Over the past few years, there has been a rapid increase of data originating from evolving networks such as social networks, sensor networks and others. A major challenge that arises when handling such networks and their respective graphs is the ability to issue a historical query on their data, that is, a query that is concerned with the state of the graph at previous time instances. While there has been a number of works that index the historical data in a time-centric manner (i.e. according to the time instance an update event occurs), in this work, we focus on the less-explored vertex-centric storage approach (i.e. according to the entity in which an update event occurs). We demonstrate that the design choices for a vertex-centric model are not trivial, by proposing two different modelling and storage models that leverage NoSQL technology and investigating their tradeoffs. More specifically, we experimentally evaluate the two models and show that under certain cases, their relative performance can differ by several times. Finally, we provide evidence that simple baseline and non-NoSQL solutions are slower by up to an order of magnitude.

中文翻译：

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Hinode：实施以顶点为中心的建模方法来维护历史图数据

在过去的几年中，来自社交网络、传感器网络等不断发展的网络的数据迅速增加。处理此类网络及其各自的图时出现的主要挑战是能够对其数据发出历史查询，即与先前时间实例中的图状态有关的查询。虽然已经有许多工作以时间为中心的方式（即根据更新事件发生的时间实例）索引历史数据，但在这项工作中，我们专注于较少探索的以顶点为中心的存储方法（即根据发生更新事件的实体）。我们证明了以顶点为中心的模型的设计选择并非微不足道，通过提出两种不同的建模和存储模型来利用 NoSQL 技术并研究它们的权衡。更具体地说，我们对这两个模型进行了实验评估，并表明在某些情况下，它们的相对性能可能相差数倍。最后，我们提供的证据表明，简单的基线和非 NoSQL 解决方案的速度慢了一个数量级。