for “Declarative uniform reactive event processing language”. Therefore the title of this deliverable has been updated to “Dura – Concepts and Examples”

Computing Multi-Relational Sufficient Statistics for Large Databases

Databases contain information about which relationships do and do not hold among entities. To make this information accessible for statistical analysis requires computing sufficient statistics that combine information from different database tables. Such statistics may involve any number of {\em positive and negative} relationships. With a naive enumeration approach, computing sufficient statistics for negative relationships is feasible only for small databases. We solve this problem with a new dynamic programming algorithm that performs a virtual join, where the requisite counts are computed without materializing join tables. Contingency table algebra is a new extension of relational algebra, that facilitates the efficient implementation of this M\"obius virtual join operation. The M\"obius Join scales to large datasets (over 1M tuples) with complex schemas. Empirical evaluation with seven benchmark datasets showed that information about the presence and absence of links can be exploited in feature selection, association rule mining, and Bayesian network learning.Comment: 11pages, 8 figures, 8 tables, CIKM'14,November 3--7, 2014, Shanghai, Chin