IoT Data Processing in the Fog: Functions, Streams, or Batch Processing?
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- Type
- article
- Published
- 2019-06-01
- Cited by
- 39
- References
- 36
- OpenAlex
- https://openalex.org/W2971894545
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:201833119
Keywords
Stream processing, Computer science, Data processing, Cloud computing, Batch processing
References
- Real time analysis of sensor data for the Internet of Things by means of clustering and event processing
- The Cloud is Not Enough: Saving IoT from the Cloud
- Data Management for the Internet of Things: Design Primitives and Solution
- IoT Security & Privacy: Threats and Challenges
- Key ingredients in an IoT recipe: Fog Computing, Cloud computing, and more Fog Computing
- Reliability in the utility computing era: Towards reliable Fog computing
- Brewer's conjecture and the feasibility of consistent, available, partition-tolerant web services
- Data management for the Internet of Things: Green directions
- MapReduce: simplified data processing on large clusters
- Lambda architecture for cost-effective batch and speed big data processing
- Real Time Analytics: Algorithms and Systems
- Event Processing across Edge and the Cloud for Internet of Things Applications
- Survey of Real-time Processing Technologies of IoT Data Streams
- Distributed Scheduling of Event Analytics across Edge and Cloud
- FOG-Engine: Towards Big Data Analytics in the Fog
- SpanEdge: Towards Unifying Stream Processing over Central and Near-the-Edge Data Centers
- A Storage Solution for Massive IoT Data Based on NoSQL
- Apache flink : Stream and batch processing in a single engine
- Internet of things patterns
- The Lambda and the Kappa
Cited by
- Towards Auction-Based Function Placement in Serverless Fog Platforms
- Using application knowledge to reduce cold starts in FaaS services
- tinyFaaS: A Lightweight FaaS Platform for Edge Environments
- From Zero to Fog: Efficient Engineering of Fog-Based IoT Applications
- MockFog 2.0: Automated Execution of Fog Application Experiments in the Cloud
- Goals and measures for analyzing power consumption data in manufacturing enterprises
- Towards AIOps in Edge Computing Environments
- Design and Evaluation of a New Machine Learning Framework for IoT and Embedded Devices
- Towards a Computing Platform for the LEO Edge
- From zero to fog: Efficient engineering of fog‐based Internet of Things applications
- On the Future of Cloud Engineering
- AuctionWhisk: Using an auction‐inspired approach for function placement in serverless fog platforms
- An Analysis of Computational Resources of Event-Driven Streaming Data Flow for Internet of Things: A Case Study
- IoT Serverless Computing at the Edge: A Systematic Mapping Review
- Towards grassroots peering at the edge
- Kubernetes distributions for the edge: serverless performance evaluation
- Fog Data Analytics for IoT Applications: Next Generation Process Model with State of the Art Technologies
- Streaming vs. Functions: A Cost Perspective on Cloud Event Processing
- Fusionize: Improving Serverless Application Performance through Feedback-Driven Function Fusion
- Machine learning for sports betting: should forecasting models be optimised for accuracy or calibration?
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