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This thesis proposes a data driven priority scheduling technique (DDPS) for stream processing platforms. DDPS assigns priorities to input data streams based on user-defined mappings between data attributes and priority levels. The DDPS algorithm then processes higher priority data first to improve their latency even during periods of high load. The thesis implements DDPS on Apache Spark Streaming and evaluates its performance using synthetic workloads and a live Twitter sentiment analysis scenario. Experime

Author

fmaachadoo

Language

EN