ﻻ يوجد ملخص باللغة العربية
The Internet of Things describes a network of physical devices interacting and producing vast streams of sensor data. At present there are a number of general challenges which exist while developing solutions for use cases involving the monitoring and control of urban infrastructures. These include the need for a dependable method for extracting value from these high volume streams of time sensitive data which is adaptive to changing workloads. Low-latency access to the current state for live monitoring is a necessity as well as the ability to perform queries on historical data. At the same time, many design choices need to be made and the number of possible technology options available further adds to the complexity. In this paper we present a dependable IoT data processing platform for the monitoring and control of urban infrastructures. We define requirements in terms of dependability and then select a number of mature open-source technologies to match these requirements. We examine the disparate parts necessary for delivering a holistic overall architecture and describe the dataflows between each of these components. We likewise present generalizable methods for the enrichment and analysis of sensor data applicable across various application areas. We demonstrate the usefulness of this approach by providing an exemplary prototype platform executing on top of Kubernetes and evaluate the effectiveness of jobs processing sensor data in this environment.
Distributed Stream Processing systems are becoming an increasingly essential part of Big Data processing platforms as users grow ever more reliant on their ability to provide fast access to new results. As such, making timely decisions based on these
High Energy Physics (HEP) distributed computing infrastructures require automatic tools to monitor, analyze and react to potential security incidents. These tools should collect and inspect data such as resource consumption, logs and sequence of syst
Distributed Stream Processing (DSP) systems enable processing large streams of continuous data to produce results in near to real time. They are an essential part of many data-intensive applications and analytics platforms. The rate at which events a
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
With the rapid development of wireless sensor networks, smart devices, and traditional information and communication technologies, there is tremendous growth in the use of Internet of Things (IoT) applications and services in our everyday life. IoT s