EAGER: A Method to Retrieve Non-Textual Data from Widespread Repositories

随着数据丰富的科学的学术研究成果，非文本数据的增长，例如数字数据，继续扩大。这项工作通过一个原型探索是否可以找到易于使用、通用和低成本的大型非文本数据集中存储库的替代方案。其想法是能够像访问文档一样容易和容易地访问非文本信息，而不需要费力的额外工作。自动创建多个索引将提供一种便利索引和检索的机制，例如互联网搜索巨头为文本所做的工作。如果成功，它将很容易发布数字数据，并使其通过标准的网络搜索引擎获得，就像今天的文本信息一样容易。该团队将使用来自一系列学科的各种数据源和对象来构建一个系统，然后设计指定非文本数据(包括数字数据)特征的表示法。下一步，他们将开发增强的索引功能，以便在扩展的搜索引擎中处理查询，并按每种类型对数据进行注释。在为开发人员和试点用户开发了查询和交互界面后，他们将测试该方法的可行性以及为各种类型的数据创建描述性添加的有效性。

As the results of scholarly research in data-rich sciences, the growth of non-textual data, for example, numerical data, continues to expand. This work explores through a prototype whether alternatives can be found to large centralized repositories of non-textual data that are easy to use, universal and low cost. The idea is to be able to access non-textual information as easily and readily as documents without laborious additional work. The automatic creation of a number of indices would provide a mechanism to facilitate indexing and retrieval, such as done by internet search giants for text. If successful, it will be easy to post numerical data and and have it be available via standard web search engines as easily as textual information is today. The team will build a system using a wide variety of data sources and objects from a range of disciplines, and then design representations that specify the characteristics of non-textual data, including numerical data. Next they will develop enhanced indexing capabilities to handle queries in an extended search engine and procures to annotate the data by each type. After developing a query and interaction interface for developers and pilot users, they will test the viability of the approach and the effectiveness for creating descriptive additions for various types of data.