ﻻ يوجد ملخص باللغة العربية
This paper introduces H-STREAM, a big stream/data processing pipelines evaluation engine that proposes stream processing operators as micro-services to support the analysis and visualisation of Big Data streams stemming from IoT (Internet of Things) environments. H-STREAM micro-services combine stream processing and data storage techniques tuned depending on the number of things producing streams, the pace at which they produce them, and the physical computing resources available for processing them online and delivering them to consumers. H-STREAM delivers stream processing and visualisation micro-services installed in a cloud environment. Micro-services can be composed for implementing specific stream aggregation analysis pipelines as queries. The paper presents an experimental validation using Microsoft Azure as a deployment environment for testing the capacity of H-STREAM for dealing with velocity and volume challenges in an (i) a neuroscience experiment and (in) a social connectivity analysis scenario running on IoT farms.
Operating a distributed data stream processing workload efficiently at scale is hard. The operator of the workload must parallelize and lay out tasks of the workload with resources that match the requirement of target data rate. The challenge is that
Big data is gaining overwhelming attention since the last decade. Almost all the fields of science and technology have experienced a considerable impact from it. The cloud computing paradigm has been targeted for big data processing and mining in a m
High-Performance Big Data Analytics (HPDA) applications are characterized by huge volumes of distributed and heterogeneous data that require efficient computation for knowledge extraction and decision making. Designers are moving towards a tight inte
The proliferation of camera-enabled devices and large video repositories has led to a diverse set of video analytics applications. These applications rely on video pipelines, represented as DAGs of operations, to transform videos, process extracted m
The smart health paradigms employ Internet-connected wearables for telemonitoring, diagnosis for providing inexpensive healthcare solutions. Fog computing reduces latency and increases throughput by processing data near the body sensor network. In th