Hybrid algorithms for subgraph pattern queries in graph databases

Numerous methods have been proposed over the years for subgraph query processing, as it is central to graph analytics. Existing work is fragmented into two major categories. Methods in the filter-then-verify (FTV) category first construct an index of the DB graphs. Given a query, the index is used to filter out graphs that cannot contain the query. On the remaining graphs, a subgraph isomorphism algorithm is applied to verify whether each graph indeed contains the query. A second category of algorithms is mainly concerned with optimizing the Subgraph Isomorphism (SI) testing process (an NP-Complete problem) in order to find all occurrences of the query within each DB graph, also known as the matching problem. The current research trend is to totally dismiss FTV methods, because SI methods have been shown to enjoy much shorter query execution times and because of the alleged high costs of managing the DB graph index in FTV methods. Thus, a number of new SI methods are being proposed annually. In the current work, we initially study the performance of the latest SI algorithms over datasets consisting of a large number of graphs. With our study, we evaluate the algorithms’ performance and we provide comparison details with former studies. As a second step, we combine the powerful filtering of a top-performing FTV method, with the various SI methods, which leads to the best practice conclusion that SI and FTV shouldn’t be thought of as disjoint types of solutions, as their union achieves better results than any one of them individually. Specifically, we experimentally analyze and quantify the (positive) impact of including the essence of indexed FTV methods within SI methods, showing that query processing times can be significantly improved at modest additional memory costs. We show that these results hold over a variety of well-known SI methods and across several real and synthetic datasets. As such, hybrids of the type reveal a missing opportunity and a blind spot in related literature and trends

Efficient algorithms for generalized subgraph query processing

We study a new type of graph queries, which injectively maps its edges to paths of the graphs in a given database, where the length of each path is constrained by a given threshold specified by the weight of the corresponding matching edge. We give important applications of the new graph query and identify new challenges of processing such a query. Then, we devise the cost model of the branch-and-bound algorithm framework for processing the graph query, and propose an efficient algorithm to minimize the cost overhead. We also develop three indexing techniques to efficiently answer the queries online. Finally, we verify the efficiency of our proposed indexes with extensive experiments on large real and synthetic datasets