CONTROL for Data-Intensive Processing

尽管多年来数据处理方面取得了显着进步，但许多数据密集型应用程序仍然以批处理模式运行：它们需要很长一段时间来执行，并且在此期间它们既不向用户提供反馈也不提供控制。这种状态存在于许多重要领域，从桌面（例如电子表格）到后台（例如决策支持）再到新的高端应用程序（例如数据挖掘和可视化工具）。在每个领域中，批处理对于认真的用户来说都是令人沮丧的，甚至是不可接受的。为了研究大型数据集，用户需要在线行为：他们需要在系统运行时获得有意义的反馈，并且他们需要能够通过在系统运行时控制系统来根据反馈采取行动。该研究项目的目标是探索和开发技术，使长时间运行的数据密集型操作具有在线功能。这包括对查询处理、用户界面、数据可视化和数据挖掘的算法和系统的新研究。这项工作的结果应该允许在数据分析和数据探索过程中用户和软件之间建立更紧密的循环。这可以显着增强用户的能力，并在数据挖掘过程中显着利用人类智能。 http://control.cs.berkeley.edu

Despite significant advances in data processing over the years, many data-intensive applications still run in batch mode: they require a long period of time to execute, and during that time they provide neither feedback nor control to the user. This state of affairs holds in a number of important domains, from the desktop (e.g., spreadsheets) to the back office (e.g., decision support) to new high-end applications (e.g., data mining and visualization tools). In each of these domains, batch processing is frustrating or even unacceptable for serious users. In order to investigate large data sets, users require on-line behavior: they need to get meaningful feedback while the system runs, and they need to be able to act on the feedback by controlling the system while it runs. The goal of this research project is to explore and develop techniques to make long-running, data-intensive operations have on-line functionality. This includes new research into algorithms and systems for query processing, user interfaces, data visualization, and data mining. The results of this work should allow for a much tighter loop between users and software during the process of data analysis and data exploration. This can lead to significantly enhanced power for users, and enable significant leverage of human intelligence in the data mining process. http://control.cs.berkeley.edu