Structural matching by discrete relaxation

This paper describes a Bayesian framework for performing relational graph matching by discrete relaxation. Our basic aim is to draw on this framework to provide a comparative evaluation of a number of contrasting approaches to relational matching. Broadly speaking there are two main aspects to this study. Firstly we locus on the issue of how relational inexactness may be quantified. We illustrate that several popular relational distance measures can be recovered as specific limiting cases of the Bayesian consistency measure. The second aspect of our comparison concerns the way in which structural inexactness is controlled. We investigate three different realizations ai the matching process which draw on contrasting control models. The main conclusion of our study is that the active process of graph-editing outperforms the alternatives in terms of its ability to effectively control a large population of contaminating clutter

A Formal Definition for Configuration

There exists a wide set of techniques to perform keyword-based search over relational databases but all of them match the keywords in the users' queries to elements of the databases to be queried as first step. The matching process is a time-consuming and complex task. So, improving the performance of this task is a key issue to improve the keyword based search on relational data sources.In this work, we show how to model the matching process on keyword-based search on relational databases by means of the symmetric group. Besides, how this approach reduces the search space is explained in detail

Bayesian graph edit distance

This paper describes a novel framework for comparing and matching corrupted relational graphs. The paper develops the idea of edit-distance originally introduced for graph-matching by Sanfeliu and Fu [1]. We show how the Levenshtein distance can be used to model the probability distribution for structural errors in the graph-matching problem. This probability distribution is used to locate matches using MAP label updates. We compare the resulting graph-matching algorithm with that recently reported by Wilson and Hancock. The use of edit-distance offers an elegant alternative to the exhaustive compilation of label dictionaries. Moreover, the method is polynomial rather than exponential in its worst-case complexity. We support our approach with an experimental study on synthetic data and illustrate its effectiveness on an uncalibrated stereo correspondence problem. This demonstrates experimentally that the gain in efficiency is not at the expense of quality of match

Harvesting Application Information for Industry-Scale Relational Schema Matching

Consider the problem of migrating a company's CRM or ERP database from one application to another, or integrating two such databases as a result of a merger. This problem requires matching two large relational schemas with hundreds and sometimes thousands of fields. Further, the correct match is likely complex: rather than a simple one-to-one alignment, some fields in the source database may map to multiple fields in the target database, and others may have no equivalent fields in the target database. Despite major advances in schema matching, fully automated solutions to large relational schema matching problems are still elusive. This paper focuses on improving the accuracy of automated large relational schema matching. Our key insight is the observation that modern database applications have a rich user interface that typically exhibits more consistency across applications than the underlying schemas. We associate UI widgets in the application with the underlying database fields on which they operate and demonstrate that this association delivers new information useful for matching large and complex relational schemas. Additionally, we show how to formalize the schema matching problem as a quadratic program, and solve it efficiently using standard optimization and machine learning techniques. We evaluate our approach on real-world CRM applications with hundreds of fields and show that it improves the accuracy by a factor of 2-4x

Amygdala activity to angry and fearful faces relates to bullying and victimization in adolescents.

Relational bullying and victimization are common social experiences during adolescence, but relatively little functional magnetic resonance imaging (fMRI) research has examined the neural correlates of bullying and victimization in adolescents. The aim of the present study was to address this gap by examining the association between amygdala activity to angry and fearful faces and peer relational bullying and victimization in a community-based sample of adolescents. Participants included 49 adolescents, 12-15 years old, who underwent fMRI scanning while completing an emotional face matching task. Results indicated that interactions between amygdala activity to angry and fearful faces predicted self-reported relational bullying and victimization. Specifically, a combination of higher amygdala activity to angry faces and lower amygdala activity to fearful faces predicted more bullying behavior, whereas a combination of lower amygdala activity to angry faces and lower amygdala activity to fearful faces predicted less relational victimization. Exploratory whole-brain analyses also suggested that increased rostral anterior cingulate cortex activity to fearful faces was associated with less bullying. These results suggest that relational bullying and victimization are related to different patterns of neural activity to angry and fearful faces, which may help in understanding how patterns of social information processing predict these experiences

Correspondence matching with modal clusters

The modal correspondence method of Shapiro and Brady aims to match point-sets by comparing the eigenvectors of a pairwise point proximity matrix. Although elegant by means of its matrix representation, the method is notoriously susceptible to differences in the relational structure of the point-sets under consideration. In this paper, we demonstrate how the method can be rendered robust to structural differences by adopting a hierarchical approach. To do this, we place the modal matching problem in a probabilistic setting in which the correspondences between pairwise clusters can be used to constrain the individual point correspondences. We demonstrate the utility of the method on a number of synthetic and real-world point-pattern matching problems

Socialism, Capitalism, and Transition - Coordination of Economic Relations and Output Performance

Contacts and the way they are organized in different economic systemsmatter for the economy. In this paper we introduce the notion of Relational Capital to model contacts. Contacts are an input into sold output in our macro model based on matching theory (Pissarides, 1990). We argue that the destruction of some contacts in search for better ones is an integral part of technological advancement. This destruction carries a negative externality on former business partners. Socialist economies restricted such creative destruction, which we argue lead to their increasing technological backwardness. This is our explanation of the output fall during (unrestricted) transition: the technological catch-up implied high levels of destroyed and replaced relational capital. This not only had high direct opportunity costs (more labor is used for the production of relational capital) but also led to a loss in overall relational capital. The basic model is used to simulate plausible transition paths which appear compatible withmany stylized facts of the transition experience. Finally we discuss empirical observations as well as policy issues brought up in the literature.Transition, Economic Systems, Relations, Innovation