Abstract
The importance of bringing the relational data to other models and technologies has been widely debated. In special, Graph Database Management Systems (DBMS) have gained attention from industry and academia for their analytic potential. One of its advantages is to incorporate facilities to perform topological analysis, such as link prediction, centrality measures analysis, and recommendations. There are already initiatives to map from a relational database to graph representation. However, they do not take into account the different ways to generate such graphs. This work discusses how graph modeling alternatives from data stored in relational datasets may lead to useful results. The main contribution of this paper is towards managing such alternatives, taking into account that the graph model choice and the topological analysis to be used by the user. Experiments are reported and show interesting results, including modeling heuristics to guide the user on the graph model choice.
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Acknowledgments
This work was developed by the first author as part of the requirement for his Master’s Degree. The authors would like to thank CAPES, the Brazilian Governmental Agency, for the scholarship granted to the post-graduate student.
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Filho, S.P.L., Cavalcanti, M.C., Justel, C.M. (2019). Graph Modeling for Topological Data Analysis. In: Hammoudi, S., Śmiałek, M., Camp, O., Filipe, J. (eds) Enterprise Information Systems. ICEIS 2018. Lecture Notes in Business Information Processing, vol 363. Springer, Cham. https://doi.org/10.1007/978-3-030-26169-6_10
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