论文标题
部分可观测时空混沌系统的无模型预测
A Review of In-Memory Space-Efficient Data Structures for Temporal Graphs
论文作者
论文摘要
时间图随着时间的推移而建立实体之间的模型。最近的研究将时间图应用于抽象的复杂系统，例如社交网络参与者之间的连续沟通。通常，数据量大于主内存，因此，我们需要平衡空间使用量和查询效率的专业结构。在本文中，我们审查了从外部内存到主内存的大型时间图并加快专业查询的宽敞时间图。我们发现了使用数据压缩技术和自我指数压缩数据结构的各种研究。我们指出了进一步的研究指示，以改善当前的最新技术。
Temporal graphs model relationships among entities over time. Recent studies applied temporal graphs to abstract complex systems such as continuous communication among participants of social networks. Often, the amount of data is larger than main memory, therefore, we need specialized structures that balance space usage and query efficiency. In this paper, we review space-efficient data structures that bring large temporal graphs from external memory to primary memory and speed up specialized queries. We found a great variety of studies using data compression techniques and self-indexed compressed data structures. We point further research directions to improve the current state-of-the-art.
