Student Travel Fellowships to Attend the 2nd Workshop on Resource Discovery

第二次资源发现研讨会（RED）与第35届超大型数据库国际会议（VLDB）将于2009年8月28日在法国里昂举行。该项目为在美国大学注册的学生提供旅行奖学金，以便参加研讨会。研讨会关注的是模型和原型的设计和开发，这些模型和原型支持资源的表示、Web上资源的发现、资源的识别、本地化和组合。资源对应于信息源，如数据存储库或数据库管理系统（如查询表单或文本搜索引擎），资源之间的链接（索引或超链接），或服务，如应用程序或工具。资源由核心信息表征，包括名称、输入和输出的描述（参数或格式）、地址以及表示为元数据的各种附加属性。资源发现是识别和定位具有特定属性的现有资源的过程。基于机器的资源发现依赖于自动爬行、聚类和分类在Web上发现的资源。资源是根据元数据来组织的，元数据描述了它们的内容（对于数据源）、语义（就本体论类和关系而言）、特征（语法属性）、性能（使用度量和基准）、质量（管理、可靠性、信任）等。资源发现系统允许查询表达式来标识和定位实现特定任务的资源。欲了解更多信息，请参阅该项目的网页：http://bioinformatics.eas.asu.edu/RED/red2009.html

The second workshop on Resource Discovery (RED) is co-located withthe 35th International Conference on Very Large Data Bases (VLDB)on August 28, 2009, in Lyon, France. This project provides travel fellowships to students enrolled in US universities in order to attend the workshop.The workshop is concerned with the design and development of models and prototypes that support the representation of resources, the discovery of resources on the Web, the identification, localisation, and composition of resources. A resource corresponds to an information source such as a data repository or database management system (e.g., a query form or a textual search engine), a link between resources (an index or hyperlink), or a service such as an application or tool. Resources are characterized by core information including a name, a description of its input and its output (parameters or format), its address, and various additional properties expressed as metadata. Resource discovery is the process of identifying and locating existing resources that have a particular property. Machine-based resource discovery relies on crawling, clustering, and classifying resources discovered on the Web automatically. Resources are organized with respect to metadata that characterize their content (for data sources), their semantics (in terms of ontological classes and relationships), their characteristics (syntactical properties), their performance (with metrics and benchmarks), their quality (curation, reliability, trust), etc. Resource discovery systems allow the expression of queries to identify and locate resources that implement specific tasks. For further information see the project web page at: http://bioinformatics.eas.asu.edu/RED/red2009.html