Data exploration and visual analytics systems are of great importance in Open Science scenarios, where less tech-savvy researchers wish to access and visually explore big raw data files (e.g., json, csv) generated by scientific experiments using commodity hardware and without being overwhelmed in the tedious processes of data loading, indexing and query optimization. In this paper, we present our work for enabling efficient query processing on large raw data files for interactive visual exploration scenarios and analytics. We introduce a framework, named RawVis, built on top of a lightweight in-memory tile-based index, VALINOR, that is constructed on-the-fly given the first user query over a raw file and progressively adapted based on the user interaction. We evaluate the performance of a prototype implementation compared to three other alternatives and show that our method outperforms in terms of response time, disk accesses and memory consumption. Particularly during an exploration scenario, the proposed method in most cases is about 5-10$\times$ faster compared to existing solutions, and requires significantly less memory resources.

数据探索和可视化分析系统在开放科学场景中非常重要，在这种情况下，技术娴熟的研究人员希望使用商品硬件访问和可视化探索由科学实验生成的大型原始数据文件（例如json，csv），而不会被淹没繁琐的数据加载，索引编制和查询优化过程。在本文中，我们介绍了我们的工作，该工作可对大型原始数据文件进行有效的查询处理，以进行交互式可视化探索方案和分析。我们引入了一个名为RawVis的框架，该框架建立在基于轻量级基于内存的基于图块的索引VALINOR之上，该索引是在原始文件上进行首次用户查询时即时构建的，并根据用户交互进行逐步调整。与其他三个替代方案相比，我们评估了原型实现的性能，并表明我们的方法在响应时间，磁盘访问和内存消耗方面均优于其他方法。特别是在勘探场景中，大多数情况下建议的方法约为5-10$\times$ 与现有解决方案相比，速度更快，所需的内存资源也大大减少。