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Multimodal path queries over transportation networks are receiving increasing attention due to their widespread applications. A multimodal path query consists of finding multimodal journeys from source to destination in transportation networks, including unrestricted walking, driving, cycling, and schedule-based public transportation. Transportation networks are generally continent-sized. This characteristic highlights the need for parallel computing to accelerate multimodal path queries. Meanwhile, transportation networks are often fragmented and distributively stored on different machines. This situation calls for exploiting parallel computing power for these distributed systems. Therefore, in this paper, we study distributed multimodal path (DMP) queries over large transportation networks. We develop algorithms to explore parallel computation. When evaluating a DMP query $Q$ Q on a distributed multimodal graph $Gmult$ G m u l t , we show that the algorithms possess the following performance guarantees, irrespective of how $Gmult$ G m u l t is fragmented and distributed: (1) each machine is visited only once; (2) the total network traffic is determined by the size of $Q$ Q and the fragmentation of $Gmult$ G m u l t ; (3) the response time is decided by the largest fragment of $Gmult$ G m u l t ; and (4) the algorithm is parallel scalable. Using real-life and synthetic data, we experimentally verify that the algorithms are scalable on large graphs. [ABSTRACT FROM AUTHOR]

Distributed graph systems are becoming extremely popular due to their flexibility, scalability, and robustness in big graph processing. In order to improve the performance of the distributed graph systems, caching is a very effective technique to achieve fast response and reduce the communication cost. Existing works include online and offline caching algorithms. Online caching algorithms (such as least recently used (LRU) and most recently used (MRU)) are lightweight and flexible, however, neglect the topological properties of big graphs. Offline caching algorithms (such as node pre-ordered) consider the graph topology, but are very expensive and heavy. In this paper, we propose a novel caching mechanism, GraphCache (GCache), for big distributed graphs. GCache consists of an offline phase and an online phase, which inherits the advantages of online and offline caching algorithms. Specifically, the offline phase provides a caching model based on the bipartite graph clustering and give efficient algorithms to solve it. The online phase caches and schedules the graph clusters output from the offline phase, based on the LRU and MRU strategies. GCache can be seamlessly integrated into the state-of-the-art graph processing systems, e.g., Giraph. Finally, our experimental results demonstrate the feasibility of our proposed caching techniques in speeding up graph algorithms over distributed big graphs. [ABSTRACT FROM AUTHOR]