Extracting intelligence from an interconnected world of data

科学实验、测量设备、商业信息系统、社交网络、从非结构化文本中提取事实的应用程序等以指数级速度生成数据，当务之急是理解这些数据，探索和链接它们，从中提取智能，并将它们与其他来源的数据集成。 今天处理数据的常见困难是应用程序之间存在许多不同的表示，这些应用程序需要共享、交换和链接数据。图结构数据为实现灵活性和无缝演进提供了一个优雅的解决方案。尽管如此，来自不同的应用程序和数据源，图结构数据通常是异构的，不精确的，不确定的，和/或不完整的。 我们如何查询和导航异构的图结构数据？当存储的数据不精确和/或不确定时，我们如何理解这些查询的结果？在不完全信息条件下，我们如何对不确定数据进行推理？这些和其他相关问题将是我们在这项研究中的主要焦点。 这项研究的新奇在于提出了在上述现实生活假设下管理图结构数据的新的强大机制。我们的研究致力于提供充分利用这些数据的方法和技术，并有可能在科学，工程和商业的许多不同领域的众多数据分析活动中产生巨大影响。

Data is generated at an exponential rate from scientific experiments, measurement devices, business information systems, social networks, applications extracting facts from unstructured text, and so on. The imperative is to make sense of these data, to explore and link them, to extract intelligence from them, and to integrate them with data from other sources. The common difficulty today in handling data is the existence of many different representations among applications, which need to share, exchange, and link data. Graph-structured data offers an elegant solution for achieving flexibility and seamless evolution. Still, coming from different applications and data-sources, graph-structured data is typically heterogeneous, imprecise, uncertain, and/or incomplete. How can we query and navigate heterogeneous graph-structured data? How can we make sense of the results of these queries when the data stored is imprecise and/or uncertain? How can we reason about uncertain data under conditions of incomplete information? These and other related questions will be our main focus in this research. The novelty of this research is in proposing new powerful mechanisms for managing graph-structured data under the above real-life assumptions. Our research strives to provide methods and techniques to make full use of such data and has the potential for great impact in a multitude of data analysis activities in many diverse areas of science, engineering, and business.