Visual Reasoning for Information Retrieval

当数据库变得越来越大时， 他知道数据库里有什么。用户也不知道 明确应该收回什么。 如何获取数据成为 这是大型数据库的核心问题。 本研究 通过数据可视化和可视化 推理 其主要思想是转换数据对象， 在视觉空间中呈现示例数据。 用户然后可以 在所述信息检索请求中递增地制定所述信息检索请求。 视觉空间 通过将数据可视化、可视化查询、 视觉例子和视觉线索，这项研究将产生更好的 制定和修改用户查询的方法。一个原型 系统已开发作为一个实验测试平台。 这项研究将开发一个框架，用于测试新的 应用于信息的视觉推理方法 在大型数据库中检索。 这也将导致更多 强大的标志性用户界面，并结合联合收割机以前的研究 导致空间推理，基于示例的编程， 从数据库中近似检索。

When the database grows larger and larger, the user no longer knows what is in the database. Nor does the user know clearly what should be retrieved. How to get at the data becomes a central problem for very large databases. This research addresses this problem through data visualization and visual reasoning. The main idea is to transform the data objects and present sample data in a visual space. The user can then incrementally formulate the information retrieval request in the visual space. By combining data visualization, visual query, visual examples, and visual cues, this research will yield better ways of formulating and modifying users' queries. A prototype system has been developed to serve as an experimental testbed. This research will develop a framework for testing new methodologies for visual reasoning as applied to information retrieval in very large databases. It will also lead to more powerful iconic user interfaces, and combine previous research results in spatial reasoning, example-based programming, and approximate retrieval from databases.