Name
Abstract | ||
|---|---|---|
Cloud-based data-intensive applications have to process high volumes of transactional and analytical requests on large-scale data. Businesses base their decisions on the results of analytical requests, creating a need for real-time analytical processing. We propose Janus, a hybrid scalable cloud datastore, which enables the efficient execution of diverse workloads by storing data in different repr... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TKDE.2017.2773607 | IEEE Transactions on Knowledge and Data Engineering |
Keywords | Field | DocType |
Pipelines,Real-time systems,Engines,Cloud computing,Servers,Distributed databases,Throughput | Data mining,Janus,Computer science,Online transaction processing,Distributed database,Online analytical processing,Distributed transaction,Change data capture,Cloud computing,Scalability,Distributed computing | Journal |
Volume | Issue | ISSN |
30 | 4 | 1041-4347 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vaibhav Arora | 1 | 65 | 5.55 |
| Faisal Nawab | 2 | 116 | 12.83 |
| Divyakant Agrawal | 3 | 8201 | 1674.75 |
| Amr El Abbadi | 4 | 6767 | 1569.95 |