Toward continuous pattern detection over evolving large graph with snapshot isolation

Abstract This paper studies continuous pattern detection over large evolving graphs, which plays an important role in monitoring-related applications. The problem is challenging due to the large size and dynamic updates of graphs, the massive search space of pattern detection and inconsistent query...
Ausführliche Beschreibung

Abstract This paper studies continuous pattern detection over large evolving graphs, which plays an important role in monitoring-related applications. The problem is challenging due to the large size and dynamic updates of graphs, the massive search space of pattern detection and inconsistent query results on dynamic graphs. This paper first introduces a snapshot isolation requirement, which ensures that the query results come from a consistent graph snapshot instead of a mixture of partial evolving graphs. Second, we propose an SSD (single sink directed acyclic graph) plan friendly to vertex-centric-distributed graph processing frameworks. SSD plan can guide the message transformation and transfer among graph vertices, and determine the satisfaction of the pattern on graph vertices for the sink vertex. Third, we devise strategies for major steps in the SSD evaluation, including the location of valid messages to achieve snapshot isolation, AO-List to determine the satisfaction of transition rule over dynamic graph, and message-on-change policy to reduce outgoing messages. The experiments on billion-edge graphs using Giraph, an open source implementation of Pregel, illustrate the efficiency and effectiveness of our method. Ausführliche Beschreibung