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+file:: [Najdataei - Efficient Data Streaming Analytic Designs for Para.pdf](file://C:\Users\david\Zotero\/storage/THKTVIBT/Najdataei - Efficient Data Streaming Analytic Designs for Para.pdf)
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+- demand for timely analysis has resulted in a shift of data processing paradigms towards continuous, parallel, and multitier computing. 
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+- analysis speed, precision, costs, and deterministic execution
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+- efficient continuous processing of streams of data in a decentralized and timely manner.
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+- ontinuous machine learning/data mining types of problems, appearing commonly in IoT applications, and in particular continuous clustering and monitoring, for which we present novel algorithms
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+- DRIVEN
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+- STRATUM
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+- STRETCH
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+- Driven: a framework for efficient data retrieval and clustering in vehicular networks
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+- oday, we are in the midst of the fourth industrial revolution, a.k.a. Industry 4.0, that optimizes the computerization from recent decades with the use of Internet technologies to create smart production and smart services
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+- automated systems with integrated computational and physical capabilities
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+- high volume and high rate,
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+- cloud computing 
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+- dramatic escalation in data volumes and data rates pushes the limits of centralized data processing infrastructures
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+- for deriving timely intelligence 
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+- the amount of data generated by each device, as well as the need to immediately extract knowledgeable information from such data, make it **inefficient and impractical to send all the traffic to the cloud, doing the processing there, and then receive back the results**.
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+- the inefficiency of traditional data analytics, has begun a new wave of scientific revolution and led to innovative tools such as **continuous processing** that analyses flows of data on the fly.
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+- enable convenient and on-demand access to a shared pool of resources
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+- In IoT applications, the main motivation behind employing clouds, consisting of high-end servers, is to carry out heavy data analysis
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+- IoT devices are often equipped with reduced computational power, i.e. they are resource-constrained devices, and hence likely to be less efficient in performing heavy analysi
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+- loud computing 
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+- challenges
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+- when dealing with Big Data, it might be impossible to send all data to the cloud without exhausting the available communication bandwidth
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+- sending data to the cloud for analysis and getting back the results to make further decisions, could cause significant high latency which is not tolerable for certain applications requiring real-time processing.
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+- high latency causes a drastic effect on power and energy consumption and influences the reliability of the system
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+- degradation in the Quality-of-Service (QoS).
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+- fog computing and edge computing 
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+- procedures are being pushed down closer to where data originates.
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+- ntelligence and data processing is offloaded onto the edge devices (e.g. routers, switches, sensors and actuators.) without being sent to the cloud
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+- Edge computing is especially beneficial for applications which require ultra-low latency and real-time analysis
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+- [:span]
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+- By bringing the intelligence away from the cloud, each fog node performs data processing closer to the IoT devices and consequently reduces the amount of data transported. A fog node also can be seen as a mini-cloud, which is located near the edge layer and the IoT devices connected to
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+- the edge/fog/cloud architecture brings several benefits as it allows applications to distribute the intelligence and take advantage of a diverse range of computing resources and storing assets
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+- to design efficient algorithms and frameworks to process data in such 3-tier architecture we need to know how and where to process the data.
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+- earn from data, provide data driven insights, and make decisions
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+- adapt the traditional ML algorithms to continuously process the flow of data
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+- Continuous processing allows improvements in memory access patterns as well as enabling real-time monitoring which is a requirement for many application
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+- continuous processing and querying the constant stream of data received from a temperature sensor, it is possible to raise an alarm once the temperature reaches a certain threshold
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+- As data processing requirements grow, IoT applications demand utilization of the whole spectrum of devices in the computing continuum, from cloud data centers to edge systems and endpoint devices.
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+- ardware-aware processing approache
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+- performance analysis
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+- decision process of where to compute applications
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+- neural network consisting of multiple layers and large data sets is a better option for cloud computing while a collaborative data filtering can be run on fog nodes.
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+- dge computing is recommended for applications with soft execution time constraints to reduce the energy costs and carbon footprints 
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+- Figure 2 illustrates an example of smart transportation system that utilizes edge, fog, and cloud computing. As shown in the figure, the data regarding street conditions and unexpected events such as accidents are generated by many sensors deployed in the cars. This data, then, can be processed locally by resource constrained devices next to the sensors, to be used by the cars to adjust the velocity and avoid obstacles (edge computing). Cars can also send such data to the nearby traffic lights to be processed by the small embedded servers and extract information in order to adjust the lights, thus, relieve potential traffic congestion around the reported location (fog computing). Moreover, the city services can receive data from cars as well as the transportation infrastructures including traffic lights, to process the aggregated data for making further decisions (cloud computing
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+- [:span]
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+- owards a computing continuum: Enabling edge-to-cloud integration for data-driven workflow
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+- 22] D. Balouek-Thomert, E. G. Renart, A. R. Zamani et al., ‘Towards a computing continuum: Enabling edge-to-cloud integration for data-driven workflows,’ The International Journal of High Performance Computing Applications, vol. 33, no. 6, pp. 1159–1174, 2019 (cit. on p. 7).
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