ACM Computing Surveys (CSUR)
CarTel: a distributed mobile sensor computing system
Proceedings of the 4th international conference on Embedded networked sensor systems
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Dryad: distributed data-parallel programs from sequential building blocks
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
The pothole patrol: using a mobile sensor network for road surface monitoring
Proceedings of the 6th international conference on Mobile systems, applications, and services
NSDI'10 Proceedings of the 7th USENIX conference on Networked systems design and implementation
OSDI'08 Proceedings of the 8th USENIX conference on Operating systems design and implementation
Large-scale incremental processing using distributed transactions and notifications
OSDI'10 Proceedings of the 9th USENIX conference on Operating systems design and implementation
S4: Distributed Stream Computing Platform
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Incoop: MapReduce for incremental computations
Proceedings of the 2nd ACM Symposium on Cloud Computing
Conflict-free replicated data types
SSS'11 Proceedings of the 13th international conference on Stabilization, safety, and security of distributed systems
Scalable and Low-Latency Data Processing with Stream MapReduce
CLOUDCOM '11 Proceedings of the 2011 IEEE Third International Conference on Cloud Computing Technology and Science
Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing
NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
IEEE Communications Magazine
| Hi-index | 0.00 |
Information has become a key commodity for most service providers. Analyzing streams of data efficiently, in real time, has become increasingly more important for supporting new products and applications. This paper outlines a novel abstraction for performing incremental stream processing based on Computational Conflict-free Replicated Data Types. C-CRDTs are replicated objects that can be updated concurrently without coordination to perform a computation and still converge to a consistent state that reflects all contributions. Results obtained with a preliminary prototype show that C-CRDTs have the potential to match and improve computational throughput when compared with a state of the art stream processing system.