HyGraph: Querying and Analytics on Hybrid Graphs

HyGraph：混合图的查询和分析

• 批准号: 505597817
• 负责人: Professor Dr.-Ing. Erhard Rahm
• 金额: --
• 依托单位: Institut für Informatik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/505597817?language=en
• 关键词: HyGraph; Querying; Analytics; Hybrid; Graphs

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.

图是用于建模和分析现实世界对象之间关系的简单而富有表现力的数据结构。由于图的结构和内容是不断变化的，例如在社交网络或交通和移动网络中，需要新的数据模型和分析机制。特别是这种时间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，通过不同的模型和分析系统只能部分实现。我们的目标是开发HyGraph，这是一种新型的混合数据模型，可以无缝地将时间图与时间序列结合起来，并通过图流实现高频更新。这种统一混合模型的组合为前所未有的查询、分析、数据挖掘和机器学习任务铺平了道路。通过计划操作符概念，可以在混合图上执行查询和分析，以及强大的数据挖掘算法，如频繁模式挖掘或聚类，这些都是通过操作符的连接来实现的。整个系统将被原型化地实现，并且它的适用性将至少在一个用例中得到证明。