Considering User Intention in Differential Graph Queries

Empty answers are a major problem by processing pattern matching queries in graph databases. Especially, there can be multiple reasons why a query failed. To support users in such situations, differential queries can be used that deliver missing parts of a graph query. Multiple heuristics are proposed for differential queries, which reduce the search space. Although they are successful in increasing the performance, they can discard query subgraphs relevant to a user. To address this issue, the authors extend the concept of differential queries and introduce top-k differential queries that calculate the ranking based on users’ preferences and significantly support the users’ understanding of query database management systems. A user assigns relevance weights to elements of a graph query that steer the search and are used for the ranking. In this paper the authors propose different strategies for selection of relevance weights and their propagation. As a result, the search is modelled along the most relevant paths. The authors evaluate their solution and both strategies on the DBpedia data graph

Methods and Tools for Visual Analytics.

Technological advances have led to a proliferation of data characterized by a complex structure; namely, high-dimensional attribute information complemented by relationships between the objects or even the attributes. Classical data mining techniques usually explore the attribute space, while network analytic techniques focus on the relationships, usually expressed in the form of a graph. However, visualization techniques offer the possibility to gain useful insight through appropriate graphical displays coupled with data mining and network analytic techniques. In this thesis, we study various topics of the visual analytic process. Specifically, in chapter 2, we propose a visual analytic algebra geared towards attributed graphs. The algebra defines a universal language for graph data manipulations during the visual analytic process and allows documentation and reproducibility. In chapter 3, we extend the algebra framework to address the uncertain querying problem. The algebra's operators are illustrated on a number of synthetic and real data sets, implemented in an existing visualization system (Cytoscape) and validated through a small user study. In chapter 4, we introduce a dimension reduction technique that through a regularization framework incorporates network information either on the objects or the attributes. The technique is illustrated on a number of real world applications. Finally, in the last part of the thesis, we present a multi-task generalized linear model that improves the learning of a single task (problem) by utilizing information from connected/similar tasks through a shared representation. We present an algorithm for estimating the parameters of the problem efficiently and illustrate it on a movie ratings data set.Ph.D.StatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/89683/1/zhouhao_1.pd

Querying Graphs with Uncertain Predicates

In many applications the available data give rise to an attributed graph, with the nodes corresponding to the entities of interest, edges to their relationships and attributes on both provide additional characteristics. To mine such data structures we have proposed a visual analytic algebra that enhances the atomic operators of selection, aggregation and a visualization step that allows the user to interact with the data. However, in many settings the user has a certain degree of uncertainty about the desired query; the problem is further compounded if the final results are the product of a series of such uncertain queries. To address this issue, we introduce a probabilistic framework that incorporates uncertainty in the queries and provides a probabilistic assessment of the likelihood of the obtained outcomes. We discuss its technical characteristics and illustrate it on a number of examples