Name
Abstract | ||
|---|---|---|
This study has been prepared to set light on the performance difficulties encountered in large datasets on graph databases and to increase performance in Create, Read, Update, Delete (CRUD) operations with Approximate Membership Functions (AMF). For this purpose, modified Bloom filter has been proposed with scalable structure. Neo4j commercial graph database was used in experimental studies as a graph data model. In the experimental studies, it has been observed that the proposed method for all CRUD operations produces better results than the BTREE indexing method. The proposed modified Bloom filter method can be used for performance optimization in such databases. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IDAACS53288.2021.9661054 | 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) |
Keywords | DocType | Volume |
graph databases,scalable bloom filter,stream data | Conference | 2 |
ISSN | ISBN | Citations |
2770-4262 | 978-1-6654-2606-0 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alisettar Hüseynli | 1 | 0 | 0.34 |
| Muhammet Ali Akcayol | 2 | 0 | 0.34 |