The expansion of data has prompted the creation of various NoSQL (Not only SQL) databases, including graph -orienteddatabases, which provide an understandable abstraction for modeling complex domains and managing hig...
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The expansion of data has prompted the creation of various NoSQL (Not only SQL) databases, including graph -orienteddatabases, which provide an understandable abstraction for modeling complex domains and managing highly connected data. However, to add graph data to existing decision sup-port systems, new data warehouse systems that consider the special characteristics of graphs need to be developed. This work proposes a novel method for creating a data warehouse under a graph database and demonstrates how OLAP (Online Analytical Processing) structures created for reporting can be handled by graphdatabases. Additionally, the paper suggests using aggregation algorithms based association rules techniques to improve the efficiency of reporting and data analysis within a graph-based data warehouse. Finally, we provide a Cypher language implementation of the suggested approach to evaluate and validate our approach.
NoSQL systems are based on a "schemaless" approach that not does require schema specification before writing data, allowing a wide variety of representations. This flexibility leads to a large volume of hete...
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ISBN:
(数字)9783319985398
ISBN:
(纸本)9783319985398;9783319985381
NoSQL systems are based on a "schemaless" approach that not does require schema specification before writing data, allowing a wide variety of representations. This flexibility leads to a large volume of heterogeneous data, which makes their querying more complex for users who are compelled to know the different forms (i.e. the different schemas) of these data. This paper addresses this issue focusing on simplifying the heterogeneous data querying. Our work specially concerns graph-oriented NoSQL systems.
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