scholarly journals Support for Adaptive and Distributed Deployment of CEP Continuous Queries for the IoMT

2020 ◽  
Author(s):  
Fernando Benedito Veras Magalhães ◽  
Francisco José da Silva e Silva ◽  
Markus Endler
Keyword(s):  
Real World ◽  
Data Streams ◽  
High Volume ◽  
Complex Event Processing ◽  
Continuous Queries ◽  
Event Processing ◽  
The Real ◽  
Volume Velocity ◽  
Controlling Elements ◽  
Distributed Deployment

The current dissemination of IoT increases the deployment of stream processing solutions for monitoring and controlling elements of the real-world. One of those solutions is Complex Event Processing (CEP), and to handle the high volume, velocity and volatility of data streams from IoT sensors the CEP pipeline should be distributed, preferably having CEP operators both in the cloud/cluster and in edge devices. In this paper, we present a model for a distributed CEP platform and an implementation of this model called Global CEP Manager (GCM). GCM is a service of the ContextNet middleware that supports the deployment and dynamic rearrangement of CEP queries to CEP engines executing in the cloud and in M-Hubs, that are ContextNet’s mobile edge devices.

Complex Event Processing over LiveArchived Data Streams

2012 ◽  
Vol 35 (3) ◽  
pp. 540-554 ◽  
Author(s):  
Shang-Lian PENG ◽  
Zhan-Huai LI ◽  
Qun CHEN ◽  
Qiang LI
Keyword(s):  
Data Streams ◽  
Complex Event Processing ◽  
Event Processing

Complex Event Processing in Sensor-based Decision Support Systems

2010 ◽  
pp. 64-79 ◽  
Author(s):  
Jürgen Dunkel ◽  
Ralf Bruns ◽  
Oliver Pawlowski
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Process Model ◽  
Road Traffic ◽  
Support Systems ◽  
High Capacity ◽  
High Volume ◽  
Complex Event Processing ◽  
Event Processing ◽  
Reference Architecture

Sensor-based decision support systems have to cope with a high volume of continuously generated sensor events. Conventional software architectures do not explicitly target the efficient processing of continuous event streams. Due to the high volume of events and their complex dependencies it is not possible to have a fixed or predefined process flow on the business level. Recently, Complex Event Processing (CEP) has been proposed as a general process model for event streams. Though CEP provides mechanisms for computing high volume of events, it does not define any methodologies, models and reference architectures, which would establish EDA as a mature software architecture. In this chapter the authors present a reference architecture for sensor-based decision support systems, which enables the analysis and processing of complex event streams in real-time. The proposed architecture provides a conceptual basis for development of flexible software frameworks that can be adapted to meet various applications needs. The authors’ architectural approach is based on semantically rich event models providing the different stages of the decision process. They illustrate their approach in the domain of road traffic management for high-capacity road networks.

SU-GG-J-123: Making High Volume OBI CBCT Work in “the Real World”

2008 ◽  
Vol 35 (6Part7) ◽  
pp. 2707-2707
Author(s):  
Joerg Lehmann ◽  
J Stacy Glass ◽  
Stan Skubic ◽  
Dave Asche
Keyword(s):  
Real World ◽  
High Volume ◽  
The Real

scholarly journals Stream Processing Tools for Analyzing Objects in Motion Sending High-Volume Location Data

2021 ◽  
pp. 257-268
Author(s):  
Krzysztof Wecel ◽  
Marcin Szmydt ◽  
Milena Stróżyna
Keyword(s):  
Real World ◽  
Data Streams ◽  
Stream Processing ◽  
High Volume ◽  
Time Analysis ◽  
Processing Technologies ◽  
Location Data ◽  
Real Time Analysis ◽  
Velocity Magnitude ◽  
Selection Of

Recently we observe a significant increase in the amount of easily accessible data on transport and mobility. This data is mostly massive streams of high velocity, magnitude, and heterogeneity, which represent a flow of goods, shipments and the movements of fleet. It is therefore necessary to develop a scalable framework and apply tools capable of handling these streams. In the paper we propose an approach for the selection of software for stream processing solutions that may be used in the transportation domain. We provide an overview of potential stream processing technologies, followed by the method for choosing the selected software for real-time analysis of data streams coming from objects in motion. We have selected two solutions: Apache Spark Streaming and Apache Flink, and benchmarked them on a real-world task. We identified the caveats and challenges when it comes to implementation of the solution in practice.

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