Efficient and Effective Search over Graph-like Databases

世界上许多高质量的企业和社会数据都以半结构化和结构化数据的形式存储。这包括企业的rdbms、知识图和社会网络。所有这些数据集合要么已经定义为图形，要么可以重新建模为图形。在过去的十年里，我们见证了存储类图数据库的进步，但在搜索方面却没有太大的进步。正如微软研究院著名科学家苏拉吉特·乔杜里（Surajit Chaudhuri）在2015年ICDE的主题演讲中所说，对类图数据库的搜索已经落后于对非结构化数据的搜索。当科学家和商业用户从他们的异构数据集中寻找令人兴奋的、可操作的发现时，提供有效搜索的需求是深刻的。

Much of the world’s high-quality enterprise and social data are stored as semi-structured and structured data. This includes enterprises’ RDBMSs, knowledge graphs, and social networks. All these data collections either are defined already as graphs or can be re-modeled as graphs. Over the past decade, we have witnessed advances in storing graph-like databases, but we have not seen much progress in search over them. As Surajit Chaudhuri (a distinguished scientist at Microsoft Research) addressed in his keynote talk at ICDE in 2015, search over graph-like databases has fallen behind search over unstructured data. While scientists and business users look for exciting, actionable discoveries from their heterogeneous datasets, the need to provide effective search is profound.