Elite: an elastic infrastructure for big spatiotemporal trajectories
Elite: an elastic infrastructure for big spatiotemporal trajectories
复制标题
Elite：大时空轨迹的弹性基础设施
DOI：
10.1007/s00778-016-0425-6
复制
发表时间：
2016
期刊：
影响因子：
4.2
通讯作者：
Jensen Christian S.
中科院分区：
文献类型：
--
作者：
Xie Xike;Mei Benjin;Chen Jinchuan;Du Xiaoyong;Jensen Christian S.
随着时空轨迹数据的数量继续快速增长，需要新一代数据管理技术，以便能够利用这些数据来提供一系列数据驱动的服务，包括地理类型的服务。时空数据带来的关键挑战包括海量数据量、高速捕获数据、需要交互响应时间以及数据固有的不准确性。我们提出了一个基础设施Elite，它利用点对点和并行计算技术来应对这些挑战。该基础设施通过将数据组织到逻辑上集中但物理上分布在计算节点之间的分层索引结构中来提供高效、并行的更新和查询处理。该基础架构在存储方面是灵活的，这意味着它可以适应存储卷中的波动，而在计算方面，这意味着并行度可以进行调整，以最好地匹配计算要求。此外，该基础设施还提供包括概率模拟在内的高级功能，用于解决查询处理中底层数据的不准确问题。广泛的经验研究提供了对基础设施的特性的洞察，并表明它满足其设计目标，从而能够有效地管理大时空数据。
As the volumes of spatiotemporal trajectory data continue to grow at a rapid pace; a new generation of data management techniques is needed in order to be able to utilize these data to provide a range of data-driven services, including geographic-type services. Key challenges posed by spatiotemporal data include the massive data volumes, the high velocity with which the data are captured, the need for interactive response times, and the inherent inaccuracy of the data. We propose an infrastructure, Elite, that leverages peer-to-peer and parallel computing techniques to address these challenges. The infrastructure offers efficient, parallel update and query processing by organizing the data into a layered index structure that is logically centralized, but physically distributed among computing nodes. The infrastructure is elastic with respect to storage, meaning that it adapts to fluctuations in the storage volume, and with respect to computation, meaning that the degree of parallelism can be adapted to best match the computational requirements. Further, the infrastructure offers advanced functionality, including probabilistic simulations, for contending with the inaccuracy of the underlying data in query processing. Extensive empirical studies offer insight into properties of the infrastructure and indicate that it meets its design goals, thus enabling the effective management of big spatiotemporal data.