HyGraph: Querying and Analytics on Hybrid Graphs

图是一种简单但具有高度表现力的数据结构，用于建模和分析现实世界对象之间的关系。由于图的结构和内容不断变化，例如在社交网络或运输和移动网络中，需要新的数据模型和分析机制。尤其是这种时态图与时间序列数据的融合以及图流的高频更新是一个重要的挑战，到目前为止，这只是通过不同的模型和分析系统来部分实现的。我们的目标是开发HyGraph，这是一种新型的混合数据模型，它将时态图和时间序列无缝地结合在一起，并通过图流实现高频更新。这种在统一混合模型中的组合为新的前所未有的查询、分析、数据挖掘和机器学习任务铺平了道路。通过规划算子的概念，可以在混合图上执行查询和分析，以及通过连接算子实现的强大的数据挖掘算法，如频繁模式挖掘或聚类。整个系统将被原型实现，其适用性将在至少一个用例中进行演示。

Graphs are simple yet highly expressive data structures for modeling and analyzing relationships between real-world objects. As the structure and content of graphs is continuously changing, e.g. in social networks or transport and mobility networks, novel data models and analysis mechanisms are needed. Especially the fusion of such temporal graphs with time series data as well as the high-frequency updating by graph streams is an important challenge, which so far has only been partially enabled by means of distinct models and analytical systems. Our goal is to develop HyGraph, a novel hybrid data model that seamlessly combines temporal graphs with time series and enables high-frequency updates through graph streams. This combination in a unified hybridmodel paves the way to novel unprecedented query, analysis, data mining and machine learning tasks. By means of a planned operator concept, both queries and analyses can be executed on the hybrid graphs, as well as powerful data mining algorithms such as frequent pattern mining or clustering, which are enabled by the concatenation of operators. The overall system will beprototypically implemented and its applicability will be demonstrated for at least one use case.