Abstract
Performing online selective queries against graphs is a challenging problem due to the unbounded nature of graph queries which leads to poor computation locality. It becomes even difficult when a graph is too large to be fit in the memory. Although there have been emerging efforts on managing large graphs in a distributed and parallel setting, e.g., Pregel, HaLoop and etc, these computing frameworks are designed from the perspective of scalability instead of the query efficiency. In this work, we present our solution methodology for online selective graph queries based on the shortest path distance semantic, which finds various applications in practice. The essential intuition is to build a distance-aware index for online distance-based query processing and to eliminate redundant graph traversal as much as possible. We discuss how the solution can be applied to two types of research problems, distance join and vertex set bonding, which are distance-based graph pattern discovery and finding the structure-wise bonding of vertices, respectively.
Highlights
The tremendous size of real-world graph data raises series of challenges in efficient graph management and query processing
Performing online selective queries against graphs is a challenging problem due to the unbounded nature of graph queries which leads to poor computation locality
We present our solution methodology for online selective graph queries based on the shortest path distance semantic, which finds various applications in practice
Summary
The tremendous size of real-world graph data raises series of challenges in efficient graph management and query processing. With the generic vertex-centric model [22] applied to practices, there has been a huge advancement in large-scale graph analytic tasks, e.g. PageRank, SCC, subgraph listing and etc. More efficient generic graph processing frameworks, like the subgraph-centric model [33, 35], are emerging to accelerate the graph analytic task. Online graph query which finds various applications in real practice does not attract much research effort, especially the highly selective queries which has an limited output size. We study the problem of answering shortest path distance-based selective graph queries in a online fashion, such that ad hoc queries of such type can be answered promptly. To find out the shortest path(s) from vertex u to v, a naive BFS would access a large number of vertices in the graph to answer the query. If graph G is a social network, and the distance between u and v is five, a shortest path query evaluation much likely leads to an entire graph visit due to the six-separation law [13]
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