 However, we find that a direct application of G-thinker is insufficient due to the drastically different running time of different tasks that violates the original design assumption of G-thinker , requiring a system URLe. We also observe that the running time of a task is highly unpredictable solely from the features extracted from its subgraph, leading to difficulty in pinpoint expensive tasks to decompose for concurrent processing, and size-threshold based partitioning under-partitions some tasks but over-partitions others, leading to bad load balancing and enormous task partitioning overheads. We address this issue by proposing a novel time-delayed divide-and-conquer strategy that strikes a balance between the workloads spent on actual mining and the cost of balancing the workloads.
<meaning-changed>  However, we find that a direct application of G-thinker is insufficient due to the drastically different running time of different tasks that violates the original design assumption of G-thinker , requiring a system URLe. We also observe that the running time of a task is highly unpredictable solely from the features extracted from its subgraph, leading to difficulty in pinpoint expensive tasks to decompose for concurrent processing, and size-threshold based partitioning under-partitions some tasks but over-partitions others, leading to bad load balancing and enormous task partitioning overheads. We address this issue by proposing a novel time-delayed divide-and-conquer strategy that strikes a balance between the workloads spent on actual mining and the cost of balancing the workloads.
 However, we find that a direct application of G-thinker is insufficient due to the drastically different running time of different tasks that violates the original design assumption of G-thinker 's execution engine to prioritize long-running tasks for mining, and by utilizing a novel timeout strategy to effectively decompose the mining workloads of long-running tasks to improve load balancing. While this system redesign applies to many other expensive dense subgraph mining problems, this paper verifies the idea by adapting the state-of-the-art quasi-clique algorithm, Quick, to our redesigned G-thinker. We improve Quick by integrating new pruning rules, and fixing some missed boundary cases that could lead to missed results.