Linguistic Summaries of Graph Databases in Customer Relationship Management (CRM)
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Keywords

graph data model
graph databases
linguistic summarization of graph datasets
customer relationship management
Neo4j
data mining
data
fuzzy representations of data

How to Cite

Bartczak, M., & Niewiadomski, A. (2019). Linguistic Summaries of Graph Databases in Customer Relationship Management (CRM). Journal of Applied Computer Science, 27(1), 7-26. https://doi.org/10.34658/jacs.2019.27.1

Abstract

The paper concentrates on data models that differ from the traditional relational one by Codd (1970). In particular, we are interested in processing graph databases (graph datasets) without any pre-configured structure, in which graph nodes may represent different objects and graph edges – relations between them. In this approach, the linguistic summarization methods for graph datasets are introduced, and differences for these methods with respect to traditional relational approach are shown, commented and improved in comparison to the preceding proposition (Strobin, Niewiadomski, 2016). The novelty of the paper is mostly the new form for summaries: Multi-Subject linguistic summaries of graph databases, previously introduced for relational databases (Superson, 2018).

https://doi.org/10.34658/jacs.2019.27.1
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References

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