Computing the shortest path distance between two vertices in a road network is a building block in numerous applications. To do so efficiently, the state-of-the-art proposals adopt a tree decomposition process with heuristic strategies to build 2-hop label indexes. However, these indexes suffer from large space overheads caused by either tree imbalance or a large tree height. Independently of this, reinforcement learning has recently show impressive performance at sequential decision making in spatial data management tasks. We observe that tree decomposition is naturally a sequential decision making problem that decides which vertex to process at each step. In this paper, we propose a reinforcement learning based tree decomposition (RLTD) approach that reduces the space overhead significantly. We model tree decomposition as a Markov Decision Process, exploiting features of both the network topological structure and the tree structure. We further optimize the tree decomposition process by taking the network density into account, which yields a great generalization of the model on large road networks. Extensive experiments with real-world data offer insights into the performance of the proposals, showing that they are able to reduce the space overhead by about 51% and achieve on average about 14% speedup for queries with almost the same preprocessing time when compared with the state-of-the-art proposals.

Growing demands for the efficient processing of extreme-scale time series workloads call for more capable time series database management systems (TSDBMS). Specifically, to maintain consistency and durability of transaction processing, systems employ write-ahead logging (WAL) whereby transactions are committed only after the related log entries are flushed to disk. However, when faced with massive I/O, this becomes a throughput bottleneck. Recent advances in byte-addressable Non-Volatile Memory (NVM) provide opportunities to improve logging performance by persisting logs to NVM instead. Existing studies typically track complex transaction dependencies and use barrier instructions of NVM to ensure log ordering. In contrast, few studies consider the heavy-tailed characteristics of time series workloads, where most transactions are independent of each other. We propose DecLog, a decentralized NVM-based logging system that enables concurrent logging of TSDBMS transactions. Specifically, we propose data-driven log sequence numbering and relaxed ordering strategies to track transaction dependencies and resolve serialization issues. We also propose a parallel logging method to persist logs to NVM after being compressed and aligned. An experimental study on the YCSB-TS benchmark offers insight into the performance properties of DecLog, showing that it improves throughput by up to 4.6× while offering lower recovery time in comparison to the open source TSDBMS Beringei.

Computing shortest-path distances in road networks is core functionality in a range of applications. To enable the efficient computation of such distance queries, existing proposals frequently apply 2-hop labeling that constructs a label for each vertex and enables the computation of a query by performing only a linear scan of labels. However, few proposals take into account the spatio-temporal characteristics of query workloads. We observe that real-world workloads exhibit (1) spatial skew, meaning that only a small subset of vertices are queried frequently, and (2) temporal locality, meaning that adjacent time intervals have similar query distributions. We propose a Workload-aware Core-Forest label index (WCF) to exploit spatial skew in workloads. In addition, we develop a Reinforcement Learning based Time Interval Partitioning (RL-TIP) algorithm that exploits temporal locality to partition workloads to achieve further performance improvements. Extensive experiments with real-world data offer insights into the performance of the proposals, showing that they achieve 62% speedup on average for query processing with less preprocessing time and space overhead when compared with the state-of-the-art proposals.