HyGraph: Querying and Analytics on Hybrid Graphs

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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.