Name
Abstract | ||
|---|---|---|
The ability to process high-volume high-speed streaming data from different data sources is critical for modern organizations to gain insights for business decisions. In this research, we present the streaming analytics platform (SDAP), which provides a set of operators to specify the process of stream data transformations and analytics. SDAP adopts a declarative approach to model and design, delivering analytics capabilities through the combination of a set of primitive operators in a simple manner. The model includes a topology to design streaming analytics specifications using a set of atomic data manipulation operators. Our evaluation demonstrates that SDAP is capable of maintaining low-latency while scaling to a cloud of distributed computing nodes, and providing easier process design and execution of streaming analytics. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-62911-7_1 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Data stream processing,High-performance computing,Low-latency,Distributed systems | Supercomputer,Computer science,Process design,Operator (computer programming),Latency (engineering),Data manipulation language,Analytics,Database,Distributed computing,Scalability,Cloud computing | Conference |
Volume | ISSN | Citations |
737 | 1865-0929 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paolo Cappellari | 1 | 2 | 2.05 |
| Mark Roantree | 2 | 240 | 40.76 |
| Soon Ae Chun | 3 | 893 | 100.67 |