The past few years have seen a tremendous increase in often irregularly structured data that can be represented most naturally and efficiently in the form of graphs. Making sense of incessantly growing graphs is not only a key requirement in applications like social media analysis or fraud detection but also a necessity in many traditional enterprise scenarios. Thus, a flexible approach for multidimensional analysis of graph data is needed. Whereas many existing technologies require up-front modelling of analytical scenarios and are difficult to adapt to changes, our approach allows for ad-hoc analytical queries of graph data. Extending our previous work on graph summarization, in this position paper we lay the foundation for large graph analytics to enable business intelligence on graph-structured data.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:83288 |
| Date | 02 February 2023 |
| Creators | Rudolf, Michael, Voigt, Hannes, Bornhövd, Christof, Lehner, Wolfgang |
| Publisher | Springer |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/acceptedVersion, doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 978-3-662-46838-8, 978-3-662-46839-5, 10.1007/978-3-662-46839-5_11 |
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