Bidirectional Transformation "bx" (Dagstuhl Seminar 11031)

Bidirectional transformations bx are a mechanism for maintaining the consistency of two (or more) related sources of information. Researchers from many different areas of computer science including databases (DB), graph transformations (GT), software engineering (SE), and programming languages (PL) are actively investigating the use of bx to solve a diverse set of problems. Although researchers have been actively working on bidirectional transformations in the above mentioned communities for many years already, there has been very little cross-discipline interaction and cooperation so far. The purpose of a first International Meeting on Bidirectional Transformations (GRACE-BX), held in December 2008 near Tokyo, was therefore to bring together international elites, promising young researchers, and leading practitioners to share problems, discuss solutions, and open a dialogue towards understanding the common underpinnings of bx in all these areas. While the GRACE-BX meeting provided a starting point for exchanging ideas in different communities and confirmed our believe that there is a considerable overlap of studied problems and developed solutions in the identified communities, the Dagstuhl Seminar 11031 on ``Bidirectional Transformations\u27\u27 also aimed at providing a place for working together to define a common vocabulary of terms and desirable properties of bidirectional transformations, develop a suite of benchmarks, solve some challenging problems, and launch joint efforts to form a living bx community of cooperating experts across the identified subdisciplines. This report documents the program and the outcomes of Dagstuhl Seminar 11031 with abstracts of tutorials, working groups, and presentations on specific research topics

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

An Integrated Alerting and Notification System Utilizing Stages of Automation and Uncertainty Visualization

While NextGen operations are still under development, several key issues have already emerged, including increased information demands on the flight deck. The ALerting And Reasoning Management System (ALARMS) was designed as a strategic, automated system for combining and evaluating alert-related outputs from current and proposed NextGen systems. The modeldriven interface integrates the status of the environment, pilot, and system to automatically present the most critical information at the right time, augmenting existing flight deck technologies. The current level of uncertainty in the environment and system as a whole is also evaluated and represented within the display. The four stage model of information processing presented by Parasuraman, Sheridan, & Wickens (2000) was used to guide the development of the underlying ALARMS automation. This document provides a brief overview of the ALARMS development process. Examples of the interface are included, and we discuss its implications

Using Graph Transformations and Graph Abstractions for Software Verification

In this abstract we present an overview of our intended approach for the verification of software written in imperative programming languages. This approach is based on model checking of graph transition systems (GTS), where each program state is modeled as a graph and the exploration engine is specified by graph transformation rules. We believe that graph transformation [13] is a very suitable technique to model the execution semantics of languages with dynamic memory allocation. Furthermore, such representation provides a clean setting to investigate the use of graph abstractions, which can mitigate the space state explosion problem that is inherent to model checking techniques