Name
Abstract | ||
|---|---|---|
In this paper, a big data analytic framework is introduced for processing high-frequency data stream. This framework architecture is developed by combining an advanced evolving learning algorithm namely Parsimonious Network Fuzzy Inference System (PANFIS) with MapReduce parallel computation, where PANFIS has the capability of processing data stream in large volume. Big datasets are learnt chunk by chunk by processors in MapReduce environment and the results are fused by rule merging method, that reduces the complexity of the rules. The performance measurement has been conducted, and the results are showing that the MapReduce framework along with PANFIS evolving system helps to reduce the processing time around 22 percent in average in comparison with the PANFIS algorithm without reducing performance in accuracy. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.procs.2018.10.514 | Procedia Computer Science |
Keywords | Field | DocType |
Big data stream analytic,Distributed evolving algorithm,Scalable real-time data mining,Parallel learning,Rule merging strategy | Evolving systems,Data mining,Architecture,Data stream,Computer science,Performance measurement,Artificial intelligence,Merge (version control),Big data,Machine learning,Fuzzy inference system | Conference |
Volume | ISSN | Citations |
144 | 1877-0509 | 0 |
PageRank | References | Authors |
0.34 | 15 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Choiru Za'in | 1 | 7 | 1.79 |
| Mahardhika Pratama | 2 | 702 | 50.02 |
| Edwin Lughofer | 3 | 1940 | 99.72 |
| Md Meftahul Ferdaus | 4 | 19 | 4.63 |
| qing cai | 5 | 60 | 8.64 |
| Mukesh Prasad | 6 | 166 | 26.33 |