基于广义判别网络的演绎图数据库增量式视图维护
Incremental View Maintenance for Deductive Graph Databases Using Generalized Discrimination Networks
摘要：
现在，当实体之间的关系在数据库查询的范围内时，采用图数据库以避免关系数据库的性能关键的连接操作。图查询用于查询和修改存储在图数据库中的图。图查询采用图模式匹配，其对于子图同构是NP完全的。可以采用图数据库视图，其在针对经常陈述的和复杂的图查询的预先计算的图模式匹配方面保持准备好的答案，以提高查询性能。然而，这样的图形数据库视图必须与存储在图形数据库中的图形保持一致。在本文中，我们描述了如何使用增量图模式匹配技术来维护图形数据库视图。我们提出了一个增量维护算法的图数据库视图，它适用于命令式和声明式指定的图查询。评估表明，我们的维护算法规模时，存储在图数据库中的节点和边的数量增加。此外，我们的评估表明，我们的方法可以优于现有的方法的增量维护图查询结果。
英文摘要：
Nowadays, graph databases are employed when relationships between entities are in the scope of database queries to avoid performance-critical join operations of relational databases. Graph queries are used to query and modify graphs stored in graph databases. Graph queries employ graph pattern matching that is NP-complete for subgraph isomorphism. Graph database views can be employed that keep ready answers in terms of precalculated graph pattern matches for often stated and complex graph queries to increase query performance. However, such graph database views must be kept consistent with the graphs stored in the graph database. In this paper, we describe how to use incremental graph pattern matching as technique for maintaining graph database views. We present an incremental maintenance algorithm for graph database views, which works for imperatively and declaratively specified graph queries. The evaluation shows that our maintenance algorithm scales when the number of nodes and edges stored in the graph database increases. Furthermore, our evaluation shows that our approach can outperform existing approaches for the incremental maintenance of graph query results.
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