Clustering Data Streams: A Complex Network Approach

Porto, Sandy M.; Quiles, Marcos G.

doi:10.1007/978-3-030-24289-3_5

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…That is, through the analysis of the temporal evolution of such streams and how it has an impact on the very network structure and performance. As a consequence, future research will be about statistics and inference in dynamic graphs [240]. The challenge may be extended further to the more general framework of machine learning in networks.…”

Section: Complex Networkmentioning

confidence: 99%

A Review on Control and Optimisation of Multi-Agent Systems and Complex Networks for Systems Engineering

Herrera¹,

Hernández²,

Parlikad³

et al. 2020

Preprint

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Systems Engineering is an ubiquitous discipline of Engineering overlapping industrial, chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It provides tools for dealing with the complexity and dynamics related to the optimisation of physical, natural, and virtual systems management. This paper presents a review of how multi-agent systems and complex networks theory are brought together to address Systems Engineering and management problems. The review also encompasses current and future research directions both for theoretical fundamentals and applications in Industry. This is made by considering trends such as mesoscale, multiscale, and multilayer networks; along with the state-of-art analysis on network dynamics and intelligent networks. Critical and smart infrastructure, manufacturing processes, and supply chain networks are instances of research topics for which this literature review is highly relevant.

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Section: Complex Networkmentioning

confidence: 99%

A Review on Control and Optimisation of Multi-Agent Systems and Complex Networks for Systems Engineering

Herrera¹,

Hernández²,

Parlikad³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…This research framework being relatively new, it should be developed further by conducting proper analysis on the streams of time series data associated to complex networks, that is, through the analysis of the temporal evolution of such streams and how it has an impact on the very network structure and performance. As a consequence, future research will be about statistics and inference in dynamic graphs [252]. The challenge may be extended further to the more general framework of machine learning in networks.…”

mentioning

confidence: 99%

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

et al. 2020

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Systems engineering is an ubiquitous discipline of Engineering overlapping industrial, chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It provides tools for dealing with the complexity and dynamics related to the optimisation of physical, natural, and virtual systems management. This paper presents a review of how multi-agent systems and complex networks theory are brought together to address systems engineering and management problems. The review also encompasses current and future research directions both for theoretical fundamentals and applications in the industry. This is made by considering trends such as mesoscale, multiscale, and multilayer networks along with the state-of-art analysis on network dynamics and intelligent networks. Critical and smart infrastructure, manufacturing processes, and supply chain networks are instances of research topics for which this literature review is highly relevant.Processes 2020, 8, 312 2 of 29 in a telecommunication system network, nodes (e.g., routers and switches) may suffer over time a degradation in their properties that may lead them to diminish their performance. However, just the daily variation on traffic demand also has an impact on such nodes performance. Unexpected, external intervention such as cyber-attacks or extreme weather conditions may affect the properties, even the topology of a telecommunication system.Intelligent distributed systems are capable of modelling how different parts of the network might work individually and collectively [10]. Ultimately, this is used as support for any decision-making system aiming to achieve better overall system functioning and its consequent performance. Intelligent distributed systems encompass autonomous learning units that can be associated with the nodes of a complex network. These intelligent network nodes are able to act independently and also interact with other nodes to pursue both individual (local) and general system-level targets. The necessary communication between individuals can be represented by links of the complex network connecting their nodes. It is natural to understand such nodes as intelligent agents within a MAS framework. Each agent separately obeying simple rules but working together with other agents makes it possible to approach complex engineering challenges. Intelligent distributed systems have been proposed in a broad range of engineered complex systems [11,12]. Precisely, the review proposed herein focuses only on engineered systems and manufacturing processes, given the wide spectrum of the approached topic. Out of this review fall, then, topics related to social systems analysis such as migration models [13,14] or online social media [15,16]. Similarly, natural systems are out of this review scope. This is the case of computational chemistry [17,18], biology [19,20], and metabolic networks [21,22].Automatic optimisation and control in an engineering system is associated with a near real-time data acquisition and an optimal decision-making. Th...

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“…Considering network dynamics within the formal framework of multilayer networks is, therefore, an open research avenue for researchers and practitioners in smart water grids, where complex network analysis already plays a pivotal role. The challenge of time series processes in networks can be extended to other statistical and machine learning methods approached on networks [122]. • Geometric deep learning: It is a new emergent topic in which convolutional neural networks (CNN), mainly focused so far on image analysis, are used for manifolds and graph-structured databases [123]; having inherited the name for the latest of graph-CNN or directly graph neural networks (GNN).…”

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confidence: 99%

Overview of Energy Management and Leakage Control Systems for Smart Water Grids and Digital Water

et al. 2020

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Current and future smart cities are moving towards the zero-net energy use concept. To this end, the built environment should also be designed for efficient energy use and play a significant role in the production of such energy. At present, this is achieved by focusing on energy demand in buildings and to the renewable trade-off related to smart power grids. However, urban water distribution systems constantly carry an excess of hydraulic energy that can potentially be recovered to produce electricity. This paper presents a comprehensive review of current strategies for energy production by reviewing the state-of-the-art of smart water systems. New technologies (such as cyber-physical systems, digital twins, blockchain) and new methodologies (network dynamics, geometric deep learning) associated with digital water are also discussed. The paper then focuses on modelling the installation of both micro-turbines and pumps as turbines, instead of/together with pressure reduction valves, to further demonstrate the energy-recovery methods which will enable water network partitioning into district metered areas. The associated benefits on leakage control, as a source of energy, and for contributing to overall network resilience are also highlighted. The paper concludes by presenting future research directions. Notably, digital water is proposed as the main research and operational direction for current and future Water Distribution Systems (WDS) and as a holistic, data-centred framework for the operation and management of water networks.

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Clustering Data Streams: A Complex Network Approach

Cited by 3 publications

References 24 publications

A Review on Control and Optimisation of Multi-Agent Systems and Complex Networks for Systems Engineering

A Review on Control and Optimisation of Multi-Agent Systems and Complex Networks for Systems Engineering

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

Overview of Energy Management and Leakage Control Systems for Smart Water Grids and Digital Water

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