Managing and querying heterogenous and evolving data

近年来，现代信息系统已经从根本上得到了发展。与传统的数据仓库的客户机-服务器体系结构相反，现代信息系统由大量分布式数据源组成，所有数据源都通过Internet连接。 除了分布式之外，数据源也在不断发展，它们的模式经常异步更新。 今天的信息系统也是异构的。 除了传统的数据库之外，还出现了新的数据来源。 它们包括实时流数据、数据服务、传感器和射频识别（RFID）读数。 建议的研究计划是在异步模式更新的存在下，弹性用户应用程序的开发技术。 我们将开发技术，让用户应用程序和查询，以自动适应不断变化的模式的异构数据集。 这些技术还将使使命关键应用程序能够以最小的限制限制远程数据库的发展，以确保正确和不间断的数据访问。 所提出的技术将考虑到新的数据源，如Web服务，数据流和传感器/RFID数据的独特特性。 该计划还将研究分布式信息系统中异构数据的自主存储和交换问题。 我们的目标是为非结构化或半结构化数据集或数据流设计高效的存储和交换布局。 通过分析要存储或交换的数据，我们将开发算法来自动设计有效利用可用数据库系统和网络资源的存储和交换布局。 除了静态数据集，我们特别感兴趣的是不断变化的数据集和数据流的数据存储和交换问题。

In recent years, modern information systems have evolved in fundamental ways. As opposed to the traditional client-server architecture of data warehouses, modern informations systems consists of large volumes of distributed data sources, all connected by the Internet.  Aside from being distributed, the data sources are also evolving, with their schemas updated frequently and asynchronously.  Today's information systems are also heterogeneous.  Beyond traditional databases, there is an emergence of novel sources of data.  They include real-time streamed data, data services, sensor and radio frequency identification (RFID) readings.  The proposed research program is on developing techniques for resilient user applications in the presence of asynchronous schema updates.  We will develop techniques to allow user applications and queries to automatically adapt to evolving schema of heterogeneous data sets.  These techniques will also enable mission critical applications to limit the evolution of remote databases with minimal restriction to ensure correct and uninterrupted data access.  The proposed techniques will take into account the unique characteristics of novel data sources such as web services, data streams, and sensor/RFID data.  The proposed program will also study the problem of autonomic storage and exchange of heterogeneous data in distributed information systems.  Our objective is to design efficient layouts for storage and exchangefor unstructured or semi-structured data sets or data streams.  By analyzing the data to be stored or exchanged, we will develop algorithms to automatically design the storage and exchange layout that efficiently make use of available database systems and network resources.  Aside from static data sets, we are particularly interested at data storage and exchange problem for evolving data sets and data streams.