Name
Abstract | ||
|---|---|---|
Voluminous, time-series data streams originating in continuous sensing environments pose data ingestion and processing challenges. We present a holistic methodology centered around data sketching to address both challenges. We introduce an order-preserving sketching algorithm that we have designed for space-efficient representation of multi-feature streams with native support for stream processing related operations. Observational streams are preprocessed at the edges of the network generating sketched streams to reduce data transfer costs and energy consumption. Ingested sketched streams are then processed using sketch-aware extensions to existing stream processing APIs delivering improved performance. Our benchmarks with real-world datasets show up to a ~8× reduction in data volumes transferred and a ~27× improvement in throughput. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TPDS.2021.3055265 | IEEE Transactions on Parallel and Distributed Systems |
Keywords | DocType | Volume |
Data sketches,stream processing systems,edge computing,Internet-of-Things | Journal | 32 |
Issue | ISSN | Citations |
8 | 1045-9219 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thilina Buddhika | 1 | 9 | 1.89 |
| Sangmi Lee Pallickara | 2 | 13 | 3.10 |
| Shrideep Pallickara | 3 | 837 | 92.72 |